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        <title>INSEAD Knowledge</title>
        <link>https://knowledge.insead.edu</link>
        <description>The business school for the world</description>
        <lastBuildDate>Wed, 13 May 2026 10:21:56 +0800</lastBuildDate>
        <language>en</language>
        <item><title>The Perils of Fighting Local Gods</title>
                  <link>https://knowledge.insead.edu/leadership-organisations/perils-fighting-local-gods</link>
                  <description> <![CDATA[Hadrian, the Roman emperor, journeyed to Athens in spring of AD 125 for the Great Dionysia, one of the most important festivals in the Hellenic world honouring Dionysus, the patron deity of theatre and wine. As ruler of the Roman empire, Hadrian could attend in imperial regalia, wearing a purple toga embroidered with gold threads, or take part as a local patron, adopting the customs and symbols of the city. He chose the latter, showing up as one of the officials in charge, presiding over the celebrations while dressed in a simple white cloth worn by Athenian citizens. In doing so, he didn’t erase differences between Roman and Greek culture – he created a bridge between them.Modern mergers and acquisitions present a similar challenge. Leaders often approach cultural integration as a problem to be solved through structural alignment and standardisation. Yet decades of research on post-merger integration shows that employees experience mergers through the lens of values, identity and purpose. This is why cultural integration so often becomes the decisive factor in whether a merger deal succeeds.When integration fails, it’s rarely because leaders ignore culture altogether. More often, it’s because they focus on the wrong aspects of it. We outline three common mistakes and our recommendations for avoiding them.Mistake 1: Confusing equal power with fairnessOne of the most common concerns in M&A is that the acquiring company will dominate the acquired company’s culture, leading to conflict. To avoid such “winner vs. loser” dynamics, many mergers are framed as “mergers of equals”. The creation of Citigroup in 1998 through the merger of Citicorp and Travelers Group illustrates both the intent and limits of this approach. Citigroup adopted a highly symmetrical structure, with two co-CEOs, equal board representation and shared decision-making authority. The CEOs shared veto power over strategic decisions, meaning that all significant decisions had to be made jointly. Although this arrangement divided power equally between executives, it didn’t address the need to create a workable governance structure and fair corporate environment. In practice, the structure diffused authority, while unclear decision rights made coordination more difficult and amplified strategic tensions. A decade later, the merger was regarded by some as one of the worst of all time. In contrast, when Indosat Ooredoo and Hutchison Tri Indonesia merged in 2022, leadership placed strong emphasis on ensuring that decisions were transparent, consistent and grounded in clear criteria. This was particularly important in the restructuring process. Rather than relying on informal judgments, the organisation adopted data-driven assessments and involved independent third parties to evaluate employees. Even difficult decisions such as redundancies were broadly accepted by employees, thanks to the clarity and consistency of the process.Our recommendation: Be fair and transparent, because this is what employees expect. Research suggests that clear decision rights, objective evaluation processes and perceived fairness are key to building trust and fostering employee engagement post-M&A.Mistake 2: Confusing espoused values with lived onesCultural integration often focuses on aligning values. Leaders invest significant effort in defining shared principles, integrating them into communications and codifying them in formal documents. Yet alignment on paper doesn’t guarantee alignment in practice.Take the 1999 merger of MindSpring and EarthLink, both internet service providers (ISPs) in the United States. MindSpring was known for a strong values-driven culture. The company’s "Core Values and Beliefs" such as respect, integrity and customer focus were distributed to employees and guided everyday decisions. Executives from EarthLink were sufficiently impressed that they incorporated these into the combined corporate handbook. However, while the language of customer focus and respect remained, operational choices at the merged company emphasised efficiency. The issue wasn’t the absence of values but the inconsistency in how they were enacted, a gap that research shows could be read by employees as an integrity problem and erode their trust and commitment.  When integration fails, it’s rarely because leaders ignore culture altogether. More often, it’s because they focus on the wrong aspects of it.  Initially, the combined company became the second-largest ISP in the US, with nearly 3 million subscribers and peaking at 5 million in 2004. But operational friction eventually caused it to miss the transition to broadband. By 2007, it was forced to cut 900 jobs, and by mid-2009, its subscriber base had halved.Our recommendation: Align behaviour, not just values. Leaders must ensure that behaviours are grounded in the principles they champion. Values only shape culture when they are reflected in everyday decisions.Mistake 3: Confusing cultural differences with strategic incompatibilityTwo organisational cultures are rarely fully aligned and friction is inevitable. The challenge for leaders is not to eliminate these differences, but to determine which ones matter.Consider an anonymised example of two software companies that merged. One was known for agility and rapid development cycles; the other emphasised structure and thoroughness. Rather than identifying how to integrate product development in a way that leveraged both speed and reliability, leaders became drawn into debates about processes, reporting structures and norms. Eventually, coordination broke down, and the integration ultimately failed. This pattern is surprisingly common. Organisations tend to choose one set of practices over another, often reflecting the preferences of the acquiring firm but neglecting valuable capabilities.Research on intergroup conflict and post-merger identification suggests a different approach: Groups can orient towards a shared objective without feeling that their distinctive strengths must be eliminated. In this context, a clearly defined purpose plays a critical role.Purpose doesn’t remove differences, but it helps organisations distinguish between what’s consequential and what’s not. It provides a basis for deciding where alignment is essential and where practices can diverge. Because this question was never resolved by the two software companies, attention drifted towards more visible but lower-stakes issues while the deeper integration challenge remained unaddressed.Our recommendation: Be clear on purpose but flexible on execution. Define a small set of non-negotiable principles or objectives and be flexible about how to achieve them. This preserves valuable capabilities while maintaining strategic coherence.What effective cultural integration looks like in practiceWhen Microsoft acquired code-sharing platform GitHub in 2018, the two companies brought very different cultures to the table. Microsoft emphasised enterprise scale and operational efficiency, while GitHub was known for its developer-centric and informal culture.At the same time, both organisations were aligned around a shared principle of empowerment. GitHub’s mission to help developers was fully aligned with Microsoft’s mission “to empower every person and every organisation on the planet to achieve more”.Rather than forcing convergence, Microsoft preserved GitHub’s developer-first ethos and gave the company a significant degree of operational autonomy. For example, GitHub was allowed to release new tools that enabled developers to deploy code directly to Amazon Web Services, Microsoft’s biggest rival in the cloud market. By preserving GitHub’s unique way of working while aligning on a shared purpose, Microsoft was able to maintain what made GitHub distinctive without sacrificing strategic coherence.The strategy paid off. In 2018, before the merger, GitHub had 28 million active users and annual recurring revenue (ARR) of some US$250 million. By 2022, the company had tripled its user base and crossed US$1 billion in ARR.From ancient history to modern M&A, the challenges of cultural integration remain consistent. Successful cultural integration of two entities doesn’t mean eradicating differences. It’s about clarity of purpose, as well as making disciplined choices about what must be aligned, what can remain local and how those choices will be understood by employees. Leaders who approach integration in this way don’t topple “local gods” – they channel them in service of a shared vision.]]></description>
                  <pubDate>Tue, 12 May 2026 04:30:50 +0000</pubDate>
                  <guid isPermaLink="false"> 48606 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/leadership-organisations/perils-fighting-local-gods#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-05/shutterstock_2365443309_1.jpg?itok=tNuceVUb" type="image/jpeg" length="113498" /><dc:creator>Piotr Furmanski</dc:creator><dc:creator>Andy J. Yap</dc:creator></item><item><title>AI Is Supercharging Open Innovation</title>
                  <link>https://knowledge.insead.edu/strategy/ai-supercharging-open-innovation</link>
                  <description> <![CDATA[Twenty years after the term “open innovation” was coined by American professor Henry Chesbrough, collaborating with other firms to share ideas and innovation has become essential to corporate success. Critical, in fact, against the backdrop of proliferating artificial intelligence, as our latest Open Innovation Report (OIR25) shows. The survey of more than 1,000 innovation and strategy leaders at private and public organisations in Europe shows that the strategic importance of open innovation has dramatically increased since our last report in 2023, particularly when it comes to AI.Specifically, OIR25 – which focuses on corporate-startup collaboration – indicates that 80% of corporates now deem startup collaboration as important or mission-critical to their business strategy, up from 67% in 2023. It’s evident that, to accelerate their AI strategies, organisations are seeking to bypass bottlenecks including the lack of talent by partnering agile, tech-driven startups, which also often brings fresh thinking and specialised expertise. Variations across industriesIndeed, corporate leaders we interviewed cite the importance of turbocharging AI execution and unlocking new value as the primary driver for collaborating with startups. Organisations in aerospace were the most enthusiastic: 92% of the industry players in our study have collaborated with startups. Within the sector, Airbus is seen as the open innovation leader, collaborating with startups in areas such as sustainable aviation fuels, digital flight operations and autonomous systems.Generative AI, not surprisingly, is the focus of corporate attention: Six in 10 organisations said AI integration is highly important to their business, and 72% of corporates with over 5,000 employees have partnered with startups on AI projects.However, not everyone is sold on open innovation. Organisations in the “defence” and “homeland security” sectors in our survey showed the lowest enthusiasm, with only 42% having collaborated with startups. Culture and competition probably underlie the divergence: The more competitive an industry is, the more its players tend to value external expertise to reduce time to market. One would expect the “defence” and “public sector & government” sectors to occupy spaces in the opposite end of the spectrum, where lower risk and stability are valued more than innovation. The need for open innovation departmentsInterestingly, our survey shows that organisations with extensive experience of collaboration with startups don’t find it easier to do so than those with less experience. Counterintuitive, yes, but not illogical. When organisations engage in more open innovation, they tend to experience real-world hurdles as they move from experimentation towards strategic integration. Our research shows that legal and regulatory constraints in areas such as procurement becomes serious barriers when existing corporate structures and processes are not tailored for agile startups.Organisations that have been successful in open innovation tend to understand the need to dedicate resources to working with startups. Those with dedicated open innovation departments reported a 73% success rate – defined in our survey as “reaching set objectives” such as exploring innovative technology – in their projects, compared to just 51% for those without.Prominent examples of open innovation departments include BMW’s Startup Garage, staffed by 25-30 employees, and Transport for London’s team of 20 employees, who work with external partners to co-develop mobility innovations.Open innovation departments are crucial for aligning strategic priorities, engaging the right business functions and industrialising the collaboration process. They also serve as internal ambassadors for external expertise. Their main mission is to enable quick onboarding of new startups and experimentation with cutting-edge technologies, ensuring that open innovation is not just a side experiment but a core strategic capability.Future trends The OIR25 signals a major shift in how large firms leverage open innovation. Traditionally, these firms collaborated with startups to fill expertise gaps. Now, they are turning to AI startups to adopt new solutions and services faster even though they already have AI capabilities. This tells us that collaboration is not just about keeping pace with technological change – it's about staying competitive and resilient in a landscape where speed and innovation are critical.]]></description>
                  <pubDate>Wed, 13 May 2026 03:53:21 +0000</pubDate>
                  <guid isPermaLink="false"> 48596 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/strategy/ai-supercharging-open-innovation#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-05/shutterstock_2502253945_1.jpg?itok=N6kbyogY" type="image/jpeg" length="89082" /><dc:creator>Tobias Studer Andersson</dc:creator><dc:creator>Andrew Shipilov</dc:creator><dc:creator>Nathan Furr</dc:creator></item><item><title>What High Oil Prices Mean for the Energy Transition</title>
                  <link>https://knowledge.insead.edu/economics-finance/what-high-oil-prices-mean-energy-transition</link>
                  <description> <![CDATA[The Iran war has sent oil prices and inflation soaring. It has also produced a less visible but no less damaging consequence: The energy transition is sliding further down the agenda of oil and gas companies.My research, published in Strategic Management Journal, offers hints as to how this could play out. I studied how oil and gas firms behaved during a previous major disruption to their industry – the 2014 oil price crash, which forced them to reckon with excess capacity. Extrapolating from those findings, today’s environment makes the transition to renewables increasingly unappealing for oil and gas firms. Even when firms have strong incentives to reposition, market forces alone are not enough, let alone when companies can instead capture war-generated windfall profits.What happened a decade agoAfter the 2014 price collapse, a small number of oil and gas companies that had already diversified into wind power cut offshore oil and gas spending and redirected it towards wind projects. These firms represented only about 2.5% of the oil and gas companies I studied. But when this minority chose to invest, the impact on offshore wind technology was significant.Where a wind farm sat within close proximity of established oil and gas assets, post-shock investment rose by as much as US$23 million per project, compared with a pre-shock average of US$6 million. These firms also deployed larger, more powerful and more technologically advanced turbines than their pure-play wind rivals. Several projects demonstrated that oil and gas companies possess transversal capabilities that can push the technological frontier in offshore wind.Equinor’s Hywind project, the world’s first commercial floating offshore wind farm, illustrates the point. Developed by a firm rooted in Norwegian oil and gas and commissioned near existing North Sea fields, it drew on engineering competencies that a pure-play renewables developer would have struggled to assemble from scratch.Policy... must create conditions in which the energy transition becomes a strategically attractive use of existing industrial capabilities – not merely a box-ticking obligation or a reputational hedge.  Post-crash conditions converged to pull energy firms towards wind in that period. The industry had idle capacity, lower returns on incremental oil and gas projects, and a pressing need to find productive uses for expensive resources – vessels, engineering and project management expertise, and an entire supply chain that would otherwise have sat idle. Combined with clear government policy favouring renewables, wind power began to look attractive by comparison – at least to some companies.What’s happening nowToday’s conditions are the opposite. In the wake of the Iran war, the incentive to redeploy resources towards renewables has collapsed. BP has announced a reorganisation of its business units that clearly reverses its push into renewable energy. TotalEnergies is swapping offshore wind projects on the United States East Coast for oil and gas projects in Texas. Left to itself, the market is plainly insufficient to drive the scale of transition required by climate targets, particularly as policy and decision-makers put climate policies themselves on the backburner.What the industry can do that others cannotThis matters because the capabilities oil and gas firms possess are not easily replicated elsewhere. The next generation of offshore wind projects – floating foundations, production sites far from shore, cables running across seabeds – would benefit enormously from the industrial project management expertise that sits inside the major oil companies and their supply chains.My research makes one conclusion difficult to avoid: The firms best equipped to accelerate the energy transition are largely not doing so, and today’s price environment gives them little reason to. Generic calls to abandon oil and gas are not only impractical but wasteful, because this industry possesses many of the elements needed to technologically advance and commercially realise renewable projects at scale.What policymakers need to doPolicy needs to do more than price carbon. It must create conditions in which the energy transition becomes a strategically attractive use of existing industrial capabilities – not merely a box-ticking obligation or a reputational hedge. That could mean targeted incentives for wind and other renewables, such as geothermal, that leverage existing oil and gas infrastructure, or long-term contracts that de-risk the investment case for firms considering redeployment. Recent moves by the European Union to shift individual behaviour are commendable, but policy needs to be far more targeted towards industrial players if it is to drive change at scale.The oil and gas industry doesn’t need to remain the villain of the energy transition story. In many respects, it is best placed to accelerate the next chapter. At current oil prices, however, this is unlikely. The gap between potential and action is what policy must target.]]></description>
                  <pubDate>Thu, 07 May 2026 01:00:48 +0000</pubDate>
                  <guid isPermaLink="false"> 48591 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/economics-finance/what-high-oil-prices-mean-energy-transition#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-05/shutterstock_1149456404_1.jpg?itok=M2VBbbcb" type="image/jpeg" length="148271" /><dc:creator>Aldona Kapačinskaitė</dc:creator></item><item><title>The New Blueprint for Competing in a Fractured World</title>
                  <link>https://knowledge.insead.edu/leadership-organisations/new-blueprint-competing-fractured-world</link>
                  <description> <![CDATA[The international order is fracturing. War in the Middle East, the prolonged conflict in Ukraine and seismic shifts in American trade and foreign policy have upended decades of assumed stability. Tariffs, export controls, sanctions and critical chokepoints – from maritime routes to semiconductor supply chains – are exposing just how fragile business networks have become.As we wrote in MIT Sloan Management Review, multinationals need to redesign traditional strategies – exit, relocate or reorganise – in this new world order. Firms that remain globally connected yet decentralised will be better able to compete in a geopolitically fractured world.Why traditional responses look different nowResearch into successive waves of globalisation and de-globalisation since the early 20th century reveals that the strategic options historically available to multinationals – exit, relocate, or reorganise – have not disappeared. But they are manifesting in different ways.Exit: A costly and often inadvisable pathWhen political risk rises sharply, the instinct is often to run. Yet exit is often painful and rarely clean. The departure of oil majors BP, Shell and Equinor from Russia following the invasion of Ukraine shows firms face steep financial write-downs, contractual disputes, legal entanglements and lasting reputational damage. Host governments can wield regulatory and coercive power to make departure far more expensive than entry ever was.Firms that will compete effectively are those that redesign their portfolios, supply chains, data architecture and governance structures for the world that is taking shape, not the one that existed. What’s more, a clean exit can permanently forfeit the firm’s access to markets that may remain strategically significant for decades. A wiser response might be to maintain a calibrated minimal presence – a legal entity and basic operational footprint sufficient to preserve relationships, regulatory standing and market intelligence without deepening financial exposure. Nissan and Volkswagen have pursued exactly this in China, pulling back R&D investment and slowing expansion without fully withdrawing, preserving the option to re-engage if conditions improve.Reorganise: The return of the poly-nationalGeopolitical pressure is calling into question the dominant model of multinational organisation, one built around centralised strategic direction, globally optimised supply chains and the primacy of commercial logic over political considerations. That model prized efficiency. What the current era demands is resilience.Many multinationals are restructuring around poly-national architectures: networks of semi-autonomous units with strong in-country leadership, regional supply chains and deep ties to local stakeholders. This represents a partial return to the multi-domestic model of the pre-globalisation era – though now driven not by the absence of global integration but by the deliberate unwinding of excessive dependence on it.HSBC's 2024 restructuring is instructive. The bank split its global operations into Eastern and Western markets and separated their governance accordingly, producing a firm that remains globally coordinated yet politically adaptable. Nestlé has pursued a comparable path, distributing strategic authority across regional hubs and embedding operations deeply within local economic and regulatory systems to absorb political shocks without systemic damage to the broader enterprise.Local anchoring can also take the form of localised ownership. McDonald’s ceded significant ownership in China to domestic partners; Hindustan Unilever and Heineken listed local subsidiaries on national stock exchanges. In the most extreme cases, as TikTok's fraught negotiations in the United States demonstrated, restructuring ownership entirely may be the only route to continued operation in a hostile regulatory environment.Beyond structure, multinationals are investing in geopolitical capabilities as a distinct corporate function. BlackRock, Allianz and Siemens have each developed proprietary tools to monitor political risk continuously and anticipate supply chain disruption. The most ambitious firms have moved further still, into corporate diplomacy, treating geopolitics not as a constraint to be absorbed but as an arena for proactive engagement. Last year, Apple simultaneously lobbied Washington against tariffs, reassured Chinese officials of its commitment to the market and cultivated ties with Indian authorities. Microsoft, meanwhile, made five significant pledges to European digital stability, expanding data centre operations across 16 countries in the region and committing to defend its legal right to operate on the continent.Relocate: Compliance over optimisationFor decades, regulatory convergence and proliferating free trade agreements allowed multinationals to let cost efficiency dictate location decisions. That calculus is now broken. Regulatory fragmentation and the return of trade barriers are forcing firms to prioritise compliance and risk mitigation over pure cost advantage.Post-Brexit Europe made this plain, compelling multinationals headquartered in London to relocate subsidiaries and restructure reporting lines to preserve European market access. The response to US–China tensions has taken a different form: reshoring, nearshoring and friend-shoring – moving production back home or to allied or neutral nations to reduce exposure to adversarial environments. Apple is shifting the bulk of iPhone production to India; Samsung manufactures most of its Galaxy smartphones in Vietnam; Intel has established a hub in Malaysia, exploiting the country's neutrality and semiconductor expertise. Each decision trades some cost efficiency for reduced dependence on a single geopolitical bloc, while positioning the firm within emerging corridors of middle-power trade.Designing for adaptabilityThe current landscape reflects a fundamental rupture within globalisation itself. Nations are weaponising the very networks they cannot fully dismantle. They are deploying techno-nationalism, sanctions, data sovereignty rules and industrial policy as instruments of competition, even as they remain deeply interdependent.Firms that will compete effectively are those that redesign their portfolios, supply chains, data architecture and governance structures for the world that is taking shape, not the one that existed. Resilience now belongs to those capable of rapid yet considered adaptation to a geopolitical order that shows no sign of stabilising.This article is adapted from “What Global Turmoil Means for Company Structure” published in MIT Sloan Management Review.]]></description>
                  <pubDate>Tue, 05 May 2026 01:09:21 +0000</pubDate>
                  <guid isPermaLink="false"> 48581 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/leadership-organisations/new-blueprint-competing-fractured-world#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-05/shutterstock_2414267267_1.jpg?itok=D1Y3BrAr" type="image/jpeg" length="707555" /><dc:creator>Caterina Moschieri</dc:creator><dc:creator>Davide Ravasi</dc:creator><dc:creator>Quy Huy</dc:creator></item><item><title>AI &amp; Jobs: What Workers Can Do to Protect Themselves</title>
                  <link>https://knowledge.insead.edu/career/ai-jobs-what-workers-can-do-protect-themselves</link>
                  <description> <![CDATA[The disruption unfolding across today's labour market is unlike anything that came before. Where past waves of automation swept through factory floors and manual work, AI is hitting white-collar jobs, especially entry-level ones. In the first quarter of 2026, tech companies laid off more than 78,000 workers, with 48% attributed to AI automation. Even Jerome Powell, the United States Federal Reserve chair, has warned that AI could “absolutely have implications for job creation”.How can we gird ourselves for AI’s impact? In our “AI & Jobs” series, we ask INSEAD professors to analyse the situation from the perspectives of individuals – the focus of this article – as well as organisations and policymakers. The consensus: AI is redesigning and restructuring jobs far more than it’s making them obsolete. Whether and how individuals (especially entry-level and junior workers) exploit AI while honing their own skills will decide the security of their future. Meta-skills will be a game changer Phanish Puranam, The Roland Berger Chaired Professor of Strategy and Organisation Design The jobs most vulnerable to AI displacements in the next few years will be those that are low-level, don’t require collaboration (e.g. modular work) or in-person presence (e.g. purely knowledge work), and are done the same way across companies. This isn’t necessarily because algorithms will become effective substitutes for all tasks in all such roles – the evidence suggests they aren’t (yet) – but because organisations are no longer hiring as they expect AI to eventually catch up. That said, completely new tasks and roles have been created since the advent of generative AI. I categorise them into four types: AI operations, AI compliance, jobs related to human-AI interaction (e.g. prompt librarian, AI personality coaches), and perhaps the biggest group is simply AI-augmented versions of old roles in software, medical services and other sectors where demand is elastic.Although we can’t forecast what skills will be in demand in the future, I think “meta-skills”, which allow humans to acquire new skills quickly, will matter more than specific skills. Meta-skills are, as I explain in a separate article, unlike domain knowledge or technical expertise. Meta-skills such as analogical reasoning, metacognitive regulation, higher-order thinking and social coordination don't directly produce output. Instead, they accelerate learning, enable knowledge transfer across contexts and help people adapt when tasks evolve.In new work with Alessandro Sforza and Matteo Devigili, I’m trying to pin down the signature of meta-skills by studying “super-jumpers” – individuals who make big leaps in the skills they seem to acquire when transitioning to new jobs. The danger of relying heavily on AI tools is that our meta-skills could atrophy. This means we might be more efficient in the short term but increasingly fragile and commoditised over time.AI is rewriting the rules — output is abundant, judgment and credibility are scarceSo Yeon Chun, Associate Professor of Technology and Operations ManagementAI is often discussed in terms of jobs lost or created, but this framing misses a more fundamental shift. AI is altering how work is structured, how value is defined and how opportunity is distributed. In short, AI is rewriting the rules of work.Instead of replacing jobs in their entirety, AI is increasingly transforming tasks within jobs. Rather than focusing on jobs gained or lost, it is more useful to look at how tasks are redistributed between humans and machines. Even without large-scale unemployment, AI may lead to a more invisible form of disruption in terms of responsibilities, scope and career progression.This shift changes the nature of value. When high-quality output becomes easy to generate, value moves away from production and towards judgment — the ability to interpret, evaluate and make decisions. To stay relevant, humans must become skilled at guiding AI systems, assessing their outputs, and applying context and causal reasoning. Judgment becomes critical not only for improving one’s own work, but for evaluating the contributions of others. As AI makes it easier to produce polished output at scale, distinguishing truly valuable work becomes more difficult. Thus, those who are better at making their work – valuable or not – visible may be better positioned to capture opportunity.  To stay relevant, humans must become skilled at guiding AI systems, assessing their outputs, and applying context and causal reasoning. This reflects a broader dynamic I highlight in my research: When people have less time and trust to judge an ever-increasing amount of output, visibility increasingly decides whose work is recognised. When judgment is lacking, appearing busy or visible can replace actually doing valuable work, and credibility becomes rare.In a world overflowing with output, the real advantage lies in exercising judgment and building credibility, both in the work we produce and in how it is evaluated. If you can’t beat them, use themWinnie Jiang, Assistant Professor of Organisational BehaviourIn an ongoing study of professional workers on the Upwork platform, I’ve observed that those who invest time in learning which tools are best suited for particular tasks, how to combine tools effectively, and how to use them skilfully succeed in turning AI from a threat into a resource.AI tools also free up time and cognitive capacity, enabling workers to try out new tasks, create side projects and think of new ways to create value. For example, market researchers who use AI for data collection and initial analysis can devote more effort to interpretation and application. In this way, individuals become more career-resilient, while organisations also benefit. There’s a caveat: Early-career employees should prioritise hands-on learning, which can mean avoiding AI use when it’s readily available. Our study shows that the professionals who benefit most from AI are those who already know what “good” actually looks like in a given context. In contrast, when individuals rely on AI without first developing this foundational understanding, they often struggle to detect errors or meaningfully improve AI outputs. For individuals, the prospect of being displaced by AI breeds uncertainty, anxiety and a sense of diminished status and agency. Socially, widespread job insecurity can deepen the divide between those who benefit from AI and those who are displaced. In “mass unemployment” scenarios, the legitimacy of the political and economic status quo could be destabilised. To mitigate or avoid these outcomes, workers need to be provided with not only support that helps them reskill but also support to help them reinterpret AI disruption as a temporary transition and an opportunity to find more meaningful work. Leaders, on their part, should treat workers as capable contributors who can identify and create new value, rather than as surplus labour. Cultivate deep domain knowledge and “taste”Victoria Sevcenko, Assistant Professor of StrategyMost of the labour market change happening now is the restructuring of existing roles to incorporate AI, not the appearance of new categories. The floor on acceptable output seems to have risen: People are expected to come in able to do things they might have been given more time to learn, and the average entry-level job might start to look more like a mid-level job.There will likely be more demand for deep domain knowledge and “taste” – that intuitive sense of what good work looks like, what counts as a contribution, and what is novel or sloppy. Within those redesigned roles, there will likely be more demand for deep domain knowledge and what I will call “taste”, which I shall explain below. Here are some specific actions individuals can take to stay competitive:Use AI extensively, preferably the best available models. This will teach you about what AI can and cannot do, and what you, as a human, are uniquely better at. As the models evolve, you can also spot the direction of improvement faster and more accurately.Build “taste” in your specific field, which is that intuitive sense of what good work looks like, what counts as a contribution, and what is novel or sloppy. Taste is typically socially constructed: it’s co-created by a community of practitioners and shaped by what they think matters, and much of it isn’t documented well enough for AI to pick up on. Build taste by interacting directly with people in your field and getting regular feedback from peers. You can’t rely on AI alone for this, and it’s also harder to be original in your thinking if you do.Build critical thinking. By this I mean the ability to assess what you know, what you don’t and what you are uncertain of, and to step back, reflect and adjust. It’s debatable how “trainable” people are in these skills, but some people are clearly stronger, and this gives them a big advantage. Getting feedback on your work and learning to test your own knowledge are all part of the training.Learn how AI works. You don’t need to build the models, but you need enough knowledge to anticipate what AI will be good or bad at, and to make sense of improvements as they emerge.Next week, we’ll look at what companies and organisations can do to mitigate AI’s impact on jobs and the talent pipeline.]]></description>
                  <pubDate>Mon, 04 May 2026 02:05:12 +0000</pubDate>
                  <guid isPermaLink="false"> 48571 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/career/ai-jobs-what-workers-can-do-protect-themselves#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-05/shutterstock_2452515473_1.jpg?itok=2JMUGZnY" type="image/jpeg" length="302566" /><dc:creator>Phanish Puranam</dc:creator><dc:creator>So Yeon Chun</dc:creator><dc:creator>Winnie Jiang</dc:creator><dc:creator>Victoria Sevcenko</dc:creator></item><item><title>AI and Career Reinvention</title>
                  <link>https://knowledge.insead.edu/career/ai-and-career-reinvention</link>
                  <description> <![CDATA[In the new edition of “The INSEAD Perspective: Spotlight on Asia” podcast series, Sameer Hasija, Dean of Asia at INSEAD, speaks with Winnie Jiang, Assistant Professor of Organisational Behaviour, about how organisations and individuals in Asia are changing the way they think about work, training and careers. In particular, Jiang identifies a fundamental shift from "institutionalised" to "uninstitutionalised" career transitions. Unlike the past, where career changes – such as moving from journalism to law or banking to baking – followed clear routes and required standard qualifications, today’s job landscape has been totally upended by AI and geopolitical uncertainty. This makes identifying "safe" next steps, or a stable career that can guarantee success nearly impossible, creating real anxiety. AI’s transformation of how skills are learned and a global reduction in entry-level hiring are further feeding a growing unease among many employees about their futures.  For Jiang, the key to navigating this disruption is not to resist technology but to actively embrace experimentation. By becoming experts in specific AI tools, individuals don’t just increase company productivity, they can also enhance their own roles and add greater meaning to their work.Jiang illustrates this with her research of a Chinese automobile firm that successfully shifted its narrative around AI from cost savings to employee empowerment. By framing the technology as a tool to delegate mundane tasks, the company freed up employees for more meaningful work, turning their initial fear into deeper engagement and greater job satisfaction. Nobody can tell which career is going to promise you the stability and the status anymore. - Winnie Jiang The shift towards "uninstitutionalised" careers may offer another positive. In many of Asia’s collectivist and "face" cultures, where career choices have historically been tied to family pride and stability, current uncertainty may ironically liberate younger generations to pursue work that they find personally fulfilling. Schools like INSEAD have an important role to play in this transformation. It is their responsibility to help people and organisations turn technological disruption into an opportunity for professional growth that benefits both the firm and the individual.]]></description>
                  <pubDate>Thu, 30 Apr 2026 01:06:46 +0000</pubDate>
                  <guid isPermaLink="false"> 48566 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/career/ai-and-career-reinvention#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-04/reinvention_in_ai.jpg?itok=DIqoSOrr" type="image/jpeg" length="557616" /><dc:creator>Sameer Hasija</dc:creator><dc:creator>Winnie Jiang</dc:creator></item><item><title>The Playbook for RIMOWA’s Transformation</title>
                  <link>https://knowledge.insead.edu/marketing/playbook-rimowas-transformation</link>
                  <description> <![CDATA[Before 2016, when LVMH acquired German luggage manufacturer RIMOWA, few would associate the brand founded in 1898 with luxury. After all, the household name from Cologne was distributed in mom-and-pop stores and wasn’t well-known outside of Germany.That took a turn when it entered the list of “10 coolest new things” after the acquisition, under the leadership of then-CEO Alexandre Arnault. From then on, known for its product design, functionality and craftsmanship, it “made flying look like a luxury again”.Branding is so often tied to imagery that few attempt to transform their brands inside-out. But a fervent focus on product turned out to be the winning factor in creating a product-centric brand out of RIMOWA. In this way, it carved out its own market: functional luxury luggage.From product to experienceRIMOWA’s transformation playbook was based on four key pillars: retailisation, traction, innovation and sustainability.Retailisation was key to elevating the brand. Pivoting from wholesale distribution, RIMOWA turned to selling directly to customers so it could provide them with the full brand experience. In addition, it had a strict no-discount policy and a distribution philosophy based on simplicity: minimalist, efficient stores, coupled with flagship stores in metropolitan cities such as New York, Paris, Tokyo, Seoul, Singapore and Hong Kong. From the shop window to the staff uniform, the brand radiates timelessness.Then there’s traction. What sets the luggage maker apart from its competition are its product excellence, embodied in the “Germanness” of the brand, and its client care. In fact, reparability and durability are at the heart of its design, and circularity is a large part of its ethos. Instead of being tucked away in a workshop, product servicing that offers instant repair is highly visible in RIMOWA stores. The brand doesn’t shy away from showing beaten-up products. Instead, it portrays them as symbols of a lifetime of memories.To drive desirability, innovation is indispensable. In July 2022, RIMOWA upped the ante by offering unconditional lifetime warranty. It also rolled out a buy-back programme, creating a secondary market for used and refurbished RIMOWA aluminium luggage, which helped normalise the use of pre-loved products. The brand is able to do because it fully owns its manufacturing facilities and because it had long made repair service available in shops.Other initiatives towards more sustainable products include limiting air transport to less than 4 percent of freight cost and using more environmentally friendly materials.Style + functionality = demandEven though Louis Vuitton rose from similar roots of crafting luggage, being part of LVMH didn’t mean RIMOWA had to follow the same high-fashion path. Instead, the brand built on its global reputation for design, make and client care, and stayed true to its product-centricity. Featuring the aluminium body, polycarbonate material and multi-wheel system, its marketing campaigns carried messages such as “Designed in Germany. Engineered for Life” and “Born in Germany. Engineered for the World.”As RIMOWA continues to innovate and build a new innovation lab in Cologne, it is keenly aware that it takes more to create a contemporary luxury suitcase market. To strengthen its positioning in the fashion and luxury sphere, the brand partnered with celebrities such as Rosé, Jay Chou and Lewis Hamilton, who are actual users of its luggage. It innovates through artistic collaborations with museums and German design schools to surprise and excite consumers and create demand through style that complements functionality. RIMOWA’s strategic approach to collaboration means that collaborating is never a commercial exercise.The road aheadWhere does a brand like RIMOWA go from here? This is a vital question for all brands in the luxury industry. New products such as bags, mahjong cases and eyewear are some initial forays and more may follow. Importantly, beyond the landscape, the brand must stay true to its vision built on authority, luxury and being cool.Specifically, authority comes from the product and must be maintained at all costs to ward off threats from new entrants or copycats. In order to be, and remain, cool, creativity is essential. After all, you can’t just claim that you’re cool: you’re cool because people believe you are. Finally, for luxury brands, control of distribution is key, but it’s not enough. You need to avoid complacency, constantly add new features and keep innovating.]]></description>
                  <pubDate>Mon, 11 May 2026 01:00:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48551 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/marketing/playbook-rimowas-transformation#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-05/rimowa_exhibition.jpg?itok=ZmnfSby_" type="image/jpeg" length="859556" /><dc:creator>Frederic Godart</dc:creator><dc:creator>Hugues Bonnet-Masimbert</dc:creator></item><item><title>Europe&#039;s Historic Second Chance: Leading AI’s Next Wave</title>
                  <link>https://knowledge.insead.edu/economics-finance/europes-historic-second-chance-leading-ais-next-wave</link>
                  <description> <![CDATA[Three decades of the digital economy have been an era of missed opportunity for Europe. While it can boast of research excellence, it can hardly claim to have exported much world-conquering technology – unlike the Big Tech giants of Silicon Valley. The current AI wave looks like more of the same: dominated by the data-saturated Magnificent Seven that were forged in the internet era and have never loosened their grip since.The fact is that Europe, for all its engineering talent and capital depth, is the least competitive of the major digital economies. This has opened up a value creation gap that could run into the trillions. What’s new? Actually, a lot, if Europe plays its card right. A second wave of AI-era innovation might hand the Old Continent something the technology industry rarely offers: the chance to run the race again.Europe’s strengthsAI development today is largely about bigger foundation models mainly trained on internet data, larger data centres and ever more capital-intensive computational scale. This is where the vast majority of investment currently goes. Nobody can know today if these huge bets, often framed as a race to agentic AI or artificial general intelligence (AGI), will pay off for the latecomers to the Nvidia, OpenAI and Anthropic gamble. But one thing is now becoming clear: the next phase of AI innovation will be won where intelligence meets matter – robotics and manufacturing, chemistry and materials, bio-pharma and healthcare, energy systems, logistics networks, and industrial operations. In other words, in the physical and scientific domains. Three key factors may be even more critical than they were in the internet-dominated AI era: scientific talent, industrial strength and production know-how, and ecosystems across multiple sectors. This is precisely where Europe’s underlying advantages are hiding in plain sight. Companies like Siemens and Bosch, Airbus and Dassault Systèmes, Stellantis and Scania, BASF and Bayer, ASML and SAP, and Roche and Novo Nordisk operate some of the world’s most advanced industrial systems. Europe’s factories, supply chains, energy grids, laboratories and engineering workflows generate vast streams of high-quality real-world data. Yet Europe has barely begun to turn this resource into AI-native industrial platforms and new global champions.The following numbers may surprise European as well as American investors. The European Union produces 22% of global AI research journal articles, compared to 17% by researchers based in the United States. Some 2.2 million people graduate with STEM diplomas from European universities each year, compared to 1.4 million from American ones. Europe employs 2.15 million researchers and spent €403 billion on R&D in 2024. And unlike the US, which is strong mainly in software, Europe’s industrial base is enormous and automation-ready: EU manufacturing generates €2.5 trillion in value-add and operates 219 industrial robots per 10,000 employees – exactly the substrate where AI’s next productivity wave will land.  Scientific talent, industrial strength and production know-how, and ecosystems across multiple sectors... This is precisely where Europe’s underlying advantages are hiding in plain sight.  Finally, Europe’s underestimated advantage is that it already runs EU-funded, cross-border ecosystems that stitch together universities, industry, startups and the public sector. Horizon Europe, the EU’s key funding programme for research and innovation, pours €93.5–€95.5 billion into collaborative R&I across fields from health and energy to mobility and manufacturing between 2021 and 2027.The US has clearly taken note. It is no coincidence that Washington recently launched the Genesis Project aimed at strengthening American industrial, manufacturing and scientific domains.It’s about scientific talent, industrial strength and sectorial breadthThe depth as well as breadth of Europe’s industrial sectors provide not only data and know-how, but also the necessary market conditions for innovation. Every startup needs customers above all. Investors’ money is good, but customers’ is better. And every investor, as well as every founder, needs exit paths. And here lies another often overlooked key European advantage. For years, success in tech entrepreneurship and investment has been defined either as an IPO or an acquisition by one of the Big Tech companies. In the era of physical AI, this is about to change fundamentally, to the benefit of Europe’s tech founders and their investors. AI entrepreneurs will not have to hope for a lucky punch with the deep-pocketed Magnificent Seven Big Tech in the US but charter new exit paths with hundreds of industrial players on home soil. Europe can create a win-win environment at scale: Entrepreneurs and investors have plenty more reasons to start and fund a company, while current industrial players have access to the latest innovations from the labs. Some of today’s industrial Goliaths may be disrupted by the AI newcomers, others will only be strengthened through innovation. In both cases, value will be created and captured either by a cohort of new players: AI-native global industry leaders or incumbents infused with startup AI. It’s time to finance the commercialisation of innovationsEurope’s central challenge is not invention but innovation in the Schumpeterian sense of the term: building companies at scale to conquer markets. In 2024, US startups captured roughly 74% of global AI venture funding while Europe attracted about 12%. The US spawns about four times as many AI unicorns than Europe. Analysts estimate the broader EU-US investment gap in information and communications technology and cloud computing at US$1.36 trillion, underscoring how much industrial digital infrastructure Europe still needs to build.The new physical AI-driven market presents a huge opportunity for a comeback. The next wave of European AI companies could capture over 25% of the global next-generation AI market, in line with its research and industrial contributions – if the region manages a step change in turning breakthroughs into venture-scale businesses.The bottleneck is neither talent nor ecosystems. It is the commercialisation capacity of new ideas at speed and scale: connecting labs across borders, building stronger pathways from discovery to company formation, and linking European deep-tech founders to global capital, customers, talent and distribution networks.Encouragingly, policymakers are beginning to respond with historic ambition. Earlier this year, the European Commission launched its €200 billion InvestAI initiative, including €20 billion earmarked for AI gigafactories. For the first time, Europe is signalling its intention to match scientific excellence with industrial-scale capital.The timing matters. AI adoption is accelerating rapidly across the economy. OECD data show that the share of firms using AI has risen sharply – from 8.7% in 2023 to over 20% in 2025. AI transformation is no longer confined to Silicon Valley labs. It is spreading across factories, hospitals, laboratories, logistics networks and energy systems – precisely where Europe retains deep structural strengths.For venture capital, private equity and institutional investors alike, this second chance at AI is also one of the most compelling investment opportunities of the coming decade. Backing the next generation of research-driven AI companies can generate outsized returns as Europe converts its talent and industrial advantages into global market leadership.Importantly, Europe’s opportunity is not to emulate Silicon Valley model for model. It is to innovate differently: to build AI-native companies rooted in scientific depth, industrial integration and responsible governance. Moreover, Europe’s diversity, strong institutions and commitment to rule of law can become competitive advantages in a world increasingly shaped by trust, security and social complexity.From laggard to leaderAs the new AI wave unfolds, Europe may transform from laggard to leader. Unlike incumbent ecosystems, which have already invested, or perhaps sunk, hundreds of billions into today’s foundation-model architecture, Europe’s current generation of AI researchers and entrepreneurs can start fresh. Entire nations can be disrupted as well. Political scientist Jeffrey Ding recently argued that major technological transitions have repeatedly reshaped global power – from Britain in the first Industrial Revolution, to Germany’s rise in the age of chemicals and engineering, and American dominance in the era of mass production, computing and the internet. History rarely offers second chances. The coming AI wave might be one for Europe. The Old Continent should seize it. A version of this article was published in Fortune.]]></description>
                  <pubDate>Thu, 23 Apr 2026 01:34:29 +0000</pubDate>
                  <guid isPermaLink="false"> 48546 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/economics-finance/europes-historic-second-chance-leading-ais-next-wave#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-04/shutterstock_2459225475_1.jpg?itok=2CltVLUS" type="image/jpeg" length="453295" /><dc:creator>François Candelon</dc:creator><dc:creator>Theodoros Evgeniou</dc:creator><dc:creator>Thomas Ramge</dc:creator></item><item><title>Can Your Audience Stifle Your Creativity?</title>
                  <link>https://knowledge.insead.edu/operations/can-your-audience-stifle-your-creativity</link>
                  <description> <![CDATA[Independent creators strive to attract an audience but often find themselves struggling to manage expectations and opinions once their work gains widespread appeal. We want to trust expert estimations and forecasts but shouldn’t take aggregated estimates at face value. In other research featured this month, INSEAD faculty unveil fresh insights on charge anxiety – the new range anxiety – in the adoption of electric vehicles (EVs) and how the market rewards companies that invest in AI and advanced analytics.From range anxiety to charge anxietyIs range anxiety still the main factor limiting EV adoption? Charge anxiety seems to have taken over. It arises from five factors: Hardware: Will the charger’s plug fit my vehicle, and does it work?Software: Will my app or card work at the specific charger?Location: Is a charger conveniently located?Time: How long will charging take?Price: How much will it cost? Anton S. Ovchinnikov and his collaborator present three empirically grounded analytical models, each fitted to real data from industry partners, to analyse key issues in the current state and potential trajectory of public EV charging infrastructure. In particular, they focus on the speed of fast charging, the scale of a fast-charging station and driver arrival patterns.Read the full paperHow independent creative workers negotiate audience relationshipsIndependent creative workers, from visual artists to musicians, need to achieve widespread appeal. But once they do, managing the relationship with their audience is the next challenge. In a study of independent creators who share their work on digital platforms, Spencer Harrison and his co-authors discovered that after attaining a substantial audience, they get deeply “entangled” with their audience, which then affects their approach to creating. This dysfunctional entanglement is characterised by an oppressive dependence on audience reactions, struggling with platform volatility and experiencing distressing emotions. But all’s not lost. Strategies such as distancing themselves from audience input, depersonalising audience critiques and connecting more deeply with their own ideals can help creators move towards a better state, allowing them to once again capture meaning from their audience and to view platform work as sustainable.Read the full paperShould you aggregate expert estimates and forecasts?How frequent are large disagreements in human judgement? Miguel Sousa Lobo and his co-author found that the frequency with which an individual judgement differs substantially from the consensus looks less like a bell curve (or a normal distribution, as widely claimed), but instead is asymmetrical (fat-tailed distribution). The findings have important implications for the aggregation of expert estimates and forecasts, highlighting why we cannot simply rely on averaging. The researchers propose a simple heuristic, which performed well for the range of distributions they studied: 73 data sets from four different sources that included over 169,000 estimates and forecasts.Read the full paperHow the market values AI and advanced analytics investmentsHow do capital markets respond when firms in traditional industries integrate AI and advanced analytics (AIAA)? Having analysed stock market reactions to 397 announcements by biopharmaceutical firms between 2001 and 2020, Michael Freeman and his co-authors found that average returns following the integration are modest, at 0.58 percent. But the average masks variations arising from firms’ characteristics and deal attributes. For instance, are the firms positioned to capture value or are they destroying wealth? Are they organisationally ready, as suggested by their R&D intensity and asset turnover? Are the investments focused on opportunity discovery or simply optimising current operations? The findings highlight the nuanced relationship between AIAA deals and market valuation.Read the full paper]]></description>
                  <pubDate>Wed, 22 Apr 2026 01:00:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48536 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/operations/can-your-audience-stifle-your-creativity#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-04/wound_up_man.jpg?itok=6Omc0aNg" type="image/jpeg" length="600585" /><dc:creator>Lily Fang</dc:creator></item><item><title>The Role of Business in the Humanitarian New Normal</title>
                  <link>https://knowledge.insead.edu/responsibility/role-business-humanitarian-new-normal</link>
                  <description> <![CDATA[Since the United States withdrew its support for the international humanitarian community in early 2025, it has cut US$709 million in life-saving grants. The United Kingdom, Germany and the European Union have followed, slashing over 30 percent of their aid budgets and channelling part of those funds to re-armament. Unfortunately, the US’ spectacular success in fighting disease in low- and middle-income countries in the last 30 years is now reversed. In this “new normal”, we’ve seen the collapse of global response capacities and the disruption of the UN cluster’s efforts to coordinate with NGOs on the ground – at a hefty cost. By June 2025, the funding cuts to USAID health programmes had led to the deaths of nearly 270,000 adults and over 560,000 children, at a rate of 88 deaths per hour.  The funding cuts could result in more than 14 million additional deaths by 2030, according to a Lancet study. In the words of Esther Duflo and Abhijit Banerjee, both Nobel-prize-winning economists and poverty researchers: “For many throughout the world, this is a bloody time”.Unintended consequencesThe mortality figures are appalling, but the indirect consequences also deserve our attention. We are seeing the emergence of unintended risks to our social and economic systems: the loss of efficiency, reliable information and influence. On the ground, the nearly 42-percent cut in funding has led to layoffs of 12,000 humanitarian workers and closure of at least 22 humanitarian organisations, representing a critical loss of talent and institutional memory. Take the US’ construction of a floating pier for the delivery of 2 million meals to Gaza per day. Its almost immediate shutdown (attributed to insufficient training, planning and equipment failures) was followed by slow and non-transparent food distribution by military contractors on the ground. This shows that defence and foreign policy bureaucracies face a steep learning curve in responding quickly to emergencies. When the lines between humanitarian and military activities are blurred, it quickly becomes apparent that military capacity is not inherently designed, or equipped, to save lives.  In addition, access to reliable information is compromised due to the politicisation of humanitarian work fuelling misinformation. Since state-led initiatives are not subject to the same rigorous audits and restrictions as international humanitarian organisations, the result is less transparency and accountability, such as the impact of the US$230 million of taxpayers’ money spent on the failed pier project. In the current geopolitical context, the unintended consequences of the new normal – the erosion of neutrality, impartiality and independence – directly impact the integrity of our modern society.  Global citizens cannot stand by as hard-won humanitarian gains and institutions are discarded.We’re all connectedBusiness is intricately linked to economic development. With globalisation, the integration of global supply chains has boosted economic growth, enabling each country to invest in its strengths and source the rest from the world. More importantly, in the last 30 years, it has lifted over a billion people out of poverty. Then there is the interconnectedness between aid providers and the local businesses and economy. For decades, international businesses have benefited from the spillover effects of USAID’s engagement in less developed countries. When local suppliers are contracted by humanitarian aid projects, it gives them the opportunity to expand their business and this in turn drives local economic growth. The entry of aid providers can trigger the development of institutional capacity, which in turn, enables international firms to operate in these emerging markets.Recent large-scale global crises such as the Covid-19 pandemic have made us acutely aware of our interconnectedness. They taught us that no one is safe until everyone is safe, and that trade has helped solve shortages as they occurred around the world. Even rich countries may one day need the kind of emergency relief that depends on the kindness of strangers. The question is how to benefit from the linkages while mitigating the risks. The difference that business makes Could humanitarianism, which directly contributes to an equitable, peaceful and rule-based system, be defined as a public good, like the environment? As global citizens, we should preserve it. And business leaders are uniquely qualified to do so by translating good intentions into plans with realistic resource allocations and measurable results. Targeted social projects by businesses, such as Walmart’s support for hurricane response in the US, are not new. Global logistics companies have joined the Logistics Emergency Team network to offer transport capacity for emergency operations. Companies like Takeda Pharmaceutical Group have co-funded public health supply chain capacity-building with governments in Africa. These companies are not only contributing resources to emergency operations; they are helping to develop capacities. In less developed countries, as they build infrastructure and trade with local firms, they are in fact promoting supplier development. And as these markets develop, their increased economic participation and growth combine to create a virtuous cycle, increasing access to goods and services, and ultimately benefiting the local communities and businesses. Business can also use its know-how (and know-who), to work with governments and key stakeholders to design more resilient systems that are not dependent upon single donors. For example, in response to drastic funding cuts, a public-private supply chain leadership group convened by the European Civil Protection and Humanitarian Aid Operations  has identified over 100 measures to improve aid distribution. But dedicated expertise is needed for public-private partnerships to succeed. In particular, governments will need to deepen their managerial capacity, make better stockpiling decisions and enable cross-border pooling of essential medicines ahead of the next global pandemic. Agents of changeAdam Smith is known for writing the classic “The Wealth of Nations”, but he also stressed the importance of empathy, fairness and community, because efficiency without virtue undermines the economic system. Our research in humanitarian operations reveals that virtue without efficiency is equally unacceptable. As the business school for the world, INSEAD has a role in informing policymakers, business and the public, empowering them to make the needed change to make humanitarian systems more resilient. For decades, our Humanitarian Research Group has worked on improving structures and processes in both commercial and humanitarian organisations to improve performance. Beside strengthening collaboration between organisations extending aid, our knowledge, built on decades of rigorous scientific studies, is translated into action by partners such as the International Federation of Red Cross and Red Crescent Societies. Practical contributions include analysing how much stock to pre-position in crisis-prone regions, devising more efficient use of humanitarian transport fleets, and updating asset replacement rules to reduce costs and carbon footprint in humanitarian operations. Our body of knowledge is fodder for new business models. Business can be a force for good if they adapt commercial practices of supplying food, shelter or medical care to meet the needs of crisis-prone regions. But it’s not just about ideals. The current geopolitical situation and ongoing war in the Middle East drives home the message: The destabilising effects of disease, starvation and political upheaval – on economies, communities and individuals – are real. ]]></description>
                  <pubDate>Thu, 16 Apr 2026 01:00:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48531 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/responsibility/role-business-humanitarian-new-normal#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-04/lifeboat_0.jpg?itok=rfelb8FJ" type="image/jpeg" length="753539" /><dc:creator>Bublu Thakur-Weigold</dc:creator><dc:creator>Luk Van Wassenhove</dc:creator></item><item><title>Your Strategy Is a Flashlight, Not a Business Plan</title>
                  <link>https://knowledge.insead.edu/responsibility/your-strategy-flashlight-not-business-plan</link>
                  <description> <![CDATA[I recently had the opportunity to speak at ChangeNow, a global summit that brings together leaders working at the frontier of social and environmental change. The question that opened our panel felt as urgent as any on the programme: What does strategy actually mean in a world that won’t hold still?My answer was blunt. The business plan, as most organisations conceive it, is dead.Strategy is not a business plan. It’s a flashlight, a tool you use to look for opportunities in a constantly shifting environment. In a world where the assumptions underpinning a strategic document can be overtaken by events within weeks, the traditional approach of writing a plan, shelving it and executing against it is no longer sufficient.Search, don't planThe alternative is to understand strategy as a dynamic process of search, one that continuously attempts to match what an organisation has with what the world currently demands. This means regularly asking hard questions: What are our goals? What resources and capabilities do we currently lack to achieve them? What is the most efficient path to closing those gaps?Gaps can be addressed in three ways: through internal development, through strategic partnerships and alliances, or through acquisitions. For social entrepreneurs and impact-driven organisations, the first two are the most relevant. Build what you can develop yourself; borrow, through partnerships, what would take too long or cost too much to build alone.Critically, this isn’t a one-time exercise. It’s a recurring discipline. Organisations must install rules around decision-making: structured processes for periodically reviewing whether they are in the right markets, pursuing the right objectives and building the right capabilities. Besides determining the strategy, consider what would happen if your assumptions are wrong, and how often you’re rethinking what you’re doing. At ChangeNow, I spoke with Monica de Virgiliis, chair of Chapter Zero France and a director at Air Liquide, whose thinking on strategy in uncertain environments maps closely onto this. She distils the challenge into three behaviours:Speed: Companies must move fast, questioning the constraints that previously slowed them down and compressing years of experimentation into months.Optionality: Rather than betting on a single vision of the future, organisations should develop strategies that remain viable across multiple scenarios.Resilience: Building redundancy into systems, diversifying across suppliers and technologies and, critically, adopting a leadership style that admits uncertainty rather than pretending to resolve it.As she mentions in her TEDx talk, the leader's role is not to have the answer, but to find it collaboratively.When good intentions go wrongFootwear brand Toms is one of the most instructive examples of a strategy that began with genuine social purpose and ran into the limits of planning. The original model was simple: For every pair of shoes sold in more economically developed markets, a pair would be donated to communities in less economically developed markets. The intent was noble, but execution created a cascade of unintended consequences.Donating generic footwear to communities with local shoemaking industries risked undermining livelihoods. The shoes were often ill-suited to local climates and terrains and, at a more fundamental level, the model addressed the symptoms of poverty rather than its root causes.Acting on these unintended negative consequences, Toms adapted its business model. Rather than donating shoes, the company now directs a portion of its profits to partner organisations embedded in local communities. These organisations understand local needs and can deploy resources far more effectively. In some cases, this means funding local enterprises to purchase shoemaking machinery, simultaneously generating employment and producing contextually appropriate products.The core concept remains the same – sell in more economically developed countries, support communities elsewhere – but the mechanism changed entirely. This is strategy as search in practice: a willingness to let go of the original plan when evidence demands it, while staying committed to the underlying mission.Ecosystem thinking: the Pratham modelIf Toms illustrates the need for strategic flexibility, the Indian literacy organisation Pratham offers a masterclass in ecosystem orchestration.Pratham began by developing two core assets: a rigorous methodology for assessing childhood reading ability and an evidence-based approach to improving it. Instead of attempting to scale by building its own operations across dozens of countries, a capital-intensive and operationally complex undertaking, it packaged those assets and deployed them through a network of local partners. Today, its methodology is used in Colombia, Brazil, the Philippines, Nepal, Egypt and beyond.Childhood literacy might seem an unlikely candidate for global scalability, but what scales is not necessarily the organisation itself – it’s the knowledge and the model. Identify what you uniquely know, codify it and find partners who can carry it further. Such ecosystem thinking can help organisations with limited resources achieve disproportionate reach.The world has always been turbulentThere’s a temptation to treat the current moment as singularly unpredictable, to argue that AI, geopolitical fragmentation and shifting trade structures have created a complexity without historical precedent. That argument doesn’t hold because the world has always been dynamic. What we’re finally acknowledging is that strategy-as-plan was always the wrong model.Steam, electricity, the telephone, the railroad, semiconductors, the internet – each brought disruption that seemed, at the time, without parallel. The organisations that navigated those transitions successfully weren’t the ones with the best plans. They were the ones with the clearest sense of purpose, the sharpest awareness of their capability gaps, and the flexibility to search for new paths when the old ones closed.The prescription is the same whether you’re building a for-profit venture or a social enterprise: know your gaps, know your ecosystem, stay ready to pivot. Treat strategy not as a destination, but as a continuous act of search. ]]></description>
                  <pubDate>Tue, 14 Apr 2026 01:00:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48526 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/responsibility/your-strategy-flashlight-not-business-plan#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-04/light_small.png?itok=iJrNvQ2G" type="image/png" length="973364" /><dc:creator>Andrew Shipilov</dc:creator></item><item><title>How to Build A Successful AI Hub</title>
                  <link>https://knowledge.insead.edu/strategy/how-build-successful-ai-hub</link>
                  <description> <![CDATA[While exact estimates vary, the size of Silicon Valley’s economy was comparable to China’s GDP in 2024, while the combined market capitalisation of major Silicon Valley tech companies topped US$20 trillion in 2025. Considering these staggering numbers, it’s no surprise that many low-income countries have tried to replicate the “magic” of Silicon Valley. In Africa, for example, a number of technology parks have been launched, including Konza Techno City in Kenya, Yabacon Valley in Nigeria, Kigali Innovation City in Rwanda and, most recently, Silicon Accra in Ghana. Common driversBased on my discussions with various ministers of economy, digital transformation and education from Asia, Africa and Latin America, there are four key drivers or objectives behind these tech-hub efforts:Sovereignty: A means of achieving independence from global tech behemoths such as Microsoft, Google, OpenAI and Meta.Localisation: The means to develop content and applications that meet the specific needs and languages of local communities, particularly those underserved by existing content.Building muscle: Offering an environment that can support and nurture local talent to implement these local solutions.Building a viable ecosystem: One that is commercially successful and sustainable, without the need for long-term government subsidies. Lessons in failureWhile the tech hub examples listed above are still under development, they are not the first to try and recreate Silicon Valley. The majority of previous efforts have failed, due to reasons ranging from talent scarcity to political uncertainty.For example, “Hope City,” Ghana’s first Silicon Valley project never took off after its 2013 launch due to bureaucratic and funding issues. Other notable failures include Cyberjaya in Malaysia, KAEC Tech & Innovation Zones in Saudi Arabia, and Amaravati in India.While each offers different lessons, all underline the fact that Silicon Valley’s success is built on a mixture of features that are often intangible and very difficult to reproduce. These include a risk-tolerant culture, closeness to top-tier universities, networks of talent, abundant venture capital and a long history of innovation. This is particularly difficult to replicate in environments that face stifling bureaucracy, alongside a culture of aversion to risk and collaboration.A commercial approachFor the last two decades, INSEAD’s TotoGEO AI Lab has been working with multinationals and non-profits, including the Gates Foundation, to tackle the “content divide” – the lack of relevant, usable online content in many local languages and contexts. As part of this extended mission, the lab is now partnering with entrepreneurs worldwide to use AI in building a new generation of local technology hubs. One such partner is David Osei, co-founder and CEO of Silicon Accra whose Twelve Springs Investment Group is developing business parks that blend high-yield commercial ventures – hotels, luxury apartments, co-working hubs and golf courses – with subsidised or free space for startups and incubators.Launched in 2016, Silicon Accra’s first phase plans office space for 20,000 people and aims to host research institutions, corporates and ICT startups in an ecosystem that drives technological development in Ghana. What sets it apart from many tech hubs is the intention to use profits from real estate developments to subsidise the tech ecosystem. Following a model inspired by London’s Canary Wharf and South Korea’s Songdo, it assumes rapid land appreciation, early anchor tenants, supportive capital markets and startups that can eventually pay market rates. Osei stresses that Silicon Accra is a long-term play. The infrastructure is already in place but full build-out of apartments, offices, labs and co-working spaces is ongoing. The long-term ambition is that more than 50 startups, linked to on-site university research labs and training programmes, will be based in Silicon Accra by the end of the decade.TotoGEO’s role is to provide the core technology infrastructure and monetisable platforms that help the park shift from a real-estate-led phase to a productive, revenue-generating innovation ecosystem. Over time, TotoGEO will transfer both know-how and technology to Ghanaian ownership, helping establish a local AI lab that builds customised products for sectors such as agriculture, fintech and govtech, and offering training and other services.These new revenue streams are intended to support startups, subsidise workspaces and attract anchor tenants, creating a self-reinforcing model for local innovation. Where earlier tech hubs often started from scratch, TotoGEO offers a way to “hit the ground running” by plugging into existing platforms, from B2C search engines to B2B tools, so that emerging ecosystems like Silicon Accra can move more quickly from concept to impact.Alternative approachesSilicon Accra’s real-estate-driven approach is not the only option. Several technology hubs in other low-income economies demonstrate alternative blueprints for success – particularly those that embed artificial intelligence into their core strategy from the outset.Take the National Innovation Center (NIC) in Hòa Lạc, Vietnam. Established in October 2019 under Vietnam’s Ministry of Planning and Investment, NIC is more than a technology park. It’s a government-led attempt to build a national innovation platform. The Hòa Lạc campus, inaugurated in October 2023, anchors a national convening hub which links startups, enterprises, academia and international partners. NIC’s key strategy is to target “mission sectors” – covering areas such as smart factories and cities, semiconductors, digital communications and cybersecurity – to concentrate programming and partnerships. NIC emphasises sector-focused pipelines, rather than a fixed number of resident companies. NIC demonstrates how to use national mandate, strong partners, and sector missions to strengthen focus, buying time for the physical campus to become the default meeting place for start-up ventures.Meanwhile in central Asia, the Astana Hub in Kazakhstan adopts a policy-led cluster model where the core value proposition is not space but incentives and internationalisation. Opened as an IT park in 2018, Astana Hub positions itself as a “bridge to the world”. It runs programmes with global partners such as Google for Startups and California’s Draper University. By late 2025, Astana Hub reported over 1,850 participant companies, including more than 470 with foreign ownership or investment. Success stories have included AI video generation company Higgsfield AI, parking management solution Parqour and agritech platform Egistic. The takeaway is simple: Countries that cannot outspend others on venture capital could make it cheaper and easier to build locally – through incentives, visas, and predictable rules – and then layer export pathways and global accelerators on top.A third approach is represented by Ruta N in Medellín, Colombia, a city-led innovation district. Founded in 2009 as a joint venture between Medellín’s Mayor’s Office, UNE (telecom), and EPM (public utilities), Ruta N demonstrates how a municipality can orchestrate ecosystem development without waiting for national policy change. The strategy combines a designated innovation district with a physical complex that functions as a soft-landing base, thanks to its infrastructure and incentivized rents, for companies of varying sizes.Ultimately, there is no single template for a “Silicon-X”. Be it national mandate, regulatory incentives, district branding or real-estate-backed cash flow, what really matters is identifying a structural advantage and turning it into a catalyst for AI-driven growth.]]></description>
                  <pubDate>Mon, 13 Apr 2026 00:53:15 +0000</pubDate>
                  <guid isPermaLink="false"> 48521 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/strategy/how-build-successful-ai-hub#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-04/creatingtechhubs.jpg?itok=-XqpOjFj" type="image/jpeg" length="896850" /><dc:creator>Philip M. Parker</dc:creator></item><item><title>Aligning Strategy and Culture: The Magic Is in the Middle</title>
                  <link>https://knowledge.insead.edu/strategy/aligning-strategy-and-culture-magic-middle</link>
                  <description> <![CDATA[Culture and strategy are related, although never perfectly. Take some daily routines in organisational life. Do employees engage in meetings or do they multi-task to the point of rudeness through displays of “being above the details”? Do they share information freely or is the best stuff kept in silos? Of course, not all elements of culture will have an impact on a company’s strategy. For example, overly polite meeting cultures do not necessarily mean that dogma is never challenged. But many elements will, some more than others depending on the strategic orientation of the company. Here are some possible orientations: Product/innovation leadership: Staying collectively curious, with longer horizons for thinking and a shared assumption that ambiguity and failure are not “bad”.Operational excellence: Strong discipline in following standards and processes while constantly looking for ways to optimise local routines.Customer intimacy: Listen to customers with a deeply shared assumption that making exceptions and customised solutions for customers are expected. Any company that wants to enhance performance will eventually embark on a culture alignment programme, which can mean culture change. The problem is that companies too often stumble. The reason: organisations address culture through espoused values and HR programmes, without an accurate understanding of the existing culture. By existing culture, I mean a true representation of the routines, processes and habits within a firm, and the underlying assumptions they reveal. Most organisations would have formal structures, but there are also many undocumented and poorly understood habits that allow work to happen: online chats, social networks and informal lines of authority. Even formal structures can look very different on paper versus reality. When attempting to align culture to strategy, most organisations operate at one or two levels: the macro/philosophical, which is mostly about espousing values, and the micro/local, which often takes the form of ticking boxes for individual behaviour. What’s missing is the vast meso level – where the real factors that shape routines and habits sit. Failing to address those structural pulleys and levers will undermine any cultural change effort.Culture work is analytical, not (just) philosophicalBefore leaders can reasonably align culture with strategy, they need to understand the existing culture. This sounds obvious, but it is neither common nor easy. It is too tempting to confuse a company’s espoused values with what it actually does, then simply rewrite those values in fresher terms. What’s needed instead is a cold, hard look at reality. To do this, leaders must think like anthropologists, approaching culture work with an analytical lens and genuine curiosity. While what Edgar Schein called “artifacts” – the routines, habits, structures, language and symbols that we see around us –are not always physical objects, they form the concrete reality of organisational life. Examples include meeting discipline, decision-making norms, office layout, Zoom habits, etc.Crucially, interpreting artifacts requires challenging your own assumptions. Consider two contrasting offices: one open-plan with modern glass dividers, the other traditional with small individual offices and (shock and horror) doors. Without examining lived experience, we might infer that the former is an open, modern and collaborative culture, while the latter is restrictive and hierarchical. Yet closer analysis might reveal the opposite. The open-plan office may reflect a lack of trust from leadership, creating a sense of surveillance and reinforcing hierarchy such that people beg to work from home due to the fishbowl feel of the office. The smaller offices may exist because management trusts its people and understands the need for privacy and concentration. The artifact itself tells us little without careful interpretation. And interpretation cannot come without artifacts (data). And so, studying organisational artifacts should be an iterative process, homing in on the real lived experience. This is careful work that requires leaders to abandon some of their pre-conceived interpretations.The point is that serious analysis – triangulating observations and lived experienced – is necessary. And you might find some pleasant surprises: routines and processes that align beautifully with strategy, without leadership having been aware of them. These are things to preserve, not inadvertently destroy. Increasingly, the volume of data captured within organisations, combined with natural language processing (NLP) and AI techniques, will create new opportunities to surface and analyse cultural behaviours (responsibly, I hope). Through thoughtful analysis, you’ll be in a better position to understand how the context people face every day may be contributing to the misfit between strategy and culture. Target change at the meso-levelOnce you have a clearer picture of real corporate culture, how to do you shape it? The typical starting point is what I call a “dump and run”: new (or refreshed) value statements are issued, a culture programme is launched by HR, follow-up is limited and then everyone moves on. In the worst cases, these operate like popcorn flicks – momentary attention grabbers with no lasting impact.This macro approach, whereby a top-level vision for the needed culture is cascaded, is a useful and necessary step – but it’s unlikely to be enough. If we’re lucky, it is followed by a micro step: HR reprogramming performance reviews to include behavioural attributes, some measures of individual attitudes and mindset. Again, useful –but not enough.What is often overlooked is the messy middle of organisational design. Let me be more specific. Here are 10 main levers that management can control, which are likely to shape the context of an organisation:Hiring policies and search criteriaCompany structures and decision rightsPerformance management and KPIsIncentives and compensationLeadership and team decision processesTraining and leadership developmentStandard operating procedures for client and partner interactionsNetworking and physical layoutLeadership role-modeling and how unscripted conflicts are resolvedCommunication, including internal narratives and interpretationsLet me add a bonus item 11: Culture “booklets” that outline the new desired culture. Yes, these can be useful, but not on its own. There are obviously more elements that just 10, but you get the idea. The point is that these meso features encode the context that people live in every day. This is where deeper assumptions must be confronted and reshaped. These meso structural elements shape how work gets done and determine whether strategic priorities can be translated into everyday behaviour. For instance, if hierarchy and physical separation make collaboration difficult, then espousing collaboration as a value will not be enough to produce meaningful change. Because culture is intangible, trying to preach it into place is not likely to be effective. Culture is shaped through the consistent alignment of these structural elements with the broader vision, purpose and strategy of the company. In that sense, culture change is not about doing a hundred things. It’s about doing two or three things a hundred times.A framework, not a formulaTo summarise, culture alignment operates across three levels. At the macro level, the questions we should be asking are: What does our strategy require from our culture? What is the vision, the central tenets and the narrative for that evolution? At the meso level, it is about how we adjust the levers of management to shape the context that drives those ideas. And finally, at the micro level, we should be asking: How do we capture and reinforce behavioural change? Are we forming new habits? Culture alignment is hard work. But organisations that analyse their existing culture rigorously, surface and challenge underlying assumptions, and redesign structures to support the behaviours they need will be far better placed to improve performance. These steps don’t constitute a formula, but it's a good place to start.]]></description>
                  <pubDate>Thu, 09 Apr 2026 01:13:19 +0000</pubDate>
                  <guid isPermaLink="false"> 48516 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/strategy/aligning-strategy-and-culture-magic-middle#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-03/shutterstock_1926642146.jpg?itok=8z68L_Bk" type="image/jpeg" length="292153" /><dc:creator>Charles Galunic</dc:creator></item><item><title>The Curse of Success </title>
                  <link>https://knowledge.insead.edu/responsibility/curse-success</link>
                  <description> <![CDATA[The World Bank recently released its 2026 Women, Business and the Law report, which measures how laws, regulations and policies shape women’s economic opportunities and private sector development across 190 economies. Among the 10 topics used to measure women's economic empowerment are workplace, marriage, parenthood and entrepreneurship. One of the highest scorers in the world is Sweden.Across the board, women spend more time on housework and carry more of the responsibility for children, which affects their pay and well-being. Sweden offers 480 days of paid parental leave, with 90 days reserved for each parent. These policies help mothers of young children return to work faster. Similarly, when it comes to laws on marriage, mobility and women's pay, Sweden gets a perfect score.But equality rankings don’t tell the whole story. Getting ahead at work comes at a price – even in Sweden.Success comes at a costEconomists Olle Folke (Uppsala University) and Johanna Rickne (Stockholm University) investigated how career success affects marriages. They compared divorce rates among candidates who ran in Swedish municipal elections – those who won and those who narrowly lost. For women, winning an election increased the risk of divorce. For men, the result of the election had no effect on their marriages.Must women choose between their career and their relationship, then? Or are politicians an exception? To explore this further, Folke and Rickne examined CEO appointments across Sweden over a decade. The pattern was the same as in politics: women promoted to top executive positions were twice as likely to get divorced as men who received equivalent promotions.The curse of success isn’t limited to politics and business. In Hollywood, it’s common knowledge that winning an Academy Award is great for your career, but bad for your love life. That is, if you’re a woman.H. Colleen Stuart (John Hopkins University), Sue Moon (New York University) and Tiziana Casciaro (University of Toronto) tracked all the Best Actor and Best Actress nominees from 1936 to 2010. They found that Best Actress winners were more likely to get divorced than actresses who were nominated but didn’t take home the golden statuette. The Best Actor winners, on the other hand, didn’t suffer from the so-called “Oscar curse”.But a separate study suggests male winners aren’t entirely unaffected. When Michael Jensen (University of Michigan) and Heeyon Kim (Cornell) compared winners and nominees against elite actors who were never nominated, they found that the men’s marriages suffered, too. The researchers attributed this to the influx of professional and romantic attention that follows a major public win.Does this mean winning a Swedish municipal election might generate a flood of romantic proposals for female politicians, and that higher divorce rates are just a reflection of their love lives picking up? Most likely not. Folke and Rickne found no evidence of a "temptation effect" in which women's (but not men's) promotions increase their chances of finding a new partner. In fact, women who were divorced and promoted remarried at a slower rate than divorced women without promotions.The explanation lies elsewhere: in deeply rooted assumptions about whose success is acceptable within a relationship. Folke and Rickne found that the couples who weathered a woman's election win with the least damage were those in which income differences remained small after the promotion. The problem wasn’t success itself, but the status disruption it created.The persistence of gender normsResearchers Alyson Byrne and Julian Barling have given this mechanism a name: status leakage. Studying over 200 hundred women in high-status positions, they found that the strain in these marriages was driven by feelings of embarrassment and resentment, on the part of the man, about a partner's career trajectory rather than income differences. Another factor was a sense that the man’s own hard-earned status was diminished by association. These feelings predicted lower relationship satisfaction and higher marital instability. In other words, what matters isn’t just money, but status and prestige as well. Their research also points to what can protect a relationship. Byrne and Barling tested whether a partner's support could lessen these effects and found an interesting result. Offering emotional support in the form of encouragement, understanding and expressions of pride made no difference to a couple remaining together. But giving instrumental support, such as taking on childcare responsibilities, emptying the dishwasher and staying on top of dentist appointments, essentially eliminated the negative effect on marital stability. When partners provided tangible support, the link between a woman's higher status and relationship strain disappeared. What’s important isn’t the chores themselves, but what doing them signals: that both careers belong to both partners. It’s a shift from "your success" to "our success".My INSEAD colleague Jennifer Petriglieri, who has spent years studying how dual-career couples can thrive in love and work, puts it well: The conventional framing of two careers as a zero-sum game is exactly the trap that breaks couples apart. Couples who navigate these tensions successfully are those who communicate openly about their values, boundaries and fears, and treat each transition as something to work through together.The curse of success isn’t inevitable. It lifts when both partners decide, explicitly, that they are on the same team]]></description>
                  <pubDate>Tue, 28 Apr 2026 01:00:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48511 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/responsibility/curse-success#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-03/shutterstock_2256924245_2.jpg?itok=22pR7Lh9" type="image/jpeg" length="309481" /><dc:creator>Kaisa Snellman</dc:creator></item><item><title>Tariffs and Turmoil: Negotiating the New World Order</title>
                  <link>https://knowledge.insead.edu/economics-finance/tariffs-and-turmoil-negotiating-new-world-order</link>
                  <description> <![CDATA[In the latest episode of “The INSEAD Perspective: Spotlight on Asia” podcast series, Sameer Hasija, Dean of Asia at INSEAD, sat down with Pushan Dutt, Professor of Economics and Political Science, to discuss how the new world order is creating a complex economic environment, where traditional business strategies are being upended by unpredictable political and technological shocks.As an expert in international trade, Dutt offers his insights on the impact of the fast-changing United States tariff policies for the Asian region. Ultimately, he advises firms to adopt a "wait and see" approach, suggesting that rash operational moves to counter temporary political swings could end up being a costly, and ultimately unnecessary, mistake.For him, a bigger concern is the massive investment by American and Chinese firms into AI, which could create a significant technological gap between those leaders and other countries. Organisations are historically slow to adapt, but firms in Asia need to fully understand the speed of exponential technological growth and the urgency of being prepared for the "gale of creative destruction" it will bring. In the same vein, countries like India and Indonesia need to overcome the slow pace of their bureaucratic democracies to become more agile and responsive. Whether it’s pivoting India’s IT sector to adapt to rapidly changing needs, or Indonesia’s efforts to move upstream in the nickel supply chain, speed is going to be key. The risk-taking appetite has to go up as well. Business as usual is not going to cut it. – Pushan Dutt With the prospect of an incoherent and uncertain future, at least for the short term, business leaders cannot afford to be delusional about "crises being opportunities". Instead, they need to make sure they have the slack, both in terms of finances and time, to make quick decisions in response to unexpected or unknown crises as and when they arrive.Note: This conversation was recorded before the start of the ongoing Middle East conflict.]]></description>
                  <pubDate>Tue, 31 Mar 2026 01:36:51 +0000</pubDate>
                  <guid isPermaLink="false"> 48506 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/economics-finance/tariffs-and-turmoil-negotiating-new-world-order#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-03/tariffs_turmoil.jpeg?itok=5y7GLXov" type="image/jpeg" length="966875" /><dc:creator>Sameer Hasija</dc:creator><dc:creator>Pushan Dutt</dc:creator></item><item><title>Could AI Tools Actually Help Us Feel Better?</title>
                  <link>https://knowledge.insead.edu/responsibility/could-ai-tools-actually-help-us-feel-better</link>
                  <description> <![CDATA[Over one billion people now live with mental health disorders, according to a 2025 World Health Organization report, costing the global economy more than US$1 trillion annually. At the same time, recent estimates suggest that over 800 million people are already using ChatGPT, and that 70 percent of interactions with the chatbot are for non-work purposes. My co-authored research has documented that around a quarter of those people are turning to large language models (LLMs) for mental health support.A massive uncontrolled, real-world experiment in AI and human psychology is already underway. People aren’t waiting for researchers or regulators to tell them whether it’s a good idea to talk to a chatbot about their problems. They are already doing it. The question is no longer whether AI will shape mental health, but how, and whether we can make these systems safe and effective.Typically, the narrative around digital platforms and mental health has been almost entirely negative. Social media has been linked to rising depression, anxiety and psychological distress, while alarming stories about chatbots giving dangerous advice have reinforced fears that AI will only make things worse. These concerns are real and should not be dismissed. But they don’t capture the full picture. The early evidence suggests that AI’s psychological effects are more varied, and in some cases more positive, than the sensational headlines imply.What the early evidence showsIn our recent study, my co-authors and I examined how people’s well-being changes after brief, structured interactions with chatbots. We tested four exercises grounded in psychological research: savouring positive experiences, expressing gratitude toward someone close, reflecting on sources of meaning, and reframing one’s life as a “hero’s journey”.The results were striking. All four interactions significantly improved happiness, life satisfaction and sense of purpose, while reducing anxiety and depressed mood. These effects emerged after a single conversation lasting just 8 to 10 minutes on average. Perhaps most surprising, the chatbot interactions also increased users’ interest in traditional therapy rather than replacing it – in a way, the tool kickstarted the conversation about well-being.These are early findings and should be treated with caution. But they suggest that well-designed AI interactions can function as a low-cost, scalable entry point for psychological support – not as a substitute for therapy, but as a complement and even a pathway to it.Who is using AI for mental healthInterestingly, in another study, also conducted in the United States, my co-authors and I found that young Black men were the most likely to turn to LLMs for mental health support. This makes sense when you consider the barriers these communities face: cost, insurance gaps, provider availability and stigma. As our respondents reported, LLMs offer something that is immediate, private, free and non-judgmental. For people who have been effectively excluded from the mental health system, that matters.These findings suggests that the people most helped by AI may not be the ones dominating the current public debate around it. Most people agree that AI therapy systems should not replace human therapy, but in many cases, the status quo they are replacing is “nothing”.A framework for doing this responsiblyThis doesn’t mean we should simply offload mental health support to general-purpose chatbots, like ChatGPT. Prior work has shown that chatbots can amplify narcissistic tendencies (through uncritical agreement with the user) and have the potential to influence vulnerable users who may be suffering from psychosis. The stakes in mental health are higher than in most other domains: In the worst case, a poorly functioning system could mishandle suicide risk. We therefore need a principled, critical approach to integrating AI into mental healthcare.Drawing on the analogy of autonomous vehicles, my colleagues and I have proposed a three-stage model. At the assistive stage, AI handles low-risk tasks – psychoeducation, activity planning and collecting behavioural logs – freeing therapists to focus on face-to-face work. At the collaborative stage, AI takes on more responsibility, such as scoring assessments or providing real-time feedback on therapy worksheets, but always under therapist oversight. A fully autonomous stage, where AI independently conducts assessments and delivers interventions, remains a distant and uncertain prospect.The central idea is that the systems should not advance to the next stage until they have shown they are completely safe in the prior stage – we wouldn’t trust a car to drive itself if it can’t park itself or stay in the same lane. To guide evaluation at each stage, we developed the READI framework (Readiness Evaluation for AI-Mental Health Deployment and Implementation). It specifies criteria for safety, privacy, equity, engagement, effectiveness and implementation. For example, a mental health chatbot should be able to detect suicidality and escalate this to human care. It should not be optimised for engagement (i.e. endless chatbot conversations) at the expense of patients getting better. And its effectiveness shouldn’t just be compared to doing nothing, but against existing treatments.Where AI may help most: training therapistsOne area where AI’s potential is especially promising, and the risks more manageable, is in the training of therapists. AI can make training more scalable and engaging by letting trainees practice real-world scenarios without needing real-world patients, much like a flight simulator. I was trained as a therapist myself, and wish that I had access to such tools to practice difficult situations with patients before encountering them for the first time in real life.For example, consider the treatment of post-traumatic stress disorder. There is a new and highly effective treatment for it, called written exposure therapy, which guides patients through five structured writing sessions to develop a trauma narrative. The treatment is cost‑efficient and effective. But training therapists in this method requires close supervision, and new therapists cannot be trained fast enough. We’ve been developing an AI coach that allows therapists to train with simulated patients, while an AI supervisor gives in-the-moment feedback. This way, the trainee gets realistic practice and has to think on their feet. So far, therapists have been loving the early versions of the tool.The bigger pictureWe are at an early stage of understanding how AI will reshape human psychology. The technology and its impact on society is developing faster than our ability to study it, yet hundreds of millions of people are already using it in ways that impact their mental health and emotional lives. Much of the public conversation has been shaped by fear. That fear is not unfounded, but it is also incomplete.The early evidence points to real opportunities: brief AI interactions that improve well-being, access to support for underserved populations, and better tools for training therapists. Realising these opportunities without causing harm will require careful evaluation, honest reporting of both positive and negative findings, and a willingness among psychologists, technologists and policymakers to work together. As with the training tools, my hope is that new AI tools can help us be better at being human for each other.AI isn’t ready to replace human therapists, but it’s already shaping mental health. Now, psychology’s task is to shape that impact wisely.]]></description>
                  <pubDate>Mon, 20 Apr 2026 00:15:07 +0000</pubDate>
                  <guid isPermaLink="false"> 48501 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/responsibility/could-ai-tools-actually-help-us-feel-better#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-03/aitools_mental_health.png?itok=cbriih6B" type="image/png" length="971268" /><dc:creator>Johannes Eichstaedt</dc:creator></item><item><title>Leading Organisational Change Without a Roadmap</title>
                  <link>https://knowledge.insead.edu/leadership-organisations/leading-organisational-change-without-roadmap</link>
                  <description> <![CDATA[Many leaderships teams staring down the barrel of organisational transformation face a similar dilemma: How do you take a leap into the unknown when there’s no clear data, no well-trodden path to follow and no assurance of success? What if the change you're considering is uncharted territory, but waiting for certainty means standing still? Over the years, we’ve seen countless organisations struggle with these questions, whether they’re trying to flatten hierarchies, implement new processes or change how decisions are made. Through our five-year study of a company we’ll call Pharma Global* (PG), we uncovered key insights into how executive teams can deftly navigate a high-stakes and uncertain transformation and move forward with conviction.Trap #1: Overthinking the leap Like its competitors, PG had long relied on a few blockbuster drugs. But by the 2010s, as a major acquisition expanded its R&D pipeline, its commercial team was managing an increasingly complex and expansive portfolio. Unfortunately, PG’s long-standing top-down culture and bureaucratic rules were an obstacle to the agility the company now required. So, its executive team hired two top consulting firms to build the case for transformation. Both said the company needed a significant re-organisation to become more agile. Yet, two years later, the executive team was still asking for more data, benchmarking and risk assessments. The executive team overlooked a crucial factor – the nature of the problem they were trying to solve. Traditional organisational changes are technical problems with clear, well-defined solutions. But PG’s transformation was an adaptive problem. Although the direction was clear, specific solutions had to come from within the organisation. Instead of a single structural fix, multiple interconnected decisions had to be made by different teams. Additionally, there weren't direct industry benchmarks or large-scale models to follow. PG would be the first to implement this approach at its scale. Leadership shift: Assume change, justify inactionNo amount of analysis could provide the certainty the executive team was accustomed to. So, Gerrick*, the head of PG, and Giorgio*, who led seven affiliates, reframed the situation by posing this question: Why shouldn’t we change? The resulting shift in thinking was subtle, yet powerful. Rather than seeking comfort in more data, the executive team recognised that the real risk lay in maintaining the status quo. They embraced a new ambition: PG would become the very first large pharmaceutical firm to flatten its organisational design. The risk was worth taking as it would send a strong message to employees that leadership was serious about purpose-driven and empowered ways of working. PG could also scale the learning curve and shape the playbook for a new kind of organisation – one that could become a competitive advantage in itself. If your organisation is overthinking the leap, ask these questions: Are we trying to solve a technical problem or an adaptive challenge?Are we reducing risks or avoiding uncertainty? What are the risks of not changing?What are the benefits and costs of being the first to experiment with the transformation? What are the consequences of competitors successfully developing a proof-of-concept first?Trap #2: Waiting for a full roadmap The next challenge PG had to overcome was its desire for a perfect plan. In traditional organisational change, a detailed step-by-step roadmap is typically seen as essential. However, given the adaptive nature of the problem and inherent ambiguity in the transformation process, only the initial stages could be mapped out. Subsequent stages would have to evolve based on early actions and results.Leadership shift: Visualise the destination To move forward, PG’s executive team made a second important shift: letting go of the need for a perfect plan, which simply didn't exist, and acknowledging that a period of instability was inevitable.Rather than fixating on the structure, they visualised their ideal outcome: an organisation where people are empowered to make decisions with minimal approval, guided by a shared understanding of strategic priorities, decision-making criteria and operational boundaries.Focusing on the envisioned outcome not only made the executive team feel less anxious; it also energised them to move forward. They outlined critical success factors, including clarity when it came to roles and responsibilities, direction and communication. This created a collective confidence that the transformation could succeed even without a pre-defined plan.To build confidence amid uncertainty, consider the following questions:What should the future organisation look like? How should it function?If chaos is inevitable, how can we make it productive by reducing disruptions, staying agile and supporting each other when unexpected challenges arise? What key values, behaviours and commitments will help us navigate uncertainty, maintain momentum and ensure the organisation adapts effectively throughout the change? Trap #3: The control reflex The next challenge: What did it mean to “lead” a transformation aimed at decentralising decision-making? Given the vast scope of the transformation, the executive team couldn’t possibly deal with every challenge at once. Such a top-down approach would also contradict the very purpose of the change, which was to empower PG’s employees to design an organisation that truly worked for them. The answer was clear and unnerving at the same time ­– ownership of the transformation had to belong to employees.Leadership shift: Build the trust muscle The executive team wasn't giving up all control. It was responsible for the objectives, directions and process of the transformation, and remained accountable for its success or failure. However, self-organising teams – called “circles” – would own the analysis, recommendations, decisions and implementation.The executive team understood that their role wasn’t to dictate solutions but to support the process. They created an internal communication platform for employees to form circles, suggest projects and share progress updates. The system also supported the early detection of signs of failure, allowing for timely course corrections and careful interventions when necessary.Within weeks, energy surged throughout the company, and employees began to view the transformation as their own. As the executive team empowered the circles, trust deepened on both sides. With this came a greater willingness from the executive team to shoulder accountability while letting go of control.To prevent the control reflex and build the trust muscle, think through these questions:Can ownership of the tasks be separated from ownership of the process? Who should own each aspect? What would make you comfortable relinquishing control? How can you remain accountable for outcomes without directly executing the transformation? What systems will help you ensure strong information-sharing, monitor progress, detect weak signals of failure and make timely interventions – without taking back control? Trap #4: One foot in, one foot outThe executive team still found themselves in an uncertain position. They were responsible for the transformation’s success, yet lacked the traditional levers of authority. This discomfort could lead to the instinct to hedge – to keep one foot in, one foot out. Anticipating this risk, the executive team saw that for the transformation to succeed, they had to redefine their role. They identified two key responsibilities: safeguarding decision-making processes and ensuring teams had the resources to execute effectively; and facilitating coordination across circles and representing PG within the wider organisation. Leadership shift: Codify the shift The executive team made a public commitment, beginning with a name change: they would now be known as the network empowering team (NET). They established a team charter that outlined their new responsibilities to the organisation and to each other. This sent a message that leadership was no longer about command and control but about enabling the system to function. Soon, the NET faced its first real test. The finance circle decided to replace the traditional months-long budgeting cycle with AI-driven forecasting. Although this approach was innovative, Giorgio, who had to sign off on the numbers for the board, hesitated.Rather than override the circle, the NET collectively reviewed the process, asked clarifying questions and ensured rigour without undermining the team’s ownership. By following their formalised charter, the NET reinforced the transformation’s principles and set a precedent, changing their role from negotiating financial targets to providing resources to support execution. To ensure your leadership team fully embodies a new vision, ponder these questions: Have we clearly defined, formalised and communicated our new role in a way that differentiates it from the past? Are we consistently role-modelling and communicating behaviours that reinforce our new organisational design? How do we show our commitment to enabling vs. directing? How are we holding ourselves accountable to our new leadership approach?Successful change amid uncertaintyPG’s transformation wasn’t driven by a detailed roadmap, perfect plan or guarantee of success. Rather, it was spurred by a series of deliberate leadership shifts that empowered the executive team to move from hesitation to action.Their experience provides a striking lesson for any executive team confronted with a similar challenge: Waiting for certainty is a trap, and moving forward requires leaders who can recognise and overcome the common stumbling blocks that stall transformation. When leaders take the leap, they enable a system where employees aren’t just adapting to change – they are driving it.This is an adaptation of an article published in Harvard Business Review.*Pseudonym.]]></description>
                  <pubDate>Wed, 06 May 2026 01:30:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48496 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/leadership-organisations/leading-organisational-change-without-roadmap#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-03/shutterstock_1832128702.jpg?itok=d0j5p9FE" type="image/jpeg" length="1009560" /><dc:creator>Chengyi Lin</dc:creator><dc:creator>Michael Y. Lee</dc:creator></item><item><title>Will AI Eat SaaS for Lunch?</title>
                  <link>https://knowledge.insead.edu/economics-finance/will-ai-eat-saas-lunch</link>
                  <description> <![CDATA["Software is eating the world", venture capitalist and entrepreneur Marc Andreessen famously declared in 2011. The ensuing 15 years proved him prescient. In February 2026, a Substack article by Citrini Research grabbed headlines and triggered a market sell-off of SaaS (software-as-a-service) firms, wiping out nearly US$1 trillion in market value in a matter of days. Citrini’s central thesis? A cannibalistic last feast where AI eats the very software industry that’s been eating the world. The argument is simple: If anyone can prompt an LLM (large language model) and vibe code a custom enterprise resource planning or customer relationship management system in an afternoon, the multi-billion-dollar SaaS industry becomes a dinosaur overnight. It’s a frighteningly plausible thought that puts the spotlight on Citrini and the article’s author, James van Geelen, but it is fundamentally naive because it assumes that we live in a world without friction.Why firms won’t build all systems in-houseAI tools like Anthropic’s Claude are incredible at instantaneous prototyping. But as any software engineer knows, writing code is 10% of the job; maintaining, scaling and debugging it for edge cases is the other 90%. Software isn't just a static pile of logic; it’s a living organism. Production-strength software requires auditability, 24/7 reliability, API (application programming interface) stability and security compliance, all at once and all at scale. These aren’t things that LLMs, which are random systems by nature, can replicate. The “AI eats SaaS for lunch” logic naturally leads to the conclusion that all firms will build all their software in-house – because now, with the help of AI, they can. But can they really? Will they really? I bet they won’t for two reasons. For starters, a primary reason why companies buy enterprise software is to transfer risk. When a Fortune 500 company uses specialised software by a SaaS provider for cybersecurity or HR, they aren't just buying code; they're buying compliance with security frameworks, GDPR (General Data Protection Regulation) indemnity and ISO (International Organization for Standardization) certifications. If firms build systems in-house using general-purpose tools such as Claude or Google’s Gemini, what happens if (or when) things go wrong, such as data leaks? There will be no vendor to sue, no platform to blame and no security patch to purchase.Another reason relates to scale and interoperability. A third-party provider spreads the cost of high-level security and compliance across thousands of customers. An individual firm trying to replicate that in-house would find the "efficiency" of AI quickly eaten up by the massive overheads of self-certification and liability insurance.If all firms build all their systems in-house, we'll be back in the world of fragmented software with limited interoperability. Remember legacy systems? Firms will end up with isolated legacy piles of AI-written code that no one understands. Opportunities and challenges If the trend of building software in-house actually takes off and every company starts creating their own bespoke AI systems, the complexity of auditing those systems becomes exponential. Auditors would then become the most needed and sought-after profession on earth. If van Geelen really believes what he says, then he might consider auditing as a new profession to hedge against the apocalypse he hypothesised. Who knows, the auditing profession might be the answer to the problem of AI displacing jobs.Everyone can buy a shovel. Not everyone shovels their own snow. Summarising all the above, let’s not forget that AI is a general-purpose technology and software companies are specialists. It’s hard to argue that generalists will replace all specialists in the modern world, which is essentially what Citrini’s scenario argues. Everyone can buy a shovel. Not everyone shovels their own snow. What’s more, the "AI eats everything" narrative assumes that LLMs have access to all the world's intelligence. They don't. Besides every company’s proprietary data, there’s the paywall barrier, which keeps the most valuable data – highly structured, organised and predictable information required for professional-grade decisions – behind the moats of companies like LexisNexis, Thomson Reuters, Nielsen and the like. Without access to this information, generic models can’t generate deep insights; they risk simply recycling data that’s available in the public domain. Indeed, the owners of the data, not the owners of the models, hold the ultimate leverage.What will AI do to SaaS?SaaS won’t be eaten by AI, but it will be shaken and stirred by it. For instance, Block reduced its headcount by nearly half in February, culling over 4,000 positions. And in March, Atlassian, one of the SaaS companies hit hardest by the market sell-off, retrenched 10% of its staff.Gone is the cosy convention of per-user pricing and 5% annual price increases justified with the release of new features nobody asked for. Companies will need to be more discerning and can wield a credible threat to take business away from a SaaS provider (whether or not they follow through on it is another matter). This threat will inevitably shake up the SaaS industry. Those who can deliver measurable value will survive and thrive. Those who cannot will perish, and the industry will emerge leaner and stronger.A version of this article was published in The Business Times.]]></description>
                  <pubDate>Mon, 27 Apr 2026 01:00:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48491 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/economics-finance/will-ai-eat-saas-lunch#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-03/shutterstock_2210055765.jpg?itok=edLWRtz_" type="image/jpeg" length="879547" /><dc:creator>Lily Fang</dc:creator></item><item><title>INSEAD Insights: Secrets, Innovations and Alcohol</title>
                  <link>https://knowledge.insead.edu/economics-finance/insead-insights-secrets-innovations-and-alcohol</link>
                  <description> <![CDATA[The outsized influence of alcohol on financial markets in China and how legal protection for trade secrets spurs innovation are among our featured research this month. Other notable papers include an examination of the factors that influence an airline’s adoption of new innovations, and how a form of dementia impacts people’s impatience for rewards. How drinking “clubs” impact China’s financial marketsBusiness-related drinking culture is contributing to companies in China distorting or faking public financial information. That’s the surprising finding of a new study Massimo Massa and his co-authors published in the Journal of Financial and Quantitative Analysis. The researchers found that business leaders, auditors and even regulators who drink together create informal networks where everyone in the "club" protects one another, even if it hurts the public market. The evidence? Toxic alcohol scandals that shook up local drinking habits also significantly reduced firms’ distortion of their financial information in that location.Read the full paperHow radical innovations impact adoption Henrich Greve and his co-author looked at the speed of adoption of new technologies or products within the aviation sector. They discovered that the nature of the innovation was crucial, and that companies relied on different factors to help make those decisions. Specifically, adoption of simple upgrades, such as the Airbus A320neo (new engine/same plane), were based on cost advantages. For more radical technological or organisational innovations, such as investing in the new Boeing 787 or point-to-point flying, the experiences of early adopters and extensive trials were crucial before firms felt comfortable in adopting the innovation.Read the paperCan dementia make people more impatient?Individuals with a particular form of dementia are much more impatient than healthy adults when it comes to food or financial rewards, Hilke Plassmann and her co-authors found in a study published in Communications Biology. Using MRI scans, the team identified that the impatience was the result of atrophy in specific regions of the brain that typically help process emotional value and the ability to imagine future consequences. The findings show that reward impatience is a core feature of the disease and such behaviour could eventually serve as a marker for very early neurodegenerative risk. Read the paperThe benefits of keeping trade secretsWhile patents often dominate the conversation around intellectual property, trade secrets – which account for some US$5 trillion in value among US firms – are the unsung hero. A study published in Management Science by Aldona Kapacinskaite and her co-author provides rare, granular evidence on how firms manage these secrets in high-stakes environments. Analysing the US hydraulic fracturing industry before and after the 2016 Defend Trade Secrets Act (DTSA), the authors found that stronger legal protection did more than help firms hide information – they actively encouraged firms to deploy more novel, productive technologies that were previously deemed too "risky" to use in the field.Read the paper]]></description>
                  <pubDate>Tue, 24 Mar 2026 01:04:18 +0000</pubDate>
                  <guid isPermaLink="false"> 48486 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/economics-finance/insead-insights-secrets-innovations-and-alcohol#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-03/insightsmar_2026.jpg?itok=OvXz6AnQ" type="image/jpeg" length="989218" /><dc:creator>Lily Fang</dc:creator></item><item><title>The Leadership Blind Spots That Frustrate Executive Teams</title>
                  <link>https://knowledge.insead.edu/leadership-organisations/leadership-blind-spots-frustrate-executive-teams</link>
                  <description> <![CDATA[Over the years, I’ve observed that being in the upper echelons of leadership is rarely as orderly as it appears from the outside. On paper, everything looks rational and well managed. In reality, executive teams often operate in a state of simmering friction, shaped less by strategy than by how people relate to one another.It usually isn’t catastrophic decisions that derail leadership teams. More often, it is low-grade interpersonal antics that slowly grind things down. The passive-aggressive email or the meeting hijacked by ego. The eye roll that communicates more than a carefully prepared slide ever could. The human psyche is a peculiar machine, and nowhere is this more evident than in the conference room.Seen this way, leadership can feel like a surrealistic play. Everyone is properly dressed and everything looks composed – yet chaos reigns. Here are seven surprisingly common behaviour patterns among executives that can drive team members up the wall. 1. Always wanting to winSome executives treat every meeting like a high-stakes tennis match. Even when the topic is something as mundane as cafeteria menu options, they must win. Unsurprisingly, they can turn strategic retreats and brainstorming sessions into gladiatorial combat.A little competitiveness can be healthy. But when the desire to be right eclipses the desire to be effective, teamwork begins to erode. People quickly learn that offering new ideas is risky, because when they are challenged, it’s less about improving them than about winning.What makes this behaviour particularly draining is that it leaves little room for collective intelligence. When every exchange becomes a contest, people conserve energy rather than contribute. 2. Taking underserved credit Some executives collect credit the way others collect loyalty points. A team success becomes “something I was instrumental in”. A good idea from a colleague somehow reappears with their name attached.Closely related is the failure to recognise others. Credit-hogging and recognition-withholding are two sides of the same coin. Both steadily drain morale and trust. People stop speaking up, stop trying and eventually, they stop caring.The irony is that many leaders do not even realise they are doing it. Success happens and their name simply floats to the top of the slide deck. True leadership means letting others take the bow. The more generously you give credit, the more credibility you gain in return.3. Not taking personal responsibilityMistakes happen. They are part of leadership. But for some executives, they trigger deflection rather than reflection. “It wasn’t me,” they insist, often with remarkable conviction.Blaming others may offer short-term relief, but it mortgages long-term trust. In contrast, leaders who say “This one’s on me” create a sense of safety that strengthens the entire team. Unfortunately, many organisations are led by people with selective memory. Successes are claimed, but failures are redistributed, with a corrosive impact on culture.4. Being a “yes-butter”, not a “why-notter”“Yes, but…” is a velvet-gloved way of saying no. It creates the illusion of openness while subtly extinguishing new ideas. Presenting itself as wisdom, this behaviour is usually driven by fear.The “why-notters” are different. They do not accept ideas blindly, but they engage with them. They explore possibilities and remain curious. Organisations need fewer gatekeepers of gloom and more leaders willing to open the gate, even briefly, to see what might be possible.5. Withholding informationInformation is power, and some executives ration it carefully, deciding who needs to know what and when. In practice, this leaves people guessing and breeds mistrust.Transparency does not mean giving away secrets. It means trusting that colleagues are not plotting sabotage. In healthy teams, information flows freely. And in any case, people usually find out what has been withheld. The only question is whether they hear it from you or from the rumour mill.6. Not listeningYou can usually spot non-listeners in meetings. They are already preparing their response while others are still speaking, or nod thoughtfully while clearly thinking about lunch.Listening is not just about staying quiet. It is about being present and knowing how to ask the right questions. Leaders who truly listen are rare and deeply motivating. When people feel genuinely heard, they feel seen. And in leadership contexts, feeling seen builds commitment.Listening becomes especially difficult at senior levels because leaders are rewarded for having answers. The higher people rise, the less often they are interrupted, corrected or challenged. Over time, speaking can feel more productive than listening. Yet leadership presence is not measured by airtime. It is measured by the quality of attention leaders offer to those around them.7. Being inconsistentSome leaders are wonderfully unpredictable in the worst possible way. One day warm and encouraging, the next irritable and dismissive. As a result, teams spend more time anticipating mood swings than doing their jobs.Inconsistency, however, breeds paranoia. People learn to manage the leader instead of focusing on work. Consistency does not mean rigidity. It means behaving in ways others can rely on, even when conditions change. When leaders are predictable in this sense, they give others room to perform.The difficult art of leading humansIf this list feels uncomfortably familiar, congratulations. You are human. Most leaders recognise themselves in at least a few of these patterns. They rarely stem from bad intentions. More often, they grow out of habit, ego, fear or simply fatigue.The great paradox of leadership is that while executives invest enormous effort in managing markets, budgets and strategies, the hardest task is managing themselves. Even occasional emotional outbursts, when they occur, leave lasting impressions.Leadership is not about being perfect. It is about being willing to laugh at yourself, apologise when necessary and to listen more carefully. The best leaders are not those who never drive others crazy. They are the ones who realise it when they do and choose to do better. That, in the end, is what separates those who merely manage from those who truly lead.]]></description>
                  <pubDate>Mon, 23 Mar 2026 01:00:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48481 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/leadership-organisations/leadership-blind-spots-frustrate-executive-teams#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-03/executive_with_box_over_head_0.jpg?itok=Ya5WnCKY" type="image/jpeg" length="831256" /><dc:creator>Manfred F. R. Kets de Vries</dc:creator></item><item><title>Why Open Innovation Often Fails to Scale</title>
                  <link>https://knowledge.insead.edu/entrepreneurship/why-open-innovation-often-fails-scale</link>
                  <description> <![CDATA[Open innovation is no longer optional for organisations facing accelerating technological change. They must collaborate beyond their boundaries to access new capabilities and ideas. But while partnerships with start-ups and external innovators have become widespread, many initiatives still fail to deliver meaningful business impact.This was the focus of a recent webinar moderated by Vibha Gaba, The Berghmans Lhoist Chaired Professor of Entrepreneurial Leadership at INSEAD. In conversation with Giacomo Silvestri, executive chairman of Eniverse Ventures and group head of innovation ecosystems at Eni, and Benjamin N. Haddad, managing director of technology strategy at Accenture, she explored why the challenge is no longer finding innovation but integrating it.In industries undergoing rapid transformation, innovation has become an ecosystem activity, and companies can no longer rely solely on internal research and development. Yet, openness alone rarely delivers results. Many organisations still focus on launching pilots, accelerators or partnerships without building the structures required to scale promising solutions.How organisations can succeedAs Gaba highlighted, large organisations operate as complex systems. New technologies must fit into existing processes, data architectures and governance frameworks. Without clear ownership and alignment with business priorities, innovation efforts risk stalling in the so-called “POC death valley” – where proof-of-concept projects fail to transition into operational deployment.Artificial intelligence is raising the stakes. As AI tools make it easier to identify start-ups and emerging technologies, competitive advantage is shifting towards orchestration and execution. The organisations that succeed will be those that treat innovation as a disciplined business process, balancing experimentation with integration and long-term value creation.]]></description>
                  <pubDate>Mon, 06 Apr 2026 02:30:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48471 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/entrepreneurship/why-open-innovation-often-fails-scale#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-03/shutterstock_1887354130.jpg?itok=bJjOjEUz" type="image/jpeg" length="625694" /><dc:creator>INSEAD Knowledge</dc:creator></item><item><title>Getting Rich Quick May Not Guarantee Happiness</title>
                  <link>https://knowledge.insead.edu/career/getting-rich-quick-may-not-guarantee-happiness</link>
                  <description> <![CDATA[Many dream of living their best lives without financial worries. For some, this means hoping for a lottery win; however, a select few entrepreneurs achieve such a windfall through a successful exit from their start-up. After the challenges of building and scaling a business, they find themselves in the prime of their careers, enjoying financial freedom and with a lot of time on their hands.Although this might sound like bliss, it can bring unexpected emotional and existential challenges. Successful “exiters” can find themselves forced to transition from their old life to a new one, to find new directions and goals. Success brings enormous freedom, but greater ambiguity.This reality is drawn from interviews with entrepreneurs who successfully exited their start-ups, many of whom had participated in INSEAD’s Post-Exit Entrepreneurs Retreat. Examining how they deal with these challenges can offer valuable lessons for entrepreneurs getting ready for their own exit.Prepare for an emotional transitionAll the successful post-exit entrepreneurs we spoke to, many of them in their early 40s, experienced emotional turmoil. This manifested itself in several ways, including a profound sense of loss. For founders, their businesses are often deeply intertwined with their identity – years of time, effort and emotional investment make it difficult to separate the self from the venture. Letting go isn’t just a logistical or financial transition; it’s a psychological rupture. All of them described how disorienting it was to no longer lead something they had poured so much of themselves into.Beyond identity, there’s also the existential challenge of finding new meaning and purpose. One interviewee reflected, “Friends say, ‘You’re rich, what’s the issue?’ But for me, the real struggle now is meaning. Who am I without the business?” This quest, no longer defined by business metrics, becomes the less visible but no less demanding challenge of post-exit life. Some of our interviewees also experienced self-doubt. They questioned whether their success was down to skill or just luck. When some were unable to replicate their previous achievements, they began to question whether they really deserved their financial rewards.Connecting with like-minded people in a safe space to openly discuss your emotional struggles can help normalise these experiences – and build the support system you need to work through and overcome them.Reflect on your psychology of wealthFor many, a successful exit means freedom from financial pressure and the chance to create a legacy. While the technical aspects including wealth management, asset allocation, tax planning and so on typically receive considerable attention, the deep intra- and inter-personal work that comes with financial freedom is often overlooked. Understanding what we term the “psychology of wealth” involves exploring how money interacts with your sense of self and how it impacts your relationships.It means shifting your mindset from thinking about wealth as something to control or maximise to seeing it as a tool to create, maintain and renew relationships. This includes intimate relationships – such as with partners, children and close family – but also broader ties to community, society and social causes. Wealth becomes less about possessions and more about self-expansion and connection.Navigating the psychology of wealth involves reflecting on several deeply personal and sometimes uncomfortable questions, even if the answers may evolve over time. These questions range from “How much wealth is enough – and why?” to “What is the purpose of wealth” and “Why do I want to give back, and to whom?”The most resilient and fulfilling financial plans aren’t those crafted in isolation or alone with financial advisers. What’s needed are coherent wealth and philanthropy strategies that serve a larger purpose and legacy, grounded in honest conversations with yourself, loved ones and the relevant communities. They also need to be reviewed on a regular basis and should evolve to reflect your changing values and needs. Redefine success on your own terms To overcome the challenges that may come with achieving financial freedom, you need to redefine success on your own terms. This doesn’t have to be about building another business but can be about finding fulfilment in new and unexpected ways.Our interviewees generally took one of four paths as they searched for what really mattered to them. Some returned to entrepreneurship, either as serial founders or investors. One of our interviewees, Timothy (all names used in this article are pseudonyms), started another business with new partners. His desire to be involved in a new venture revealed a personal quest to affirm and revalidate his abilities.Others looked to maintain their sense of self by staying involved in the entrepreneurial ecosystem as advisers or mentors. Still others used their new-found freedom to make a social impact. Another interviewee, Clement, saw philanthropic efforts as a natural evolution of his entrepreneurial drive – still “building”, but now in service of social or environmental goals. He pursued climate and energy studies and palliative care training, and explored opportunities in local politics, climate-focused non-profits and philanthropic investments.Finally, some decided to prioritise personal interests and relationships – areas they may have previously neglected during their entrepreneurial years. To Rohan, for instance, his personal wellness and family are now his biggest priority. He chose to focus on being a full-time father and now measures success in terms of relationships and self-care. There are no templates to follow – the key is to define success in this era of life on your own terms. Give yourself permission to explore and discover which activities make you feel like you’re spending your time and money well and are utilising your talents in the most meaningful way. A word of caution: Be intentional about your time. Avoid diving into an all-consuming project unless it aligns with your priorities and remember that the first path you take may not be the one you stick with. For example, Rohan, although mostly content, did admit that being a full-time father can trigger feelings of inadequacy. That is normal and acceptable. In fact, he is already beginning to explore other paths.Beyond successful entrepreneurs, the challenges and potential paths we’ve outlined apply to other professionals who have achieved financial freedom and are looking to pursue something new. Importantly, you must be prepared to handle the emotional and existential challenges that this transition might present. Support from like-minded people facing similar issues can help you negotiate the process and navigate your newfound freedom with greater clarity and purpose.]]></description>
                  <pubDate>Tue, 21 Apr 2026 01:00:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48466 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/career/getting-rich-quick-may-not-guarantee-happiness#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-03/shutterstock_2446813659.jpg?itok=ws3pGi_g" type="image/jpeg" length="952912" /><dc:creator>Winnie Jiang</dc:creator><dc:creator>Balagopal Vissa</dc:creator></item><item><title>How On‑Demand AI Assistance Undermines Learning</title>
                  <link>https://knowledge.insead.edu/responsibility/how-demand-ai-assistance-undermines-learning</link>
                  <description> <![CDATA[Imagine two students training with the same AI tutor. Both receive tips at key moments, but one can additionally request help whenever they want. Who learns more? In theory, giving students greater agency should increase engagement and active participation. However, in practice, without adequate self-regulation, students may become over-reliant on AI assistance – which could ultimately undermine learning.As AI tutoring tools provide personalised, on-demand support at an unprecedented scale, this has become one of the most fundamental questions in management education, employee training and student learning. I studied this with Hamsa Bastani from The Wharton School and Osbert Bastani from the University of Pennsylvania. We recruited over 200 chess students for a 12-week intensive AI-enabled training programme. Students were randomly assigned to either a system-regulated condition (in which the platform automatically provided AI tips at key moments) or a self-regulated condition (in which students could additionally request help at any time by clicking a button). The only difference between groups was access to this button.After 12 weeks, those who could request AI help at any time improved their performance by just 30 percent, compared to 64 percent for students in the system-regulated group. Students with on-demand AI access learned less than half as much, a remarkably large effect for such a subtle difference in system design. Even more striking, these students were fully aware of their over-reliance on AI yet continued to increase their use of it over time.These findings matter far beyond chess. As AI tools proliferate across schools and workplaces, from coding assistants to medical decision-support systems, users have unprecedented control over when they receive help. Our research suggests that even small differences in system design can have dramatic consequences for long-term human skill atrophy.Why self-regulated AI use hinders learningWhile some negative effect from the excessive use of AI assistance is expected, the magnitude demands explanation. Our analysis points to two potential mechanisms that amplified learning loss.First, on-demand AI assistance short-circuited productive struggle. Students in the self-regulated condition performed significantly better during training games, but this initial success came at the expense of long-term learning. When tested without AI assistance, they performed substantially worse than their counterparts who had struggled through difficult challenges on their own.The damage wasn't uniform across all types of assistance. Learning losses were driven specifically by students requesting assistance on problems within their “zone of proximal development” – problems that were challenging but feasible for their skill level. These are precisely the problems for which struggle, error and targeted feedback produce the greatest learning gains. By ​​bypassing this process with on-demand solutions, students deprived themselves of the very experiences that build expertise. Interestingly, requesting help on trivial problems (below their skill level) or highly complex ones (far beyond their abilities) had little impact on learning. The damage occurred specifically when AI assistance displaced productive struggle on appropriately challenging problems.Second, self-regulation reduced overall engagement. Students with on-demand access completed 24 percent fewer training games than their system-regulated peers. Our post-study surveys revealed why: Many reported that clicking the help button diminished their sense of accomplishment and made training feel less rewarding.Yet, despite this awareness, these same students increasingly relied on AI over time. In the first week of training, they requested help approximately five times per game. By week 12, this had more than doubled to 11 requests per game. Meanwhile, students in the system-regulated condition steadily improved, eventually closing the performance gap with their self-regulated peers.This pattern reveals a classic failure of self-regulation: short-term convenience overrides long-term goals, even when the trade-off is fully understood. Our chess students weren't naive about AI's risks; they were caught in an agency trap where immediate ease consistently won over future learning.When motivation matters (and when it doesn't)We explored whether student skill or motivation might moderate these effects. Common wisdom suggests that more skilled or motivated learners would be better equipped to self-regulate their use of AI assistance. Our findings tell a more nuanced story.Highly motivated students, those who reported spending more hours per week on chess prior to the study, experienced substantially smaller learning losses from self-regulated AI access. However, even among the most motivated students, on-demand assistance still reduced learning compared to system-regulated assistance. Skill or expertise, however, offers no such protection. Beginners and advanced players both fell into the over-reliance trap. This finding contradicts the prevailing view that expertise enables better self-regulated learning.The design implications are clear. Organisations deploying AI‑assisted learning and training systems should:Resist the urge to give users unlimited control. The intuition that more choice enables better learning can be wrong. Educational AI systems should algorithmically determine when to help based on what best supports learning by targeting moments when assistance accelerates learning rather than displaces it.Recognise that user awareness is not enough. Our students knew they were over-relying on AI but couldn’t help themselves. System design must account for the gap between intentions and behaviour. Don’t expect users to self-regulate effectively, even when they understand the risks.Consider the types of assistance provided. Our research suggests that what we call attention signals – alerts that flag important decisions without prescribing solutions – can encourage engagement without triggering over-reliance. These signals prompt learners to slow down and think carefully at critical moments while preserving the productive struggle essential for learning.Monitor engagement metrics alongside performance. In our study, students with on-demand AI access didn't just learn less, they also practiced less. Reduced engagement may be an early warning sign that assistance is undermining rather than supporting learning. Besides tracking when users are improving during training, track whether they remain motivated to train at all.Using AI doesn’t inevitably lead to skill atrophy. The risk arises specifically when assistance displaces productive struggle on appropriately challenging problems. By limiting AI assistance there, but allowing it freely elsewhere, productivity gains need not come at the cost of skill development.Beyond the classroomAI assistance, whether in software engineering or medicine, presents a double-edged sword for skill development. While it can help streamline workflows and free up cognitive resources, an over-reliance on AI for complex problem-solving is detrimental. If junior staffers habitually defer to AI-generated solutions for challenging cases, they risk failing to cultivate the fundamental reasoning skills required when these systems encounter unexpected problems or fail.As we integrate AI more deeply into education and training, we must design these systems with their long-term effects on human capability in mind. The chess students in our study were diligent learners with strong motivation to improve, training in a domain where AI assistance has been available for decades. If even these learners fall into the trap of over-reliance, the risks are likely far greater in contexts where motivation is lower, or users are less aware of AI's limitations.]]></description>
                  <pubDate>Tue, 17 Mar 2026 01:00:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48451 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/responsibility/how-demand-ai-assistance-undermines-learning#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-03/shutterstock_2547509993.jpg?itok=tuwEkXFa" type="image/jpeg" length="818956" /><dc:creator>Stefanos Poulidis</dc:creator><dc:creator>Hamsa Bastani </dc:creator><dc:creator>Osbert Bastani</dc:creator></item><item><title>What Happens When You Actually Listen to Customers</title>
                  <link>https://knowledge.insead.edu/marketing/what-happens-when-you-actually-listen-customers</link>
                  <description> <![CDATA[When did you last ask your customers what they thought of your business, and then actually made changes based on what they said? A field experiment my colleagues* and I conducted with hundreds of small businesses in Rwanda produced some findings worth paying attention to. We found that systematically seeking customer feedback from just a small subset of customers and acting on it increased monthly revenues by 62 percent and profits by 54.5 percent. What's interesting is how acting on feedback has an impact beyond the customers who provide it, and what that means for how businesses should engage with customers.It's the learning that matters mostFor our study, published in the Marketing Science journal, we ran a field experiment with 274 business recruited across retail and service sectors in the greater Kigali region of Rwanda. Our year-long experiment was deliberately low-tech and scalable, designed to be something small businesses could realistically sustain.First, we provided each firm with a smartphone and a basic app to collect customer contact details. The firms were divided into two groups: treatment and control. The treatment group was given templates to collect customer feedback. The template had just two questions: how customers rated their purchase experience and what they'd suggest for improvement. These business owners were asked to seek feedback from a randomly-selected subset of their customers. The control group of firms received the smartphone and app to collect customer contact details, but no feedback templates or prompts for seeking feedback from any of their customers. The results uncovered two ways customer feedback affects business performance. The first is straightforward. When you ask customers for their opinion, they feel valued and remember your business better, and are therefore more likely to come back. This "solicitation effect", as we call it, is substantial: Customers who were asked for feedback showed a 19.9-percent higher heavy-aided (i.e. respondents are given multiple identifying details about a firm to see if they can remember it) recall rate and spent 27 percent more than the untapped customers of the same firm.The second mechanism is less obvious but more important. When firms made changes based on the feedback, such as tweaking their product line-up, opening hours and order delivery as suggested, everyone benefits. This is a win for the firm. In fact, in our study, changes made by feedback-seeking firms had a significant impact on the untapped customers who were never asked for their feedback. These customers showed 38.2-percent higher recall rate and spent 77.4 percent more compared to customers of the control firms. We call this the “learning effect”. This suggests something important about the value of customer feedback. Most businesses approach it as a relationship management tool, which isn’t wrong, but it misses the bigger opportunity: discovering what's lacking or even broken in your business and fixing it.Small effort, big impactOur study focused on small and medium-sized businesses in Rwanda that had been operating for roughly a decade. And yet putting a proper feedback system in place still reaped substantial revenue and profit improvements. This suggests that most of us probably understand our customers less well than we think we do. Our study uncovered another counterintuitive aspect of feedback-seeking. We randomised the number of customers each firm should contact – 70 percent or 30 percent. Firms that sought feedback from 70 percent of their customers didn't perform significantly better than those who surveyed only 30 percent. While both groups substantially outperformed businesses without feedback systems, more feedback didn't mean much better results. For these small businesses, getting monthly feedback from 10 to 12 customers on average was enough to identify areas for improvement.This matters for how businesses design feedback programmes. The instinct is often to maximise coverage by surveying as many customers as possible, collect as much data as possible, and measure response rates. But if the goal is learning and improvement, a smaller random sample appears to work just as well.Back to basicsThe real takeaway from our study is about how much room for improvement exists in most businesses, even established ones.For entrepreneurs and business strategists, before optimising your operations or refining your positioning, ask yourself: Do you know exactly what your customers want? Have you asked them directly? And have you done anything with the answers?For owners and executives of established businesses, the question is similar. If your firm has been operating for years without a systematic way to gather and act on customer feedback, or has feedback processes whose findings go unheeded, what might you be missing?Based on our research, quite a lot. Sometimes the most valuable business insights merely requires companies to go back to the basics, which many mistakenly forsake for complicated marketing or promotions. What your customers want may just be your listening ear and follow-up action. Before trying anything fancier, start here.*Stephen J. Anderson, Texas A&M University; Pradeep K. Chintagunta, University of Chicago; and Naufel Vilcassim, London School of Economics and Political Science.]]></description>
                  <pubDate>Thu, 19 Mar 2026 03:33:33 +0000</pubDate>
                  <guid isPermaLink="false"> 48446 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/marketing/what-happens-when-you-actually-listen-customers#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-03/shutterstock_2369503599_1.jpg?itok=-PGkOrcq" type="image/jpeg" length="165095" /><dc:creator>Rupali Kaul</dc:creator></item><item><title>How Stigma Affects Male Friendships</title>
                  <link>https://knowledge.insead.edu/marketing/how-stigma-affects-male-friendships</link>
                  <description> <![CDATA[Much has been written about the “male loneliness epidemic” – a rise in social isolation and emotional disconnection among men. In the United States, the share of men without close friendships increased from 3 to 15 percent between 1990 and 2021. Men also tend to have less intimate and supportive friendships with other men than women have with other women.Research by INSEAD PhD student Sherrie Xue and Assistant Professor of Marketing and The Cornelius Grupp Fellow in Digital Analytics for Consumer Behaviour Stephanie Lin, along with Chris du Plessis from Singapore Management University, suggests that men tend to pass up certain opportunities to connect platonically with other men due to pressure to conform to heterosexual norms. Even if they actually want to watch a movie or go out to dinner with a friend of the same gender, some men will avoid doing so if the activity could be perceived to have romantic undertones.Why it mattersBe it in a professional or personal context, shared experiences form the bedrock of social connections. When men avoid partaking in certain activities with other men, it might affect the quality and closeness of these relationships and contribute to their loneliness. This could, in turn, negatively impact those around them. Such behaviour may also reinforce heterosexual norms, potentially upholding a gendered power structure dominated by stereotypically masculine men that marginalises women, gender-nonconforming men and the broader queer community.The studyThe researchers conducted five studies and one supplementary study involving over 3,200 participants in the US, the United Kingdom and Singapore. Studies compared same-gender experience-sharing between pairs of men and women. Scenarios included consuming a drink from the same container, watching romantic and non-romantic film clips together, and ordering two different dishes to share.The takeawayThe researchers found that men generally avoided shared activities with same-gender friends more than women did, especially when it came to experiences with romantic connotations, such as sharing food or watching a romantic movie like The Notebook. Certain male participants even made suboptimal decisions (i.e. forgoing a cash reward or their preferred choice) to avoid these activities. This was linked to their discomfort with implied romance rather than concerns of appearing feminine. In one study, men indicated that social constraints – not their personal preferences – were the primary reason why they chose not to engage in a shared experience with a friend of the same gender. Societal attitudes towards gay people may have evolved, but the cultural pressure to behave in an unambiguously heterosexual manner can still shape some men’s behaviour within their friendships. A shift in these standards could help improve both individual and societal well-being.]]></description>
                  <pubDate>Wed, 29 Apr 2026 04:00:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48441 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/marketing/how-stigma-affects-male-friendships#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-03/shutterstock_1493383928.jpg?itok=rIYvd0XD" type="image/jpeg" length="1026992" /><dc:creator>INSEAD Knowledge</dc:creator></item><item><title>How GenAI Is Driving Personalised Marketing in the UAE</title>
                  <link>https://knowledge.insead.edu/marketing/how-genai-driving-personalised-marketing-uae</link>
                  <description> <![CDATA[The United Arab Emirates is one of the most complex markets for those seeking scale and effectiveness. Among the roughly 200 nationalities that make up the resident population, expatriates account for around 85 percent, staying for an average of just 4.4 years. This fluid demographic, coupled with pronounced linguistic and cultural diversity, makes it difficult to rely on traditional customer segmentation or historical purchase data.Against this backdrop, the ability to hyper-personalise products and services has become a key competitive differentiator – which is where generative AI (GenAI) comes in. The UAE’s appetite for innovation, forward-looking regulation and flexible data protection laws has made it one of the fastest adopters of the technology. The majority of the population use GenAI tools regularly, and consumers show a relatively high willingness to share their data with companies in exchange for personalisation and loyalty benefits. For example, a study on grocery retail trends by consulting firm Oliver Wyman found that UAE-based consumers were consistently more open to using AI tools than their peers in the United States. A total of 71 percent of UAE respondents were interested in customised promotional offers (vs. 56 percent of US respondents), and 55 percent expressed interest in enhanced online customer service chatbots (vs. 17 percent of US respondents).In this article, we build on insights from our recent panel discussion at INSEAD’s Middle East Campus on hyper-personalisation in marketing, exploring how marketers in the UAE are using GenAI to provide individualised experiences to its diverse and dynamic population. The UAE’s unique need for personalisationTried and tested segmentation variables, such as past behaviour, age, life stage and income level, offer limited predictive power when your customer base is inherently fluid and turns over every four or so years. This challenges the very foundations of established personalisation practices, requiring agility and new approaches.GenAI provides a way forward. By ingesting and processing multilingual content, cultural nuances and real-time behavioural signals simultaneously, it enables contextual personalisation at a scale, speed and cost-effectiveness that traditional methods can’t match. UAE customers increasingly expect this level of tailored engagement; a survey found that 67 percent of respondents wanted businesses to remember their previous shopping experiences and preferences to tailor their browsing journeys. The rise of predictive and culturally aware AIBusiness in the UAE have moved quickly to put these abilities to work. For instance, Abu Dhabi-based technology company e& (formerly Etisalat) announced a partnership with Amazon Web Services to deploy GenAI technologies that generate real-time personalised recommendations across its ecosystem. The technology analyses customer preferences to suggest tailored products across multiple touchpoints, well beyond what traditional algorithms could achieve.Emirates NBD (ENBD), one of the UAE’s leading banks with over 9 million customers and 35,000 employees across 13 countries, began its transformation even earlier. As far back as 2021, before GenAI gained global momentum, it set out to become an AI-driven organisation, focusing on internal capability-building, cultivating AI-native talent and unlocking new growth streams. Today, ENBD uses predictive GenAI to deliver personalised customer experiences at scale, including advising first-time retail investors on tailored investment solutions and recommending appropriate spending strategies in real-time. The bank aims to generate a five- to seven-times return on its AI investment through data-driven initiatives.What makes this a genuine strategic shift, and not an incremental upgrade, is the move from reactive to anticipatory personalisation. Rather than inferring future preferences from consumers’ past behaviour, GenAI continuously processes streams of unstructured, near-real-time data – including social activity, voice interactions and browsing patterns – to anticipate customer needs at the right moment, and with the right context.GenAI also enables personalisation grounded in cultural intelligence, which is especially critical across the UAE and broader Middle East. In a multicultural society where religious and cultural calendars shape consumer behaviour, AI-powered adaptability marks a meaningful evolution: personalisation that’s both precise and culturally appropriate, reflecting local sensitivities.Challenges and considerationsEven in a progressive, innovation-first market like the UAE, this degree of personalisation faces real obstacles. Below are five hurdles for marketers to overcome:Algorithmic bias: AI must avoid discriminating based on nationality, religion or cultural background. This requires diverse development teams, clear compliance guardrails and strong ethical guidelines.Data privacy: Data protection regulations require businesses to balance personalisation ambitions with respect for individual privacy rights.Date sovereignty: Frequent cross-border travel patterns within the region make it difficult for marketers and technologists to navigate different countries’ respective data regulations. Cultural evolution: Demographics and cultural diversity add complexity to GenAI training, as AI models must continuously learn new inputs and preferences.Integration complexity: Legacy systems and traditional marketing tools can slow down the adoption of GenAI platforms.We explored these tensions at our panel discussion, joined by industry experts Marie de Ducla from Google and Joe Abi Akl (MBA'11D) from Oliver Wyman. Both underscored potential personalisation risks, including the personalisation paradox: consumers increasingly expect relevance, but excessive personalisation can backfire if it feels intrusive or limits serendipitous discovery. There’s also the risk of a “sea of sameness” – if every marketer deploys AI-generated personalisation, how does anyone stand out?De Ducla pointed to Etihad Airways’ Black Friday campaign as a compelling answer. The airline used AI-driven insights to match consumers’ purchase interests to relevant travel destinations (e.g. suggesting a trip to Paris for someone interested in buying a Louis Vuitton bag). This campaign turned shoppers into passengers, resulting in one of the airline’s best digital sales days on record and demonstrating that personalisation can enhance creativity rather than constrain it.Indeed, the commercial case is becoming harder to ignore. A recent study showed that retailers experimenting with AI-powered personalisation are seeing a 10- to 25-percent increase in return on ad spend for targeted campaigns.The path forwardThe UAE’s experience with AI-driven personalisation offers a blueprint for marketers in other culturally diverse, economically dynamic markets. Organisations that navigate cultural complexity, address privacy concerns and take a clear-eyed view of regulation will be best placed to compete – and the lessons being learned in the UAE today can be used to shape personalisation strategies across emerging markets tomorrow.GenAI-enabled personalisation represents a real shift in how businesses can serve sophisticated, globally connected consumers. For the UAE, it’s an opportunity to lead the next phase of digital transformation while honouring the cultural legacy that makes the country distinctive.The authors would like to thank Marie de Ducla, Ads – Sales Lead at Google, and Joe Abi Akl (MBA'11D), Partner and IMEA Retail & Consumer Practice Head at Oliver Wyman, for their invaluable contributions to our panel discussion. ]]></description>
                  <pubDate>Tue, 07 Apr 2026 02:05:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48436 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/marketing/how-genai-driving-personalised-marketing-uae#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-03/shutterstock_2400964429.jpg?itok=EyU9tH9s" type="image/jpeg" length="929121" /><dc:creator>Danil Starobogatov</dc:creator><dc:creator>Wolfgang Ulaga</dc:creator></item><item><title>What’s Different About Working in Tech?</title>
                  <link>https://knowledge.insead.edu/leadership-organisations/whats-different-about-working-tech</link>
                  <description> <![CDATA[The tech sector has long outgrown its status as just another industry vertical. Today, the sector – made up of companies developing software and related products – is a foundational force reshaping economies, societies and even the nature of organisations themselves. This statement was already true before the generative AI (GenAI) revolution – and is even more so now.  The sector appears to operate on a different kind of “business physics”, driven by near-zero marginal costs, rapid experimentation and a shift away from command-and-control hierarchies. For managers looking to lead in this space, the challenge is mastering these unique dynamics, which render the old rules of management less relevant.  Move fastTech start-ups today need little more than a laptop, cloud credits and a brilliant idea to take off. Moreover, new ideas can be prototyped quickly, tested with real users and continuously refined at high speed. The low barriers to entry, combined with the speed of experimentation, translate into higher competition and faster idea cycles. As strategic horizons in tech shrink, leaders must be comfortable operating at high velocity and extreme uncertainty, focusing on first-mover advantage and shorter planning cycles.OpenAI, Anthropic and Google are no strangers to strategic one-upmanship, frequently timing their launches within days or even hours of one another. For instance, OpenAI launched GPT-4o on the Monday of Google’s annual product launch event in May 2024. Then there’s extreme efficiency. In late 2024, DeepSeek surprised the world by disrupting a mostly American-centric GenAI race with their V3 model, which cost a mere US$6 million to train compared to hundreds of millions (or even billions) spent by American tech giants. This 100 times difference in capital efficiency calls into question the need for massive capital and talent hoards to win the race.At the same time, it pays to know your tech. The industry has a tendency for massive hype cycles. New technologies such as GenAI, Web3 and the metaverse tend to attract immense investment and talent, but the narratives around them can sometimes obscure reality and polarise discourse. More importantly, hype can create pressure-cooker conditions in tech companies, where the fear of missing out often dictates strategy. While trying to keep pace with the competition, leaders must discern real innovation from overhyped fads. “Move fast and break things” may not always be the best way forward.Race for talentIn an industry defined by rapid boom-bust cycles, tech companies are continually restructuring to meet investor expectations, resulting in periodic, seemingly coordinated layoff waves. And when global talent flows quickly between companies, roles and industries, managing talent is not just about attracting top performers, but also about maintaining morale and engagement in a world where job security is seen as fluid.Interestingly, although tech professionals are some of the world's highest earners, it’s not only the money that draws them, but also purpose, autonomy and mastery. Many are intrinsically motivated by a passion for solving complex problems and a desire to make an impact. When experimentation is cheap and talent is intrinsically driven, the manager’s role shifts from assigning tasks to architecting autonomy: curating the tools, culture and guardrails that allow autonomous teams to self-organise and innovate. Many tech companies have made autonomy and decentralisation a central theme, like GitLab’s open-handbook culture and asynchronous decision-making, as well as Valve’s self-managing teams. Decentralised autonomous organisations go one step further, using blockchain to govern operations without central leadership.This combination of high skill, high pay and high autonomy has birthed a modern “labour aristocracy” – a class of professionals whose specialised expertise grants them rare leverage over their employers. These professionals view their roles as core to their identity and treat their companies as moral proxies, making their organisations a fertile environment for internal activism. The Netflix employee walkouts in 2021 over the anti-transgender content of Dave Chappelle’s comedy special is just one high-profile example.Putting in place an impact-driven mission, such as Airbnb’s quest to "create a world where anyone can belong anywhere" or Etsy’s ethos to “keep commerce human", can appeal to these professionals and align decentralised teams around shared values.Navigate ethical and regulatory frontiers The same speed and scale that allow tech to reshape the world is pushing society into uncharted territory – beyond technical frontiers to ethical ones. Tech innovation is constantly outpacing our collective understanding, with breakthroughs in AI, big data and social platforms forcing us to confront novel dilemmas. As new technologies shape human behaviour and societal norms, they’re creating a new class of ethical considerations, namely: autonomy, fairness and democracy.  In a world where data fuels the digital economy, what rights do individuals have? The rise of so-called "surveillance capitalism" triggers debates over privacy and manipulation. Algorithmic fairness is another concern when decisions are increasingly made by AI – from loan applications to medical diagnoses. To avoid bias in algorithms, care must be taken to prevent historical biases from being encoded into AI systems. At the larger, societal level, social media platforms such as Meta have been in the spotlight for their role in amplifying polarising content through their algorithms. As they embrace the ideal of open expression, they have the ethical duty to balance the risk of misinformation at scale, which can polarise societies and hinder democratic processes. Regulators certainly have a role in keeping tech companies in line. But while technology evolves at an exponential pace, legislative processes are, by design, deliberative and linear. As a result, tech companies not only need to deal with the uncertainty of regulators catching up with technology, but also the complexity of navigating a patchwork of rules across geographies: from the United States’ "permissionless innovation" to the European Union’s proactive, risk-oriented regulations (like the AI Act). Then there are major players like India and China, which focus on digital sovereignty through assertive policies such as data localisation requirements to ensure national control over their growing digital economy.Tech companies can also expect heightened uncertainty as the technology reaches a public tipping point. Even if they have been operating in environments with long periods of minimal oversight, the landscape can suddenly become unpredictable beyond this tipping point, in what is known as a “regulatory whiplash”.What are the implications? From both a strategic and ethical standpoint, while regulations may take time to catch up, leaders cannot be passive players. They can better manage uncertainty by being proactive: through self-regulation (by embedding ethical guardrails), building trust with users and policymakers, as well as investing in proactive compliance and policy engagement.Be ever readyThe tech industry is characterised by breathtaking speed and scale, thanks to the low barriers, global talent and autonomous organisations. But the landscape is “breathtaking” only if leaders are ready for the realities of the industry. They can be prepared if they: Understand how to embrace decentralisation while ensuring alignment and accountabilityKnow enough about the technology to discern real innovation from overhyped fadsBe able to define and articulate a clear ethical stanceNavigate a complex, fast-evolving regulatory landscapeManage talent in a world where intrinsic motivation and continuous learning are central The dynamics of managing in tech companies are beginning to spill over into other industries as they adopt tools such as digital twins and simulation, and engage in continuous technology integration. AI is now enabling these, even in traditionally capital-expenditure-heavy or service-intensive industries. That means it pays for managers in all sectors to be aware of what makes working in tech different and to recalibrate what it takes to lead in a tech-infused world.     ]]></description>
                  <pubDate>Thu, 02 Apr 2026 01:00:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48426 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/leadership-organisations/whats-different-about-working-tech#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-03/speed_of_tech.jpg?itok=WJsWF9WO" type="image/jpeg" length="684089" /><dc:creator>Martin Gonzalez</dc:creator><dc:creator>Phanish Puranam</dc:creator></item><item><title>The Courage to Tackle Fear as a Leader</title>
                  <link>https://knowledge.insead.edu/leadership-organisations/courage-tackle-fear-leader</link>
                  <description> <![CDATA[Fear is inevitable in leadership. The weight of responsibility for an organisation’s performance and the livelihood of its people, especially in times when the next crisis is never too far away, can feel overwhelming. The question is how leaders should respond to fear.Together with Heidi K. Gardner of Harvard Law School, I hosted a discussion on the topic at the recent World Economic Forum Annual Meeting in Davos. During a lively exchange with executives from diverse sectors, I made the case that tackling fear requires building two essential foundations: a cohesive organisation and a moral compass.Building cohesion through diversityWhen times get tough, our instincts often betray us. The common response to uncertainty is often to surround ourselves with faces and perspectives that mirror our own. It’s tempting to seek consensus and eliminate friction by working only with people who think like ourselves.But this instinct can lead to our downfall.The world is diverse, complex and multifaceted. If we don't tap into the diversity within our organisations, blind spots may emerge. We may miss trade-offs we should be considering or fail to understand segments of our market, our stakeholders or our own teams. No doubt embracing diversity can be uncomfortable, since it means facing perspectives that challenge our assumptions and slow down our decision-making. But it’s also the organisational equivalent of stress-testing our ideas before we implement them.A cohesive organisation is one where diversity is embraced. It is also where people with different perspectives are united by something deeper: a shared sense of purpose.This brings me to the other glue of organisational cohesion. When people see the meaning of their work, the firm’s performance also improves. Take for example a recent study of a consumer goods multinational where some 3,000 employees were invited to workshops about identifying personal meaning in their work. Over two years, the firm’s internal rate of return improved as low performers either left or improved their performance. When people feel their work has purpose, they're more resilient in the face of uncertainty. They can weather the storms – and stay motivated – because they understand what they do matters.Trust mattersOf course, cohesion requires trust. You cannot have a united organisation, especially one that embraces productive disagreement, without deep trust among and between employees and leadership.Research shows that trust is built on two pillars: performance and integrity. Delivering results is important, yes, but you must also demonstrate consistent ethical behaviour, because it’s the latter that will sustain trust through crises, when results inevitably suffer. A moral compass: principles over performanceEconomics can teach us much about building organisations, but it has less to offer when we ask deeper questions about leadership: What guides us when the path forward is shrouded in fog? What keeps stakeholders by our side when we stumble or are laid low by events beyond our control?Tackling fear requires building two essential foundations: a cohesive organisation and a moral compass. This is where I turn to philosophy. Immanuel Kant wrote, "Act only according to that maxim whereby you can at the same time will that it should become a universal law." To the German philosopher, it’s imperative to identify the principles guiding our actions and apply them consistently.Your stakeholders – employees, investors, customers and communities – need to understand not just what you do, but why you do it. They need to know the principles that guide your decisions. And critically, they need to see you abide by those principles even when circumstances change, when it's inconvenient and when a different path might seem more expedient.This transparency of principles creates predictability. When people understand your framework for decision-making, they can trust you through good times and bad. When unforeseen events lead to disappointing outcomes, they can see that you remained true to your stated values. This consistency is what transforms a competent manager into a memorable leader.Yes, performance maximisation must remain one of your guiding principles. But I believe truly memorable leadership requires going beyond this. It requires articulating a set of values that encompasses how we treat people, how we engage with our communities, how we steward resources, and what we're willing to sacrifice.What will you be remembered for?Every leader should ask themselves: What do you want your legacy to be? What do you want to be remembered for? If one is remembered only for hitting quarterly targets or maximising shareholder value, I’d argue they have fallen short of their potential to be a great leader. Fear in leadership often stems from focusing on outcomes we cannot fully control. Markets will fluctuate, crises will erupt, or our best efforts may fall flat. But we can stay true to our principles. We can control whether we build organisations that embrace diversity and foster purpose. We can control whether we act with integrity. Because as leaders, these matter more than fear itself.]]></description>
                  <pubDate>Mon, 16 Mar 2026 02:10:30 +0000</pubDate>
                  <guid isPermaLink="false"> 48416 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/leadership-organisations/courage-tackle-fear-leader#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-03/shutterstock_1855925743_1.jpg?itok=mPSr_gHD" type="image/jpeg" length="152420" /><dc:creator>Alexandra Roulet</dc:creator></item><item><title>Four Mindsets That Undermine Workplace Well-Being Initiatives</title>
                  <link>https://knowledge.insead.edu/career/four-mindsets-undermine-workplace-well-being-initiatives</link>
                  <description> <![CDATA[Well-being – be it physical, mental, emotional or otherwise – is essential for a strong and productive workforce. According to a McKinsey Health Institute report, employers increasingly recognise that employee health and well-being are core to performance. Yet, many organisations struggle to find the right approach when it comes to implementing solutions that sustain both well-being and output.For over a decade, I’ve worked with multinational organisations to design and deploy well-being and mental health programmes. Based on these experiences, I’ve found that despite their best intentions, many leaders get tripped up by a few common mindsets. That’s not to say these perspectives are inherently flawed. It only becomes a problem when they are the sole lens through which things are viewed, causing other important aspects to be overlooked.Below, I elaborate on the ways of thinking leaders should avoid if they want to build sustainable and effective well-being programmes that catalyse real organisational change. I also offer practical suggestions for those ready to take these efforts seriously.Let’s “fix” them: overly individual-focusedIf an employee isn’t feeling well, a manager will likely suggest they take a day off. Some particularly empathetic managers may even grant short-term sick leave if they believe someone could benefit from a longer break, especially if that person is under intense stress or displaying symptoms of a mental health condition. “Perhaps they just need some time away,” their manager may think.This works if it’s part of a more holistic approach to employee well-being. However, if the only solution is sick leave or even providing access to free counselling services, the implicit message to employees is: This is an individual issue, so go and work on yourself and come back ready to work again. This approach focuses on addressing the symptoms, not the root causes. It neglects other factors that might be at play, including workload, resource distribution, time constraints and team culture and dynamics. The stress of transitional periods, such as adjusting to a different boss, organisational restructuring or new business strategies, can also affect well-being in ways beyond an individual’s control.Instead, managers could ask, “What else might be happening in the work environment to cause this employee distress?” before enacting the appropriate solutions, whether that’s extending a project deadline or designing a team intervention. As leaders, this is where you need to step in to examine and address the factors that might be compromising that person’s well-being, because no amount of sick leave or counselling sessions would ameliorate the situation. Let’s “change” them: overly short-term focusedAnother mental trap is thinking that simply changing someone’s working conditions will eradicate the problem. I’ve seen leaders spring into action – be it by moving an individual to a different team or reducing their workload – in the hopes of improving an employee’s well-being, sometimes inadvertently sending that person into a panic because they assumed their manager no longer trusted them.These actions are taken with good intentions and are often cited as best practices for workplace mental health. But they fail to consider one important thing: what the individual actually wants. What’s more, if the root of the problem is an organisational culture issue, such as harassment, bullying or a norm of toxic behaviour, then changing teams might offer some breathing space, but it won’t be long before the same negative patterns creep in again.Asking, “How do I involve and include the employee and others in decision-making?” is one way for leaders to escape this thinking trap and resist the bias towards action. Rather than rushing to implement change, leaders can take the time to speak directly with the employee to understand their concerns and preferences. It’s equally important to consult other managers across the organisation to determine whether this is an isolated incident or a pattern requiring broader behavioural change. Let’s “educate” them: overly surface-levelMost companies run some variation of well-being training and educational campaigns. These are foundational for building awareness, and I think they are a good first step to getting buy-in and destigmatising thorny topics like mental health and suicide. Classic formats include guest speaker series, World Mental Health Day events and sending managers to Mental Health First Aid training.These initiatives aren’t the problem. In fact, they are crucial. The issue arises if they’re all a company offers, or treated as a box-ticking exercise. Employees who attend these events tend to be self-selecting – often those who already know how to prioritise their mental health – which means that the people who need support the most may not be reached. Another challenge is that many of these initiatives primarily focus on building knowledge and delivering content, rather than establishing habits or longer-term follow-ups to support behavioural change.Instead of piecemeal initiatives, focus on your overarching well-being strategy. Think of workplace well-being as you would any long-term business strategy: Design it across one-, three- and five-year time horizons, with building blocks that compound over time instead of repeated topics repackaged under a new name. Give employees a clear view of the learning journey ahead. Borrowing strategies from leadership development, such as coaching, can also help translate newly gained knowledge into day-to-day behaviours. Let’s “do something”: overly siloedMany companies allocate a limited budget to well-being programmes, sometimes folding them into learning and development budgets and tacking them on to other training sessions. They may treat such initiatives as the purview of HR or the people and culture department, or as a theme that the communications or marketing teams can build on to boost camaraderie and staff spirit.Without meaningful human and financial support from cross-departmental leaders, the board of directors and the wider organisation, these programmes are unlikely to have their intended effect. Treating them as one-off projects wears out the teams responsible for delivery and, if no real change follows, employees may grow cynical and view these initiatives as a nuisance rather than helpful.To pave the way for sustainable change, leaders can ask themselves, “How do we embed well-being as a pillar of our business?” Well-being should be integrated into an organisation’s values and performance frameworks, and reflected in the behaviours modelled by leaders. This is where diverse perspectives are essential: Those making decisions about well-being strategies should include people with lived experience of various health conditions, ensuring that organisations don’t inadvertently sideline the very people they intend to support, nor design programmes that fail to speak to their needs.A comprehensive approachIf any of the above resonates, it may be worth pausing to reflect on whether your current approach to workplace well-being is truly the best way forward. Good intentions are a starting point, but they must be translated into something strategic and comprehensive. Here are some suggestions to get you started:Get your board of directors on board. Workplace mental health is a risk-management and sustainability issue for corporate governance. In many countries, directors have an obligation under local work and safety laws to ensure appropriate resources are in place to address such issues. A board that is interested in employee well-being will provide greater support to the organisation’s leaders and ensure accountability. Allocate an adequate, recurring budget to support your organisation’s long-term well-being strategy. People are the cornerstone of any workplace, and mentally fit employees are better equipped to adapt, remain agile and be productive amid inevitable business changes. Investing in your people should be no less a priority than investing in your latest product or service. Seek out your employees’ real experience. Metrics can be misleading, and many leaders place too much confidence in low utilisation rates of counselling hotlines or the absence of formal complaints as indicators of a healthy workplace. In reality, what gets reported and what is actually experienced can differ significantly. To close that gap, leaders need to create the conditions for honest conversations with employees and invest the time to listen without judgement.Treat well-being as a non-negotiable. Every employee should have access to foundational well-being development, which should carry at least the same weight as mandatory compliance, regulatory and data privacy training. These all serve the same ultimate purpose: the long-term sustainability of the organisation.By adopting a more holistic mindset, your well-being initiatives can move beyond surface-level gestures with fleeting impact – creating something that genuinely supports your people, strengthens your culture and delivers lasting and meaningful change.]]></description>
                  <pubDate>Thu, 26 Mar 2026 01:15:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48411 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/career/four-mindsets-undermine-workplace-well-being-initiatives#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-03/shutterstock_2494647419.jpg?itok=NxOAUpcd" type="image/jpeg" length="1034892" /><dc:creator>Enoch Li</dc:creator></item><item><title>How Leaders Can Have Effective Conversations</title>
                  <link>https://knowledge.insead.edu/leadership-organisations/how-leaders-can-have-effective-conversations</link>
                  <description> <![CDATA[Whether you’re delegating complex tasks or rallying your team around a new initiative, communicating effectively is an essential leadership skill. Clear, intentional and impactful conversations can not only help you secure buy-in from others but also ground professional relationships in high trust and strong rapport.The internet is littered with advice on how to pull this off – some based on collections of anecdotes, others on experiments, and still others on frameworks that provide a list of instructions for communicating well. Each of these approaches has its merits, as well as its drawbacks. Anecdotes are vivid but prone to selection bias. Experiments provide evidence for causality but are often separated from real-world contexts. And step-by-step frameworks are structured and easy to follow but tend to rely on generalised principles that aren't tailored to or verified by data from actual conversations. What’s lacking is a comprehensive dataset of real-world professional conversations that allows us to identify characteristics associated with effective workplace communication. In this article, we draw on data from a professional services firm that specialises in training and matching executive assistants (EAs) with clients to do just that.Insights from professional conversationsTo date, academics and practitioners haven’t had access to an extensive dataset of naturally occurring professional conversations. To our knowledge, the closest thing available is a large dataset of personal conversations, which differ from professional conversations in norms, goals and style. To fill this gap, we present an analysis of over 2,300 recorded conversations containing more than 1.2 million individual statements. The anonymised data comes from exchanges between leaders – from entrepreneurs to high-level executives – and their EAs.The professional services firm that fulfilled the placement of these EAs tracked signs of trust and rapport between the leaders and their EAs on a weekly basis. Trust, for instance, was measured through objective, escalating markers – starting from the leader providing their EA with calendar access (low marker of trust); to allowing their EA to correspond with others on their behalf; to providing their EA with access to their personal bank accounts (high marker of trust).Rapport was defined through escalating markers that tracked the level of closeness implied by the routine communication between leaders and EAs. The lowest marker was the presence of email exchanges – a relatively perfunctory way of communicating. From there, rapport markers escalated to personal messaging apps, phone calls, scheduled one-on-one meetings, unscheduled one-on-one meetings and being a part of the leader’s circle (highest marker of rapport). Studying their interactions, we scored trust and rapport based on whether these behaviours took place between leaders and their EAs. We then conducted a regression analysis to test whether elements in the conversations predicted trust and rapport. We used AI to parse and transcribe the recorded conversations, as well as classify them based on whether the statement was professional or personal in nature; if it was about the past, present or future; and whether it was a question. We also counted the number of turns (passing the baton from one speaker to the other) in the conversation. Building trust and rapportThrough our analysis, we identified specific characteristics that were associated with higher trust and better rapport:Conversations in which leaders and EAs both participated more in the conversation and exhibited greater turn-taking (i.e. participants spoke one at a time in alternating turns) were associated with greater trust. This may be because greater turn-taking signals greater mutual respect, enhances information exchange and reflects better synchrony between individuals.Conversations with a lower proportion of statements about the past, and a higher proportion of statements about the present and future, were associated with leaders having greater trust in their EAs. This could be because future-oriented conversations suggest proactivity – handling upcoming challenges and problems – which can elevate trust. Psychological research also suggests that people who focus on the future are seen as more agentic and forward-looking, and therefore more trustworthy. Conversations with a greater proportion of professional statements (relative to personal ones) were associated with better rapport between leaders and their EAs. This might be because strictly professional conversations that place a clear focus on business are more aligned with role expectations of EAs and signal competence and professionalism.It should be noted that regression analysis, although widely used and accepted, is based on correlations. This means that the findings imply that greater turn-taking, future-orientation and professional focus either cause or reflect greater trust and quality of communication, or both. Regardless, our analysis illustrates what effective professional conversations look like and reveals the behaviours associated with greater trust and better rapport. Putting this into practiceJust as people who aspire to be wealthy try to mirror characteristics of affluent individuals and those who aim to be successful athletes emulate the training habits of decorated Olympians, leaders can assess their own professional conversations against these metrics.Although our dataset covered only conversations between leaders and their EAs, we believe the findings are relevant to a wider range of professional settings. Many workplace situations involve interactions with similar status asymmetries, rapid information exchange and relationship-building, and toggle between administrative details and strategic decisions.Leaders can facilitate more effective professional conversations that promote trust and rapport among co-workers by cultivating organisational norms. Specifically, we recommend the following:Promote greater participation: Fostering a culture of “making sure everyone is heard” can increase participation during conversations, particularly those between individuals at different levels of the corporate hierarchy (e.g. between a junior employee and a director). Simple norms can help, such as ensuring sufficient time for everyone to speak at meetings, encouraging junior employees to share their thoughts, or prioritising smaller meetings or one-on-ones. Shifting virtual meetings to in-person can also help in this regard.Focus on the present and the future: During feedback sessions with team members, a template that is heavily weighted on the present and future (vs. the past) can promote trust. Similarly, meeting agendas can emphasise current goals and upcoming opportunities with the help of forward-looking frameworks, such as scenario-planning, to shift the conversational focus away from the past. Leaders can also use future-oriented framing in their everyday language, such as “let’s build on where we are now” or “what’s our next step?” instead of “we should have done this differently”.Keep the conversation professional: Although people want to connect with their colleagues on a personal level, our findings suggest that “less is more” in this respect. Designating time for casual chats separate from work-related conversations can help achieve the dual goals of professionalism and connection. For example, leaders can dedicate a brief period at the start of meetings to discuss personal matters – thereby allowing employees to connect with each other – while maintaining focus on work-related issues. This can promote positive perceptions of rapport and an appropriate level of professional closeness. Finally, modelling and perhaps even codifying an etiquette that keeps workplace conversations mostly at the professional level could help.Effective professional conversations are essential for building trust and rapport in any workplace. By sharing the mic, not dwelling on the past and keeping things professional, leaders can foster a more positive and productive working environment.]]></description>
                  <pubDate>Mon, 30 Mar 2026 01:30:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48401 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/leadership-organisations/how-leaders-can-have-effective-conversations#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-03/shutterstock_1676881228.jpg?itok=j9deSulD" type="image/jpeg" length="1018054" /><dc:creator>Nadav Klein</dc:creator><dc:creator>Eliot Gattegno</dc:creator></item><item><title>Is the Asian Economic Model Breaking?</title>
                  <link>https://knowledge.insead.edu/economics-finance/asian-economic-model-breaking</link>
                  <description> <![CDATA[Here’s a question for you: In which economies do exports account for more than half of GDP? If you guessed Germany (41 percent) or China (20 percent), you’d be wrong. The answers are Vietnam (90 percent), Cambodia and Malaysia (70 percent), Thailand (60 percent) and Singapore – which, at 175 percent, exports more than it produces, the economic equivalent of a magic trick. These aren’t merely export-oriented economies. They are the beating heart of globalisation. Strip away the flows of goods, capital and technology of the last five decades, and you strip away the Asian model itself. For more than 50 years, that model delivered the most compressed period of mass prosperity in human history. It rested on three pillars: open trade, a stable geopolitical order anchored by American power, and the assumption that technology would continue to flow from rich to poor countries, allowing catch-up growth.Unfortunately, all three pillars are currently under strain as the world undergoes a structural break. The break isn’t yet visible in headline growth numbers, which is precisely what makes it dangerous. The IMF projects that most of Asia will grow respectably through 2026. However, many factors that sustained GDP growth last year – tariff exemptions, TACO (Trump Always Chickens Out), stockpiling and the AI boom – may all wane in the years ahead, curtailing growth.The trade pillarDonald Trump’s first stint as President of the United States was annoying but manageable. Tariffs were narrow, targeted, telegraphed in advance and phased in slowly. Crucially, firms could plan for them, with many rerouting supply chains through Vietnam and Malaysia. Trump 2.0 is different. The tariffs are sudden, volatile, whimsical and coercive. Unpredictability is a feature, not a bug. The current conflict in the Middle East has only deepened the sense of chaos and uncertainty.After the tariffs were announced last year, countries raced to strike deals: Vietnam accepted 20-percent tariffs in exchange for zero tariffs on its goods and commitments to buy American liquefied natural gas and aircraft. Malaysia agreed to 19-percent tariffs, giving Washington a say over its export controls. Japan and Korea got 15 percent in exchange for investment pledges, while Singapore, despite its free trade pact with the US, quietly accepted a 10-percent baseline tariff. Last month, the Supreme Court stepped in and deemed these tariffs unconstitutional. Almost immediately, Trump invoked presidential powers under a 1974 trade law to impose blanket 15-percent global tariffs. Everyone now gets 15 percent, and the trade deals and bilateral concessions, extracted under duress, are in limbo. Some will be slow-walked, some renegotiated, others reneged on. However, even these tariffs are likely illegal, as they are used to address balance-of-payments issues that the US simply doesn’t face. They expire in 150 days, and what happens after that is anyone’s guess.Beyond tariffs, the more immediate threat is transhipment. Goods in Asian supply chains cross borders an average of six times before becoming a final product. Transhipment tariffs of 40 percent, designed to prevent rerouting and reclassification, expose Singapore, Vietnam and Thailand to a compliance nightmare they are only beginning to map. The advice for companies is to invest heavily in data, trace what fraction of your inputs originate from each source, and prepare for rules that will change before the compliance systems are built.Indonesia's position deserves a separate note. Its 2020 nickel ore export ban was a device to force Chinese and Western battery manufacturers to invest in downstream processing within Indonesia. It didn’t want to be an upstream commodity exporter, susceptible to the resource curse and Dutch disease of deindustrialisation. In fact, it wanted the entire value chain. The complication is that Chinese firms currently control an estimated 70 to 80 percent of Indonesia’s nickel processing capacity. The mineral is Indonesian; the value chain is largely not. Battery facilities have been slow to ramp up, while Chinese electric vehicle manufacturers are shifting to battery chemistries that use far less nickel.The geopolitical pillarJapan and South Korea built their entire defence posture around one assumption: that the American security umbrella would be deployed in times of need. That assumption is now a question mark. The recent behaviour of the Trump administration – the Venezuela operation, the Greenland threats, the treatment of NATO allies as supplicants rather than partners – has communicated something specific to every Asian capital: Sovereignty has a price, and the US no longer considers itself unconditionally bound by the architecture it built.The Taiwan issue concentrates this anxiety. A recent article in The New York Times put a number on what a Taiwan crisis would cost: an 11-percent decline in US GDP, while China’s economy would contract by 16 percent. Taiwan produces roughly 90 percent of the world’s high-end chips and underpins an estimated US$10 trillion of global GDP. Such a scenario would not merely threaten its flagship chipmaker TSMC; it would detonate the entire regional supply chain, including Singapore, Malaysia, Vietnam, Thailand and the Philippines.ASEAN countries don’t primarily buy from TSMC to consume chips. Rather, they sit downstream of it, specialising in the assembly, testing and packaging (ATP) of chips before re-export to end markets globally. In 2024, Taiwan exported close to US$40 billion in semiconductors and components to ASEAN. Roughly 80 percent of these exports flowed to Singapore and Malaysia alone. The two countries differ in an important way, however. Malaysia remains almost entirely a back-end operation: It handles global chip ATP but produces almost no wafers itself. Singapore is further up the value chain and hosts multiple active wafer fabrication plants, or fabs, with more under construction. But even in Singapore, the fabs are foreign-owned and dependent on an unbroken supply of wafers and equipment from Taiwan, the Netherlands and the US.What makes this especially uncomfortable is that ASEAN is exposed to the downside of both the current system and the transition away from it. A Taiwan crisis would cut off inputs. But a successful American reshoring effort (the Trump administration is pushing for 50 percent of chips to be made on US soil) would also shrink the flow of Taiwanese inputs through Asian ATP hubs.The question ASEAN planners aren’t yet asking loudly enough is: What is our exposure not to a Taiwan invasion, but to a Taiwan disruption – elevated tensions before any shots are fired, when shipping insurance spikes, investment freezes, flows of chips are disrupted, and the great powers start asking smaller states whose side they are on?The AI technology pillarAn AI Impact Summit was organised in India just last month. The summit dismissed the idea of “superintelligence soon” as an American imperialist narrative and instead bet on the diffusion of small models, open-source models and the need for some edge compute. This may be right. But what if it happens to be wrong?American hyperscalers including Microsoft, Google, Meta and Amazon collectively committed over US$300 billion to AI capital expenditure in 2025 and US$690 billion in 2026. The Stargate Project alone commands US$500 billion over four years. These are investments in recursively self-improving systems and building “god in a box”. Again, it’s right to question the fragility and sustainability of this investment and lament the relentless AI hype. Then again, Claude Cowork and Claude Code wiped out US$300 billion in software company valuations in a week!Against this, Singapore’s recent commitment of S$1 billion over five years to its national AI strategy is a rounding error. This isn’t a criticism of the city-state; it simply can’t match American numbers. The question is whether "good enough" local models will be sufficient as the frontier accelerates, or whether economies that make this bet find themselves locked into technological dependence on whoever controls frontier systems – almost certainly either the US or China – at precisely the moment when AI-driven productivity gains are widening the gap between frontier and non-frontier economies faster than any previous technology.China is exerting a different kind of pressure. Its relentless application of AI-enabled automation in manufacturing has compressed the low-cost labour advantage that Vietnam, Thailand, Indonesia, the Philippines and India are counting on. Robot density in Chinese manufacturing rose from 25 per 10,000 workers in 2015 to roughly 392 in 2023 – nearly matching Germany, the world’s most automated large manufacturing economy. The window for labour-cost-based manufacturing competition may be closing faster than anyone in Jakarta or New Delhi is prepared to acknowledge.India’s position is the most paradoxical. Its US$250 billion IT services sector, employing five million people, was built on one comparative advantage: large numbers of English-speaking engineers who could do, at lower cost, what Western firms needed. That advantage is being structurally eroded by the very AI systems India now hopes to deploy for growth. And it’s being built by the major AI companies, staffed and often led by Indians. The middle rung of the ladder is being pulled up while the climbers are still on it.My fear is not that AI disrupts. Every technology disrupts. My fear is that the disruption arrives before the adaptation does, and that governments currently building five-year AI roadmaps premised on a plateau that may not materialise will look up in 2028 and find that the world they planned for no longer exists.The Asian trilemmaEvery piece about Asia in crisis is supposed to end on a positive note, with every challenge reframed as an opportunity. But sometimes, a crisis is just a crisis. And compulsory or compulsive optimism is a form of denial.The threats to Malaysia and Singapore in semiconductors and to Japan and South Korea on security are real. So are the opportunities for India and Indonesia. The fracturing of the China-centric supply chain creates space. The demand for alternative manufacturing bases, mineral suppliers and technology partners is genuine. But the opportunity is narrow, time-bound and conditional on decisions that no government has fully committed to: continued investment in logistics, reforms and state capacity in India’s case, diversification away from Chinese processing dominance in Indonesia’s.The Asian miracle was built on the assumption that the system would hold. The trilemma is the likelihood that it won’t. What is new for firms and governments in this region is that the pillars are cracking, and the pivot has to happen faster than the displacement.Read a longer version of this article.]]></description>
                  <pubDate>Mon, 09 Mar 2026 01:30:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48396 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/economics-finance/asian-economic-model-breaking#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-03/shutterstock_2624487499.jpg?itok=LW8e_rH7" type="image/jpeg" length="1005483" /><dc:creator>Pushan Dutt</dc:creator></item><item><title>The Leadership Aspiration Gap</title>
                  <link>https://knowledge.insead.edu/leadership-organisations/leadership-aspiration-gap</link>
                  <description> <![CDATA[Corporations, societies, nations. All require a mix of different qualities to succeed, including quality leadership. Yet, as today’s leaders prepare to pass on the mantle to a new generation, leadership seems to be approaching a moment of crisis. A growing body of evidence suggests that fewer young professionals today aspire to traditional leadership roles.The changing nature of leadershipThe Silent Generation (born between 1925 and 1945) was defined by duty, discipline and conformity. Shaped by the war and economic scarcity, they valued job security and hierarchy. Baby Boomers (born between 1946 and 1960) were driven by growth and prosperity, following linear careers paths marked by loyalty and long-term organisational commitment. Younger generations, on the other hand, see the world differently and have different aspirations. Generation X (born between 1960 and 1980) entered the workforce during early digitalisation. They hold a more individualistic and flexible career mindset, and favour competence and autonomy over status. The preference for autonomy, mental well-being and meaningful work over a conventional career path became even more pronounced in the following generations. Millennials (Generation Y, born between 1980 and 1995) and Generation Z (born between 1995 and 2010) were born into a world of abundance and acceleration. Their lives and values have been shaped by global connectivity, systemic uncertainty and constant change – marked by 9/11, financial crises, climate change, Covid-19 and social justice movements. They embrace portfolio careers, question traditional authority and measure success not just by position, but by impact and balance. They are also drawn to alternative models of leadership such as “conscious unbossing”, choosing to lead through influence, collaboration and purpose. Yet, the waning interest in pursuing leadership roles isn’t simply due to the lag in developing leadership models suited to a new generation. Rather, younger generations increasingly see a world in which assuming almost any leadership position has become an unattractive proposition. The new leadership challengesThe youth of today are reaching maturity in a world that is volatile and uncertain. One that seems only to move from one crisis to the next.Activism and civic society form important parts of our political discourse, particularly in a world of difficult trade-offs. Yet when public challenge becomes personal and uncompromising, and when single issues dominate complex debates, the space for balanced leadership can narrow and the cost of taking on highly visible roles is set to increase.No doubt, the personal cost of holding any leadership position in the public eye is increasing. As one C-suite executive of a major corporation revealed to me in a recent conversation: “The overwhelming emotion gripping our board and executive team for the last five years has been fear.”In such a world, it’s unsurprising that many among the younger generation are shunning leadership opportunities. A plush corner office no longer holds much attraction. Neither do ever-larger compensation packages, which have become a source of public criticism themselves. In Deloitte’s 2025 Gen Z and Millennial Survey covering over 23,000 Gen Z and millennial professionals across 44 countries, only 6 percent identified reaching a leadership position as their primary career goal, despite attaching high value to meaningful work, development and social impact. These pressures are increasingly visible at the very top of organisations. CEO tenures have been steadily declining across major markets, while a growing share of new appointments involve first-time incumbents – an indication that leadership roles that are not only more exposed and volatile, but also increasingly short-term. Bigger compensation packages are, in part, designed to offset the growing personal and reputational costs of leadership. Yet, by enabling early financial independence, they also lower the barriers to exit when those costs become too high. This reinforces, rather than reverses, the fragility of leadership roles.Incentives built on traditional ways of thinking are increasingly insufficient to attract younger would-be leaders – except, perhaps, for those driven by ego or individual advancement. Such motivations may attract certain types of people, but experience suggests they are poorly suited to building the resilient, inclusive leadership systems the world requires. Leadership of hopeAs highlighted in the 2025 INSEAD Global Talent Competitiveness Index report, competitiveness isn’t only about developing and attracting talent, but also about creating environments in which people still believe leadership is viable, desirable and impactful. Those conditions are eroding, with significant negative implications for economic and societal development.Turning this around is possible. It will take true leadership from the current crop of business and political leaders, as well as a shift in approach from those, such as business schools, that have the responsibility for nurturing potential leaders.The first step is to replace the pervasive apocalyptic narratives – be they about climate, economic potential or technological developments – with a narrative of hope. Hope isn’t the same as optimism or believing something will turn up to make everything alright. It’s what drives us to engage and motivates us to channel our energies to work towards what we believe to be a positive outcome. Opportunities for innovation and continued progress, however imperfect, are worth striving for.   In this regard, the leaders of today are responsible for demonstrating that there are viable leadership paths for younger generations, and that leadership is about shaping the future, not just managing the present while being fatalistic about (or, worse, uninterested in) the future. They need to recognise that the values and incentives in a financialised world, while still relevant, are no longer enough to inspire.They also need to demonstrate resilience and the confidence to navigate external pressures with clarity and consistency. When leaders act from a grounded sense of purpose – rather than reacting to the loudest voices or the shifting political climate — they earn the respect of younger generations and become the role models they seek.Institutions and their leaders need to demonstrate greater steadiness and consistency, rather than defaulting to short-term expediency. Above all, we all bear responsibility for the kind of society in which we live. We need a culture that welcomes open discussion without resorting to personal attacks. All of this will determine whether capable individuals step forward or step back from top roles.As political scientist and writer Dominique Moïsi observed, “The problem is that Europe has become, for the younger generation, a place to be and not a place to do – and this has to be changed.” Increasingly, this holds true not only for Europe, but for much of the developed world. We need a new generation of leaders with the courage not just to inhabit, but to shape, protect and advance the world we live in. To do so, leadership must not only be made accessible, but worth aspiring to. Because when the most thoughtful and capable step up – not despite the complexity, but because of it – leadership can become a force for connection, progress and hope.]]></description>
                  <pubDate>Thu, 12 Mar 2026 01:00:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48391 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/leadership-organisations/leadership-aspiration-gap#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-02/bridging_young_people_to_move_forward.jpg?itok=3ys_5koU" type="image/jpeg" length="802143" /><dc:creator>Ron Soonieus</dc:creator></item><item><title>Can AI Align Sustainability and Profits?</title>
                  <link>https://knowledge.insead.edu/operations/can-ai-align-sustainability-and-profits</link>
                  <description> <![CDATA[In 2020, Spacemaker, a Norwegian start-up applying machine learning to architecture, was acquired by Autodesk for US$240 million. Despite exciting developments in AI and machine learning, few companies were focusing on AI applications for construction and real estate. Spacemaker was an exception.According to McKinsey, construction was by far the weakest sector as measured by productivity growth since 1995. “If you look at the efficiency curve in [the construction] industry, it is sinking,” said Janne Aas-Jakobsen, founder and CEO of Consigli, a software-as-a-service (SaaS) provider for real estate developers and contractors. She added that 85 percent of projects face cost overruns, with almost 50 percent experiencing cost overruns of more than 15 percent. The sector is primed for disruption.In a webinar with Aas-Jakobsen, we explored this untapped opportunity at the intersection of technology, construction and finance, and how her company applies AI to the early-stage design of new building projects to massively reduce costs and carbon emissions.Ripe for reinventionWhen planning to construct a new building, developers need to solicit bids from contractors, obtain building permits and get investors’ buy-in. Early-stage calculations and plans are prerequisites to develop specifications on which contractors could develop binding bids. At the same time, developers contract engineering consulting firms to provide detailed specifications of technical systems – from electrical to ventilation, heating, cooling, water and fire prevention. An estimated 10 percent of total new real estate development costs are tied to early-stage engineering – a figure that’s fairly consistent across geographies. It’s a highly complex task with many disciplines involved, and regulatory requirements add more complexity to the mix. For instance, counties and municipalities in the United States had reportedly over 93,000 different building codes, which means companies building prefabricated houses must adapt to the specific codes of each jurisdiction. “We have been doing things the same way for years, regardless of whether we are using pen and paper or computer-aided design programs,” said Aas-Jakobsen. As information is sequentially passed on from one party to the next, it’s inevitable that quality and complexity in the final design are lost. Fortunately, everything within that complexity is known: the laws of physics, the building standards, what global suppliers can provide and so on. The design process in construction engineering was ripe for reinvention. “Finding an optimal solution to a complex puzzle of knowns is what AI is good at,” she surmised. As Aas-Jakobsen searched for methods to solve these problems, she took inspiration from ship construction, where algorithms were used to size and design engine rooms. In 2020, she founded Consigli, which uses AI to facilitate early-stage mechanical and electric design – from lighting and sprinklers to ventilation. Engineering a solutionThe SaaS product, the “autonomous engineer”, enables real estate developers and construction contractors to reduce their expenses while maximising usable space. It’s capable of reorganising rooms and parking areas for space optimisation, which is more complex than what appears on paper: cars need to be able to move out of parking lots, guests need to be able to escape from their hotel rooms in an emergency, and plant rooms need to be large enough to operate and maintain – but no bigger than necessary due to the high costs.In addition, the AI co-worker provides a wide range of services, including generating reports customers expect from construction engineers and ensuring consistency and compliance. It flags inconsistencies between the building model and tender materials, misalignment between tenants’ needs and the spatial design, and non-compliance of designs and specifications with building codes. Real estate developers can log in to the web-based software, create a project and upload the building information model (BIM) from the architect, along with relevant information such as tenant specifications, room mix or technical details such as energy requirements. The autonomous engineer would then update the BIM to include optimal room layouts and systems such as electrical, cooling and fire prevention.  This flow of communication isn’t too different from how one might work with consultants, but speed is of the essence. Whereas engaging with consultants can take months, the autonomous engineer can produce actionable output in one to two days, saving 15 to 20 percent in costs. A vision for more sustainable construction The beauty in the tool isn’t only in lowering the risks and costs of real estate development, but also its carbon footprint – by reducing the use of concrete, ducts, pipes and cables, alongside potentially decreased cooling needs.  “We do the boring stuff, the boring part of sustainability. It’s about using less,” said Aas-Jakobsen. But in the context of the construction sector, which is responsible for 40 percent of annual global emissions, the impact of using less materials is anything but mundane or negligible. Consigli’s vision is to reduce construction materials by 20 percent and, in doing so, decrease embodied carbon in the industry. Resistance to change is inherent among real estate companies. After all, it takes time to change workflows. Moreover, risk and liability are common concerns, since real estate development projects involve developers, engineering consultants and contractors who are liable for various aspects of the project. This is where a tool to help developers focus on optimising the early stages of the design process can reduce risks considerably.In one instance, Consigli helped a client identify discrepancies in the insulation capacity of materials (U-value) and the conditions of green financing. Had the variance not been flagged at the design stage, the building would have been built outside the scope of the financing conditions, which could result in major reworking and waste. Financial gains may be more apparent in some cases, such as the example of Norwegian real estate company Entra. It had bought a hotel and sought Consigli’s services to find ways to increase the property’s value. The solution offered was to design spaces to incorporate more hotel rooms, which didn’t only result in increased operating revenue, but a huge premium when the property was later sold.As improved planning lowers risks and costs while increasing efficiency, sustainability follows naturally. With a clear business case for sustainability, little convincing is needed. Indeed, Consigli caught the attention of global infrastructure engineering giant AECOM, which acquired the start-up in November 2025 for an estimated US$390 million. Ultimately, its ability to boost construction efficiency and margins, while generating solid sustainability benefits is not only a clear strategic differentiator for AECOM, but is also in line with its sustainable legacy strategy, helping it  to leave “a positive, lasting impact for communities and our planet". ]]></description>
                  <pubDate>Tue, 03 Mar 2026 01:00:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48386 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/operations/can-ai-align-sustainability-and-profits#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-02/ai_in_construction_engineering.jpg?itok=8n_v1ekM" type="image/jpeg" length="893681" /><dc:creator>Karel Cool</dc:creator><dc:creator>Atalay Atasu</dc:creator></item><item><title>AI in Healthcare Diagnostics: Promises, Pitfalls and the Path Forward</title>
                  <link>https://knowledge.insead.edu/responsibility/ai-healthcare-diagnostics-promises-pitfalls-and-path-forward</link>
                  <description> <![CDATA[Whether it’s routine check-ups, emergency care or managing chronic illness, everyone relies on healthcare at some point in their lives. AI is no longer a future prospect in medicine; it’s already being used in hospitals to analyse medical images, assist with reading scans and support diagnostic decisions as part of routine clinical practice.Our research highlights how AI systems can operate at scale, flagging urgent abnormalities and enabling clinicians to make faster and, in some cases, more accurate diagnoses. In a sector as large and complex as healthcare, their adoption raises urgent questions not only for medicine, but for organisational design, ethics and trust.Transforming diagnostic practiceRadiology and pathology are uniquely suited to AI adoption due to their reliance on pattern recognition and image interpretation. In radiology, our research shows that AI trained on millions of scans can detect conditions such as tuberculosis, stroke, lung nodules and breast cancer at a level comparable to specialist clinicians. In pathology, AI systems can analyse digitised tissue slides to identify cancer cells, quantify biomarkers and prioritise cases requiring urgent review.In practice, these tools don’t replace clinicians but function as decision-support systems. AI models typically analyse images immediately after they’re taken, identifying potential abnormalities for review. A radiologist or pathologist remains responsible for issuing the final diagnostic report. This human-AI collaboration can significantly reduce reporting times and administrative burden, enabling clinicians to focus on complex decision-making and patient care.The impact is measurable. Our research shows that AI-assisted workflows have reduced stroke treatment delays from hours to minutes, accelerated tuberculosis detection in low-resource settings – such as high-density prison populations – and improved early cancer diagnosis. In health systems facing workforce shortages and rising demand, these efficiency gains aren’t merely convenient but are increasingly necessary.Beyond efficiency: organisational changeDespite its technical capabilities, AI’s success in healthcare depends as much on organisational design as on the models themselves. Introducing AI into a hospital isn’t simply a procurement decision; it requires redesigning workflows, redefining professional roles and building trust among clinicians.Research highlights how intelligent organisations aren’t merely defined by their ability to automate tasks, but by how effectively they combine the complementary strengths of humans and technology. AI excels at processing vast volumes of data quickly and consistently, while clinicians contribute contextual understanding, ethical judgment and accountability. When AI is positioned as an assistive tool, it can augment diagnostic accuracy and free professionals from repetitive tasks. When poorly integrated, it risks creating friction, mistrust and overreliance. Clinician acceptance is therefore critical to successful adoption. Concerns about job displacement, de-skilling and opaque decision-making remain significant barriers. Even highly accurate tools may be underused if they are difficult to interpret and poorly integrated into existing systems, or if they lack endorsement from senior clinicians. Effective leadership by individuals trained in “AI literacy” and transparent communication are essential to bridge the gap between AI adoption and sustained use in clinical workflows.Where AI falls shortWhile AI has demonstrated impressive performance under controlled conditions, real-world deployment exposes important limitations. One key risk is automation bias – the tendency for humans to over-trust algorithmic outputs. Incorrect AI suggestions can increase diagnostic errors, even among experienced clinicians, particularly when errors aren’t clearly signposted.In pathology, AI systems can be vulnerable to factors such as tissue contamination. Unlike human experts, who can recognise and disregard irrelevant material, AI models may misinterpret contaminants as clinically significant features.What’s more, many models are trained on historical medical data. When those datasets reflect long-standing inequalities in research and clinical practice, these disparities risk being reproduced at scale. Women, people from ethnic minority backgrounds and younger patients have historically been underrepresented in clinical trials, and patterns of bias in care have further shaped the data available for training AI systems. In practice, this can translate into underdiagnosis or delayed treatment for already underserved populations, amplifying existing health inequities. Ethical and legal accountabilityThe integration of AI into diagnostics complicates traditional notions of responsibility. In medicine, clinicians are morally and legally accountable for patient outcomes. When AI contributes to clinical decisions, accountability becomes distributed across clinicians, developers, institutions and regulators.Opacity in AI systems, often described as the “black box” problem, further complicates accountability. If clinicians can’t understand how an algorithm reached a conclusion, their ability to critically evaluate its output is diminished. This concern has influenced regulatory approaches. For example, the United States Food and Drug Administration has historically approved only so-called “locked” AI models that do not change after deployment, prioritising predictability over adaptability. While this stability supports safety and accountability, it also limits learning. Locked systems can’t adapt to new data or correct for emerging biases in real-world use. The challenge for healthcare leaders is to balance the need for reliable, auditable systems with the potential benefits of carefully governed adaptive AI. Globally, regulatory strategies vary. The European Union’s AI Act introduces risk-based obligations for healthcare AI, while the United Kingdom has adopted a more flexible, innovation-friendly approach through regulatory sandboxes – engaging firms to test products or services that challenge existing legal frameworks. These frameworks reflect an ongoing effort to balance patient safety, innovation and ethical responsibility. However, legal systems continue to lag behind technological progress, particularly in addressing harms related to bias or systemic design flaws.The path forwardAI has already begun to reshape the landscape of healthcare diagnostics, and avoiding it due to fear of error or liability risks forfeiting substantial benefits. The solution lies in responsible implementation: rigorous validation on diverse populations, continuous monitoring after deployment, clear accountability frameworks, transparency with clinicians and patients, and sustained human oversight.Ultimately, AI should be understood not as a replacement for clinical expertise, but as an intelligent partner. When embedded thoughtfully within organisational structures and guided by ethical principles, it can help reduce diagnostic error and support clinicians in their work. Governed by adaptive regulation, it also has the potential to extend specialist care to underserved populations and support more sustainable healthcare systems.The challenge for future healthcare leaders isn’t simply to adopt AI, but to design systems where humans and machines work together – safely, ethically and effectively – in the service of patient care.]]></description>
                  <pubDate>Mon, 02 Mar 2026 01:00:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48381 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/responsibility/ai-healthcare-diagnostics-promises-pitfalls-and-path-forward#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-02/shutterstock_1627208287_1_1.jpg?itok=FadNn2e6" type="image/jpeg" length="207671" /><dc:creator>Sophie Delaye</dc:creator><dc:creator>N. Craig Smith</dc:creator></item><item><title>How Talent Can Thrive in an AI-Driven World</title>
                  <link>https://knowledge.insead.edu/career/how-talent-can-thrive-ai-driven-world</link>
                  <description> <![CDATA[In this era of "permacrisis" and constant disruption, adaptability and resilience are vital traits that require collaboration and an increasing focus on human-centric skills. That’s the messaging to come out of the 2025 Global Talent Competitiveness Index (GTCI), an annual report that highlights the latest talent trends and offers insights into the current global talent landscape. For this episode of “The INSEAD Perspective: Spotlight on Asia” podcast series, Sameer Hasija, Dean of Asia at INSEAD, analyses the results and implications of the 11th edition of the GTCI through an APAC lens alongside two of its authors: L. Felipe Monteiro, Academic Director of the GTCI and Senior Affiliate Professor of Strategy, and Paul Evans, Emeritus Professor of Organisational Behaviour. Perhaps the most notable theme from the 2025 report is the shifting value of human capabilities, where soft human-centric skills are becoming just as vital as hard digital or technical skills. As AI handles increasingly complex technical tasks, Monteiro and Evans suggest that "generalist adaptive skills" – including leadership, innovation, creativity and entrepreneurship – will increasingly take centre stage.With AI, we are going to be thinking more and more about what are the human-centric skills. Things like critical thinking, the soft skills and, above all, the ability to adapt. - Felipe Monteiro Reflecting on the high ranking of certain countries such as Singapore, Switzerland and the Nordic nations, Evans points to the strength of their integrated ecosystems, where government, business, educational institutions and labour organisations work together to solve problems using a forward-looking approach. He warns that without this deep ecosystem collaboration and a long-term vision, even technologically advanced nations may struggle to implement the systemic changes required to thrive in today’s disrupted global economy.I've got this profound belief that ecosystem collaboration is going to become more and more important. But there are signs, worrying signs, that in a lot of countries, it's breaking down. - Paul Evans That potential danger is highlighted in a concerning trend identified in the report, where several upper-middle-income countries, such as Malaysia, Brazil and Mexico, appear to have reached a "talent plateau" or “trap". Despite making good headway in the earlier stages of their development, these countries have seen their progress stall as they find themselves squeezed between high-innovating top-tier countries and lower-income countries with cost advantages. Levels of optimism for the future were mixed among the three speakers. However, they agreed that greater collaboration, an increased emphasis on lifelong education and the ability of individuals to learn and adjust on the job will be vital if countries and companies hope to successfully navigate the uncertain waters of the next five years – and beyond. ]]></description>
                  <pubDate>Tue, 24 Feb 2026 23:45:10 +0000</pubDate>
                  <guid isPermaLink="false"> 48376 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/career/how-talent-can-thrive-ai-driven-world#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-02/shutterstock_2692081645.jpg?itok=zO-sN-DX" type="image/jpeg" length="976398" /><dc:creator>Sameer Hasija</dc:creator><dc:creator>L. Felipe Monteiro</dc:creator><dc:creator>Paul A. L. Evans</dc:creator></item><item><title>Unravelling the Deep Tensions of Human-AI Collaboration</title>
                  <link>https://knowledge.insead.edu/leadership-organisations/unravelling-deep-tensions-human-ai-collaboration</link>
                  <description> <![CDATA[Whenever new systems alter how information flows, they inevitably alter how people understand their responsibilities, their judgement and their place within the organisation. This is evident in how AI is reshaping the organisational and psychological foundations of work - an insight long emphasised in organisational sociology.We saw this dynamic unfold repeatedly in clinical and diagnostic settings in a study of healthcare professionals across Danish hospitals using AI in clinical imaging. When an AI system becomes part of professional judgement – whether as a second reader, a triage assistant or a risk classifier – clinicians do not simply adopt or reject the technology. Instead, they reorganise their work: pausing at unexpected moments, re-sequencing steps, double-checking outputs or temporarily withholding acceptance until they can reconcile the AI model’s recommendation with what they see. Often misinterpreted as hesitation or resistance, these behaviours are in fact signs of a profound and intelligent process of adaptation. The deeper shifts beneath the surfaceBefore any visible change in workflows appear, the introduction of AI surfaces four deep forces that reshape how expert judgement is exercised. The first force is identity. Clinical professions derive part of their meaning from authorship, from interpreting an image, forming a diagnosis or making a call that matters to a patient. When AI begins to offer its own interpretation, clinicians’ identity becomes unsettled – not because they are territorial, but because expertise has always been anchored in interpretive ownership. AI introduces a new actor into this space. The second force is responsibility, which, in medicine, is not merely a bureaucratic construct; it is ethical and relational. If an AI system contributes to or overrides part of a diagnostic judgement, clinicians must decide who is accountable. The answer cannot be vague or shared in the abstract. Tension arises where accountability is expected to stay with humans when part of the reasoning has shifted to the machine.The third is truth. When AI systems surface patterns or assign confidence scores that diverge from a clinician’s reading, the question is not only “who is correct?” but “how do we integrate two modes of seeing?” Finally, there is trust. This becomes a source of tension not because people are sceptical of AI, but because they must decide when to rely on a system when it cannot fully access its internal reasoning. Trust, in clinical work, is less about comfort with technology and more about understanding its behaviour well enough to make prudent, high-stakes decisions.The tensions felt in practiceTogether, identity, responsibility, truth and trust reshape the psychological terrain of clinical work, giving rise to observable tensions that clinicians experience when AI becomes part of diagnosis and care.Tension between trust and expertise: When an AI system flags a suspicious region a radiologist would normally dismiss or misses something an experienced clinician would have seen immediately, a negotiation begins. It is a negotiation not between a human and a machine, but between two sources of “expertise”. This tension is not about whether clinicians believe in technology; it is about how to honour their professional obligation to judge carefully, especially when two interpretations compete.Tension around responsibility: When a decision is produced jointly, responsibility becomes ambiguous. Clinicians often respond by reordering tasks: they make their own assessment first and consult the AI afterwards, rather than the other way around. They do this not because they distrust the model but because they need to preserve what neuro-cognitive psychologists call the sense of agency: the internal assurance that “I am still the one in the driver’s seat”. In medicine, as in many other fields, this sense of agency is essential to responsibility.Tension around objective prioritisation: AI systems are typically optimised for throughput, speed or statistical accuracy. Clinicians, by contrast, prioritise patient safety, contextual nuance, learning and fairness. These objectives do not naturally align. When AI accelerates work, but clinicians need to slow down, question or widen the diagnostic frame, friction in the system emerges. What may look like inefficiency is often a deliberate safeguard.These three tensions manifest in every clinical environment where AI touches judgement. In response, clinicians have learnt to collaborate with AI in different ways, adopting one of four distinct models.Four models of human-AI collaborationParallel expertise or “dual-track mode” is extremely common in radiology: Clinicians read the scan while AI produces an independent interpretation. The outputs coexist but do not directly interact. This model of parallel expertise allows clinicians to retain authorship while managing identity and truth tensions, making this a safe starting point for integrating AI into practice.The second model is forwarded expertise, or “AI-as-decider mode.” This applies to contexts such as triage tools, automated prioritisation systems or structured decision-support protocols. Here, AI produces the operative decision and the human’s role is to relay or enact it. This is often a rational workflow choice but can generate significant responsibility tension if clinicians are accountable for decisions that they do not fully shape. A third model is augmented expertise or “amplified judgement”. In this mode, clinicians remain in charge of the decision but use AI to widen perspectives or avoid diagnostic blind spots. The machine may highlight areas of interest or provide probability scores that prompt deeper scrutiny. This mode preserves human agency while leveraging AI’s perceptual capacity, thus reducing the three forms of tensions rather than amplifying them.Finally, there is collective expertise, or “co-created judgement.” Here, the clinician and AI contribute different pieces of insight that neither could produce alone. In risk stratification or complex ICU decision support, AI may spot subtle statistical or temporal patterns while clinicians integrate patient history, symptoms and lived context. This shared judgement model is the most demanding but also the most potent form of human-AI collaboration.These four models do not represent stages of maturity. They are the system’s natural adaptations to the deeper forces of identity, responsibility, truth and trust – and the tensions clinicians experience when AI becomes part of the workflow.A more confident way forwardAI may feel disruptive, fast and opaque, especially in clinical settings where the stakes are high. But clinicians already know how to use AI responsibly. They pause when they should pause, question what needs questioning, protect the integrity of their role, and adapt in ways that preserve safety and quality.The real opportunity for leaders – whether in clinical or organisational environments – is not AI adoption but design: redesign workflows, accountability structures and decision rights in ways that allow clinicians to work with AI confidently, safely and professionally.In the clinical settings we observed, the organisations that succeeded did not mandate compliance. They created, more or less formally, space for clinicians to test the system, compare human and machine interpretations, articulate disagreements and raise uncertainties. In one radiology department, AI was introduced as a second reader while clinicians were encouraged to annotate differences and reflect collectively on patterns of disagreement. As trust and understanding grew, so did responsible use – not because of forced adoption, but because clinicians on the ground maintained authorship and preserved a sense of agency while learning how the system behaved.Designing work for the AI era means beginning with “work as done” instead of “work as imagined”. If leaders focus on resolving the deep forces of identity, responsibility, truth and trust, the visible tensions diminish and a new, more capable form of clinical judgement becomes possible. Agency can be protected by defining when and how clinicians may override the model. This calls for the alignment of responsibility with actual decision-making power and performance evaluation to reward the quality of judgement – including prudent overrides – rather than output.When leaders build environments that support these conditions, AI does not erode expertise. It strengthens it.]]></description>
                  <pubDate>Thu, 26 Feb 2026 01:00:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48371 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/leadership-organisations/unravelling-deep-tensions-human-ai-collaboration#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-02/balancing_human_and_ai.jpg?itok=xi8C26Ro" type="image/jpeg" length="725593" /><dc:creator>Astrid Galsgaard</dc:creator><dc:creator>Jens Meyer</dc:creator></item><item><title>The AI Clarity Gap</title>
                  <link>https://knowledge.insead.edu/strategy/ai-clarity-gap</link>
                  <description> <![CDATA[Many organisations today are experimenting with AI, yet few manage to scale it or achieve meaningful business impact. The reason is simple: AI rarely fails because of the algorithm; it fails because of the organisation.Across industries, we see the same pattern: early pilot projects produce promising results yet production deployments stall. Pilots thrive in conditions that do not exist in the real world. They rely on curated data, motivated users and simplified processes. But once AI intersects with the true complexity of an enterprise – fragmented systems, inconsistent data pathways, unclear decision rights and informal workarounds – the promise of the pilot cannot be replicated.This is the AI clarity gap. As we discussed in a recent Tech Talk X webinar, the solution lies in shifting to a new mindset, redesigning workflows and addressing the identity questions that change brings. Where the gap shows upThe first is clarity in data. Pilots often assume a level of consistency and completeness that is divorced from the reality of day-to-day operations. One financial institution built a highly accurate credit-scoring model, only to discover that 30 percent of customer records were missing key fields when the system went live. The model was sound; the data reality was not. AI forces leaders to confront data ownership, quality, accountability and transparency, often for the first time.The second is clarity in workflow. Most organisations operate with two workflows: the one depicted on the org chart, and the one that actually governs decisions. The official workflow is linear and documented; the real one moves through WhatsApp groups, tacit knowledge, undocumented exceptions and influence patterns. In one merger we observed, two banks insisted they had aligned governance. In practice, one used formal approval chains while the other relied on three senior executives making informal decisions in a glass room. When an AI system built on the “official” workflow met the “real” one, the initiative faltered within days. Systems simply cannot align with decision-making they cannot see.The third is clarity in purpose. AI can optimise, but only humans can decide what is worth optimising. Without a clear objective function about what matters, what must be protected and what trade-offs are acceptable, AI systems risk solving the wrong problem. A consumer-goods company learned this when an optimisation tool maximised warehouse efficiency at the cost of team cohesion and safety. Only when leaders clarified the broader purpose did the system deliver sustainable value. How to close the gapClosing this clarity gap is less about technology than about disciplined managerial work. Three actions, in particular, differentiate organisations that progress beyond pilots.First, leaders must surface the real workflow before designing solutions. This means shadowing decisions, examining communication trails, mapping escalation paths and identifying informal influencers. AI cannot be deployed into a workflow that exists only on paper.Second, purpose must be made explicit at the beginning of every AI initiative. Teams need to formally define the objective function (what the system should optimise), the guardrails (what must not be compromised) and the acceptable trade-offs. Without this, AI simply amplifies organisational ambiguity.Third, data accountability must be institutionalised. Critical fields need clear owners, lineage must be documented and data quality metrics must be tied to process owners. AI can only be as reliable as the data ecosystem that supports it.If AI demands clarity, then the essential leadership task is to provide it, not just through technical investment but through courage and strategic discipline. When leaders close the clarity gap – in data, in workflow, and in purpose – AI moves from isolated pilots to systemic impact. It becomes more than a technology project, but a catalyst for organisational reinvention.]]></description>
                  <pubDate>Tue, 17 Feb 2026 08:00:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48361 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/strategy/ai-clarity-gap#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-02/ai_clarity_gap_photo.jpg?itok=p9lIJyCZ" type="image/jpeg" length="331558" /><dc:creator>Benedetto Conversano</dc:creator><dc:creator>Ville Satopää</dc:creator><dc:creator>Benjamin Haddad</dc:creator></item><item><title>Are We in an AI Bubble?</title>
                  <link>https://knowledge.insead.edu/economics-finance/are-we-ai-bubble</link>
                  <description> <![CDATA[Fears of an AI bubble have picked up steam in recent months. Some point to surging AI capital expenditure that lacks corresponding immediate returns. Others have raised red flags about the influx of circular deals between OpenAI and the likes of Nvidia, Microsoft and Oracle, which could be artificially inflating demand. Still others question the sky-high valuations of AI-linked stocks, despite the recent market pullback.Are we in an AI bubble reminiscent of the dotcom era? If so, what are some signs that it may be about to pop? INSEAD faculty analyse how we got here, whether investors have cause for concern and what to pay attention to in the months ahead.The market is pricing in continued exceptional growthBen Charoenwong, Associate Professor of FinanceThe question of whether we are in an AI bubble requires some definition. A bubble exists not only when prices exceed current fundamentals, but when prices exceed what future fundamentals can realistically deliver. It implies that the market is behaving like expected future returns are negative, which can happen when the market is “wrong” or when risk-taking capacity turns from risk-aversion to risk-lovingness – distinguishing one from the other is notoriously difficult.So, bubbles can form when market participants unrealistically extrapolate recent growth rates to the indefinite future, either by ignoring competitive dynamics or when circular validation (rising prices confirming the narrative that justified the price increase) crowds out rigorous analyses of sustainable cash flows.By this framework, the current AI market is not obviously a bubble – yet. Earnings growth has largely matched price increases for AI infrastructure leaders, but the market is pricing in continued exceptional growth for years to come. Whether those expectations prove realistic or represent overextrapolation will determine if current valuations are justified or become the foundation of a correction.There are warning signs. The web of circular financing deals in AI has reached a scale and complexity that warrants serious scrutiny. These arrangements bear uncomfortable similarities to the vendor-financing structures that characterised the late-stage dotcom bubble. Consider the arrangement between Nvidia and OpenAI. Nvidia invests up to US$100 billion in OpenAI. OpenAI uses that capital to build data centres, which are filled with Nvidia chips. OpenAI receives cash to expand, and Nvidia is guaranteed to be the supplier for that expansion. The arrangement between Oracle and OpenAI follows the same pattern.The distinction between "flywheel" and "house of cards" comes down to whether real end-user demand is being generated, or whether money is simply moving in circles. Bull markets and elevated sentiment are forgiving of circular financing in hopes of future growth. But if demand fails to materialise in earnings, these hopes can be dashed. What appears as a virtuous cycle today becomes the mechanism of collapse tomorrow.While markets have focused on generative AI and cloud infrastructure, the next phase of the AI cycle may be physical, comprising humanoid robotics and embodied intelligence. Goldman Sachs projects that the global market for humanoid robots could reach US$38 billion by 2035. This wave is beginning to materialise commercially but seems less of a factor in current valuations compared to the AI darlings – and could be a significant upcoming catalyst for capital investment beyond AI data centres. Investors focused solely on generative AI may be underweighting the longer-term transformation that physical AI represents.Investors are already asking firms to “show me”Lily Fang, Dean of Research and Innovation, Professor of Finance and the UBS Chair in Investment BankingIt’s always dangerous to say, “this time is different”. From a pure valuation perspective, there are some parallels between current market conditions and the dotcom bubble. For example, if you look at the Shiller P/E ratio, which is the ratio of the current price over the trailing 10-year inflation-adjusted earnings of the S&P 500, we are at a level (40) that’s very close to the peak in 1999 (45).But there are some fundamental differences. In the dotcom era, the most expensive stocks were loss-making, newly listed tech stocks that drove up overall market valuation. Today, valuation is propelled by massive – and massively profitable – firms such as Nvidia, Google and their Big Tech peers. These firms’ stock prices have risen a lot, but so have their earnings. Based on the Shiller P/E ratio, they look expensive. However, if you look at the regular P/E ratio of the S&P 500, which is calculated as the current price divided by only the trailing 12-month earnings, then we are at 29 – quite a bit lower than the peak of 45. Though 29 is hardly cheap, it indicates that current earnings are significantly higher than historical earnings, so valuations regarding current and future earnings are more reasonable compared to the dotcom era.That being said, the market wants assurances that its investments are yielding the desired ROI. Investors are already asking firms to “show me”. Recently, both Meta and Microsoft reported top- and bottom-line earnings that beat expectations. But Microsoft’s stock price dropped by 10 percent, while Meta’s rose by 10 percent the next day. Why? Meta was able to convince investors that AI was improving its ad targeting and profitability, whereas Microsoft couldn’t show a clear ROI. Let’s not forget that not too long ago, the market was unforgiving to Meta. In the previous quarter, when it couldn’t show such data, its stock price was duly punished.I think the bubble is less in the listed market and more in pre-IPO AI start-ups. Many early-stage investors, including venture capitalists, will lose money if they continue pouring capital into loss-making, cash-burning AI start-ups with no clear path to profitability. In this sense, there’s a parallel to the dotcom bubble: countless VC firms disappeared after it burst. The valuations of AI firms are eye-wateringBoris Vallée, Associate Professor of FinanceI see many similarities between the current AI boom and the dotcom era. First, there’s no question that we are facing an innovation that will have a profound impact on our economies and societies, much like the internet. Contrast this with the crypto ecosystem, where compelling use cases have been challenging to find. Second, the valuations of AI companies are eye-watering and have risen very quickly, despite the absence of profit for most of these firms. This is reminiscent of the dotcom era. On the other hand, Nvidia is a highly profitable company, which reminds me of the gold rush in some ways – shovel-sellers were the ones with the best business case, given the uncertainty of gold prospection.There’s a key difference between the hundreds of billions that companies are pouring into AI-related capex and the spending that occurred during the dotcom era. Large and extraordinarily deep-pocketed incumbents are participating in and financing the development of AI technologies and capacity, either on their own or by partnering with the most innovative firms. This capex is mostly being shelled out on infrastructure, which should be largely redeployable. This is very different from the dotcom bubble, during which marketing spend, for instance, was particularly large.General academic research has shown that complex financing structures often serve the purpose of hiding risk. My understanding is that the circular transactions we’re seeing aren’t simply providing financing across the supply chain (e.g. a client financing a supplier) but are structured in opaque ways to limit recourse over the general assets of the large tech firms. There are some concerns here that deep-pocketed incumbents are trying to design options for themselves, thereby fuelling the financing of investments while limiting their own downside exposure. So, if things go awry, the losses would be transferred to the financial system.Looking ahead, I’d personally keep an eye on adoption trends by both corporate clients and individuals and the associated revenues, to monitor their willingness to pay. We’ll need to be able to start rationalising both valuations and capex with more precisely estimated cash flows. An interesting parallel is that Nvidia’s market cap, at roughly US$4 trillion, is as large as the entire amount of PE assets under management in the United States. When we think about the footprint of firms under PE ownership, we have to start seeing AI building a similar one.“Strategic necessity” isn’t a blank cheque Lin Shen, Assistant Professor of FinanceOn valuation alone, today’s AI-led market looks hot, but it still doesn’t match dotcom extremes. The current rally is more tightly anchored to profits and cash flow. The Nasdaq-100 is currently trading at a trailing 12-month price-to-earnings multiple of just over 33 vs. around 60 in March 2000.The dotcom peak was more extreme because a large share of market value was tied to companies that hadn’t yet built real earnings or cash flow, so prices were driven more by future stories than by current results. In the current AI cycle, many of the key beneficiaries are cash-flow machines. Nvidia – arguably the emblematic AI winner – reported a record US$57 billion in revenue for its fiscal Q3 2025, up 62 percent year-over-year, and US$23.8 billion in cash flow from operating activities for that same quarter. These numbers make it easier to justify paying up via discounted cash-flow logic, not just “eyeballs”.That same focus on fundamentals emerges in how quickly the market pushes back when the cash-flow story gets stretched by spending. For instance, when Alphabet declared it would target US$175 billion to US$185 billion of capex in 2026 (roughly double 2025’s level), it triggered an immediate sell-off as investors questioned whether AI spend would convert into earnings.Alphabet isn’t alone. The scale of the arms race is now explicit: Big Tech players are estimated to shell out US$660 billion on AI this year, a number that crystallises both the ambition and the risk. The market’s reaction to these capex disclosures – sharp repricing rather than applause – underscores how different today’s mindset is from the late-1990s: Investors aren’t blindly buying a vision; they are policing the cash conversion path.So, why are firms spending so aggressively anyway? Because AI is a “winner-takes-most” game: distribution, proprietary data, model quality and developer ecosystems can compound, and the prize for becoming the default platform is enormous. That belief turns AI into an arms race, with each firm fearing that under-investing today risks irrelevance tomorrow.But the capex-driven sell-offs are a reminder that “strategic necessity” isn’t a blank cheque. The past couple of years were largely about building capacity. Going forward, investors will demand proof of utilisation, pricing power and profits.]]></description>
                  <pubDate>Mon, 23 Feb 2026 02:00:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48341 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/economics-finance/are-we-ai-bubble#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-02/shutterstock_2465304407.jpg?itok=x08Z_HYq" type="image/jpeg" length="934813" /><dc:creator>Ben Charoenwong</dc:creator><dc:creator>Lily Fang</dc:creator><dc:creator>Boris Vallée</dc:creator><dc:creator>Lin Shen</dc:creator></item><item><title>INSEAD Insights: Strong Cultures, Supply Chains and Surprise</title>
                  <link>https://knowledge.insead.edu/leadership-organisations/insead-insights-strong-cultures-supply-chains-and-surprise</link>
                  <description> <![CDATA[From understanding the role of surprise in organisations to finding ways to improve disease tracking and analysis, this month’s selection of recently published research by INSEAD faculty spans a diverse array of topics. Other papers explore how context shapes the effectiveness of fiscal rules; how market-based incentives can drive the energy transition; and how firms with less hierarchical structures manage to get their employees pulling in the same direction.The role of surprise in organisationsFrom Aristotle to Charles Darwin, surprise has been a topic of fascination and the source of many questions in philosophy, the social sciences and beyond. However, a history of inconsistent definitions and fragmented perspectives has led to conceptual confusion, limiting our understanding of how surprise affects individuals in organisations.In a paper published in the Journal of Applied Psychology, Spencer Harrison and his co-authors blend insights from psychology, management and other related fields to provide a comprehensive understanding of surprise in organisational contexts. They explore the cognitive and emotional mechanisms that underlie surprise and identify how key factors – such as organisational memory and emotional capabilities – shape how it is experienced and managed within organisations.Read the full paperImproving disease surveillance in Sub-Saharan AfricaPathogen genomic sequencing is central to disease surveillance, enabling laboratories to track the spread of diseases and inform public health responses. A study by Luk Van Wassenhove and his co-authors evaluates two types of donor interventions aimed at improving underdeveloped pathogen genomic sequencing supply chains in Sub-Saharan Africa: in-kind donations and supply chain management capability-building. The study, published in the International Journal of Operations & Production Management, revealed that although in-kind donations can mitigate acute shortages, frequent use risks creating dependency and suppressing learning. In contrast, supply chain management capability-building brings more sustainable improvements, particularly for laboratories that are unlikely to improve without external support.Read the full paperWell-designed fiscal rules are no silver bulletFiscal rules have been shown to improve government budget balances and restrain debt growth. But while they generally improve a country’s cyclically adjusted primary balance, their impact depends on both the time horizon and the context in which they are adopted, according to research by Antonio Fatás and his co-authors published in the Journal of International Money and Finance.In advanced economies and countries with strong political institutions, the effects strengthen over time. But in emerging markets and developing economies – especially those with weaker institutions – their impact tends to fade as time passes. This suggests that fiscal rules introduced during periods of economic hardship or under highly concentrated political power are often less effective in the medium term.Read the full paperHow firms redeploy assets in response to industry shocksHow should firms respond when a core industry experiences a downturn? Research by Aldona Kapačinskaitė, published in the Strategic Management Journal, examines how energy giants reacted to the 2014 oil price crash. Focusing on oil and gas companies that diversified into wind power, she shows that these firms reduced investment in oil and gas – especially in offshore projects – while increasing investment in wind power.Importantly, firms were more likely to invest in newer, higher-capacity wind technologies when these could be co-located with existing offshore oil and gas assets. This shows how firms facing industry shocks redeploy resources into more promising sectors. However, their willingness to do so may depend on the possibility of leveraging existing assets – meaning that market-based incentives alone may be insufficient to drive the switch to renewable energy sources.Read the full paperThe ties that bind less hierarchical firmsInstead of depending on traditional forms of managerial hierarchy to align the work of employees, can strong cultures – made up of systems of widely shared beliefs and values – do the job? To investigate this, Phanish Puranam and his co-author analysed 1.5 million Glassdoor employee reviews and 42 million professional social media profiles from 23,000 American firms.Their research, published in the Strategic Management Journal, found that organisations with stronger cultures do indeed have a lower proportion of managers to total employees. This suggests that attempts to flatten organisational hierarchies by eliminating layers of managers is more likely to succeed if accompanied by efforts to build strong cultures. This can be facilitated in various ways, including the careful selection and socialisation of employees.Read the full paper]]></description>
                  <pubDate>Mon, 16 Feb 2026 01:10:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48336 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/leadership-organisations/insead-insights-strong-cultures-supply-chains-and-surprise#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-02/shutterstock_2100721102.jpg?itok=afP9-t-x" type="image/jpeg" length="1008987" /><dc:creator>Lily Fang</dc:creator></item><item><title>Diversity in the Workplace: An Unexpected Side-Effect</title>
                  <link>https://knowledge.insead.edu/responsibility/diversity-workplace-unexpected-side-effect</link>
                  <description> <![CDATA[It’s no secret that enthusiasm for DEI (diversity, equity and inclusion) programmes has waned in certain corners of the corporate world. Despite this, and although the jury is still out on diversity’s ultimate impact on a company’s financial performance, many still acknowledge that a diverse workforce yields important benefits.Prior research suggests that exposure to counter-stereotypically successful minority employees – those who defy social stereotypes about their group’s ability to thrive in professional settings – weakens prejudice in the workplace. This also applies to members of stereotyped groups: Seeing women in leadership roles, for example, can reduce women’s own beliefs about gender stereotypes, while Black Americans may exhibit less racial self-stereotyping after exposure to successful Black Americans.Counter-stereotypically successful minorities are generally viewed as having a positive impact on workplace perceptions, attitudes and behaviours. But in our research published in the Journal of Applied Psychology, my co-authors (Daniela Goya-Tocchetto from University at Buffalo and Shai Davidai) and I point to a potential drawback: The presence of such individuals can lead others to believe that an organisation is more diverse than it actually is, and so reduce their support for future diversity-enhancing policies.Standing out from the crowdAccording to the stereotype content model, women and racial minorities are often perceived as less competent than other social groups, and therefore less likely to achieve professional success. As a result, when individuals from these groups do succeed – for instance, by earning high salaries or holding senior leadership positions – they tend to stand out as particularly salient.This matters because people often rely on salience as a shortcut for judging prevalence, a process known as attribution substitution. A classic example is how the vividness of shark attacks leads people to believe they happen quite frequently. In reality, you’re more likely to be the victim of a lightning strike or fireworks.Because counter-stereotypically successful women and racial minorities tend to be highly visible in corporate settings, we hypothesised that their mere presence would inflate perceptions of organisational diversity. We further predicted that this effect would be weaker for individuals from minority groups stereotyped as highly competent (e.g. Asian Americans, which are considered a model minority in the United States). Finally, we expected inflated diversity perceptions to be associated with lower support for initiatives aimed at improving workplace diversity.Perception vs. realityWe tested these ideas across four studies. The first examined whether firms led by women CEOs were perceived as more gender diverse. We found that participants consistently overestimated the gender diversity of a firm’s board of directors when the CEO was a woman – an effect that didn’t appear when the CEO was a man.In our second study, conducted with US residents, we compared reactions to firms with either counter-stereotypically successful Black American employees or equally successful but non-counter-stereotypical Asian American employees. Although participants overestimated the share of non-White workers in both conditions, exposure to Black American employees led to significantly higher estimates of racial diversity. This suggests that the effect of being exposed to high-performing minorities on diversity perceptions is stronger when that success contradicts prevailing stereotypes.Our third and fourth studies investigated the downstream consequences of inflated diversity perceptions. In the third study, participants reviewed information about the gender composition of each quartile of a company’s pay distribution, then estimated the organisation’s overall gender composition. Participants overestimated the true level of gender diversity when women were more heavily represented in the top salary quartile. What's more, we discovered early evidence that exposure to counter-stereotypically successful women was associated with larger gender pay gaps within the firm.In the fourth study, participants were significantly more likely to overestimate the proportion of women employees in an organisation when the latter were successful (i.e. described as earning high salaries). This, in turn, reduced participants’ willingness to hire a woman for an open role.The impact of inflated diversity perceptionsPerceptions of diversity shape a wide range of outcomes, including support for policies aimed at increasing representation. Our findings suggest that organisations with counter-stereotypically successful women or racial minorities can be perceived as more diverse than they truly are. This can foster an unrealistically rosy view of diversity that may not only discourage organisations from addressing deeper, systemic workforce issues, but could also reduce support for initiatives such as hiring additional members of underrepresented groups.The implication is clear: Without careful attention, firms may inadvertently distort perceptions of diversity and undermine support for the very individuals they aim to advance. Our results also show that “quick fix” diversity strategies, like hiring a few counter-stereotypically high-performing minority employees, can undermine motivation to invest in longer-term solutions.How can organisations that are committed to building a genuinely diverse workforce navigate these risks? Prior research suggests that providing complete, accurate statistics on gender and racial pay gaps and overall representation can help offset the distortions created by counter-stereotypical exemplars and better align perceptions with reality. This is especially important at key decision-making points, such as when firms are considering new diversity policies or hiring practices, given that inflated diversity perceptions can undermine support for such initiatives.At the same time, leaders should be mindful that some well-intentioned efforts may backfire. For instance, excessive or highly performative messaging around diversity – be it via the company’s website or social media accounts – may cause fatigue or scepticism, ultimately reducing engagement rather than building it.Success is the solution, not the problemCrucially, the solution isn’t for outstanding minority professionals to downplay their achievements or feel responsible for the unintended consequences of their success. Rather, part of the problem has to do with scarcity. Despite progress, underrepresentation persists: Black employees hold only 8 percent of top executive roles in the most valuable US public corporations, and women account for just 11 percent of Fortune 500 CEOs. As long as women and racial minorities remain underrepresented in the upper ranks of organisations, their success will continue to stand out. But as more of these individuals move into senior roles, their presence is likely to become normalised and feel less exceptional. Stereotypes about their competence should weaken, reducing the risk that a few high-profile success stories distort diversity perceptions.In that sense, while our findings highlight an important bias, they also point to a hopeful possibility: As diversity in leadership gets more common, it may also become less remarkable – laying the groundwork for broader acceptance and more equitable, inclusive organisations.]]></description>
                  <pubDate>Thu, 19 Feb 2026 02:20:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48316 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/responsibility/diversity-workplace-unexpected-side-effect#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-02/shutterstock_2659422195.jpg?itok=d0RDrYXe" type="image/jpeg" length="993183" /><dc:creator>Asher Lawson</dc:creator></item><item><title>Is AI Really Going to Take Your Job?</title>
                  <link>https://knowledge.insead.edu/career/ai-really-going-take-your-job</link>
                  <description> <![CDATA[Whether it’s fielding basic customer complaints or generating sophisticated code, it seems like today’s AI tools can just about do it all. But as the technology keeps growing at a rapid clip, so have employees’ fears over its ability to make them redundant.These concerns aren’t unfounded. Companies including Microsoft, Amazon and Salesforce have cited AI adoption as the reason behind over 50,000 job cuts in the United States in 2025. More recently, The Wall Street Journal reported that AI companies are asking subject-matter experts to train their models in everything from astronomy and psychology to video editing and financial investing. OpenAI has also apparently been asking contractors to upload projects from other jobs to train and prepare its AI agents for office work.Will humans be left in the dust as AI takes over? I’d argue that history and simple economic analysis suggest otherwise, and that conventional wisdom about the technology annihilating jobs is almost certainly wrong.A look at past technological advancesLet’s take it back to the 1980s, when CT scans became appreciably cheaper thanks to advances in medical imaging technology. The worry was that everyone employed in radiology departments would lose their jobs. But per the US Bureau of Labor Statistics, employment in this area didn’t fall – it actually grew, leading to a shortage of radiology technicians.What happened was that more people could afford getting CT scans. More doctors recommended them, and insurance companies were more willing to pay for them. The resulting efficiency led to an uptick in CT scans, which created jobs, not killed them. It increased the need for human oversight, unearthed more complex cases (that subsequently needed treatment) and led to the emergence of new specialties like interventional radiology. Another example is transportation. Ride-sharing apps like Uber commodified mobility when they first burst onto the scene in the late 2000s, dramatically dropping the cost of a ride. You’d expect that many drivers, especially taxi drivers, would lose their jobs. Instead, lower costs exploded demand. People who typically walked or took buses started hailing rides for short trips, while new sectors like food delivery emerged. Globally, the number of ride-hailing drivers ballooned from thousands to millions. Sure, some taxi drivers had to become Uber drivers, but overall employment in the ride-sharing and taxi industry grew substantially between 2015 and 2023.We can go back in history to the Industrial Revolution to see the same pattern. Until the 1870s, prior to the invention of something called the Bessemer process, buildings were limited to materials like wood, brick and iron, which couldn't support extreme heights or withstand environmental stressors without costs becoming prohibitive. The Bessemer process of producing steel cost-effectively changed that, allowing us to build much taller buildings. But it also led to fears over job displacement in traditional ironworking trades, which manifested in strikes, union resistance and shutdowns. Although there were some shifts in labour types – and steel production reduced the need for highly skilled ironworkers – new jobs sprouted and demand for overall construction labour increased.Jevons paradox: efficiency in actionThe idea behind this, known as Jevons paradox, was first articulated in 1865 by economist William Stanley Jevons. He noticed something about the use of coal in England: New steam engines made coal use more efficient, but instead of reducing the amount of coal required, it made coal cheaper and more versatile. Industries ramped up their consumption, deploying it for everything from powering factories and ships to heating homes. Demand for coal skyrocketed, and so did jobs.Let’s apply this logic to AI and knowledge work. AI agents make non-routine tasks like coding prototypes, drafting reports and brainstorming campaigns cheaper and more accessible. This means that a small start-up can use AI to avoid shelling out significant resources on bespoke software, allowing it to tackle more projects, build more apps and input more features. Demand for coders doesn't drop. It rises because customised software is being used by more companies. Beyond writing code, coders will now do different things, such as testing code generated by AI, as well as providing oversight, integration and advanced tweaks.You can see a similar trend in the graphic design industry. Prior to the invention of AI, graphic design was time- and cost-intensive. Now, companies can create ads quickly and easily. Graphic designers have to use AI to generate more ads within a shorter timeframe, but the need for human collaboration, good taste and a strong connection to the brand and audience will remain – things that AI has yet to fully replicate.Pre-AI, you often had to shell out a substantial fee for basic legal documents. With AI making legal work cheaper, the demand for it will increase, and lawyers will have to assume a more advisory role rather than just executing legal documents. The same goes for mergers and acquisitions, where AI has brought down the cost of deal-making. Bankers will then act as advisers – not so much as writers of contracts and covenants. There will be more deals happening, and more work for bankers, but in a different capacity.How to give yourself an edgeSo, take heart. All is not lost, not by a long shot. That’s not to say that the transition to an AI-infused world will be easy, or that there won’t be pain in the form of layoffs and reskilling. Some may gripe that a few companies and individuals seem to be reaping the spoils of AI, while the rest of the population is left having to adjust to a new technology that, frankly, many of us didn’t ask for.But what's clear is that AI will likely affect every job in one way or another, and employees must adapt as companies increasingly incorporate the technology into how they do business. McKinsey, for instance, is piloting an AI skills test as part of its recruitment process by asking applicants to use the firm’s AI tool, Lilli, to complete certain tasks. Candidates are evaluated on how they prompt the chatbot and what they do with the information it generates.How can you ensure that you gain from AI? Here are some easy-to-implement tips:Embrace it as a multiplier: Use it for drafts and ideation, then add your human edge. By using AI to improve your performance and get work done better and faster, you can free up time for higher-level tasks. Upskill in oversight: Learn how to manage AI agents – which can be extremely useful if you use them well – such as by taking courses on prompt engineering specific to your job or industry. You can even ask AI agents to summarise the latest knowledge about how to make the best use of AI for your context.Spot opportunities: Ask yourself, “What in my field will be unlocked if this task gets 10 times cheaper?” Capture these opportunities, either in your existing job or by becoming an entrepreneur. Indeed, it’s not difficult to create custom AI agents that specialise in a particular job function.Lead on AI adoption: If you’re running a team, normalise AI in workflows. Use it to share knowledge more quickly and solicit ideas from your employees on how to better use it.It’s true that AI will eliminate some jobs and reshape many others. But it will also increase demand for products and services that were previously cost-prohibitive, creating opportunities that didn’t formerly exist. If you figure out how to use the technology to your benefit – rather than resist it – you’ll be better positioned to navigate the changes and uncertainty ahead.]]></description>
                  <pubDate>Tue, 10 Mar 2026 01:15:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48261 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/career/ai-really-going-take-your-job#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-01/shutterstock_2528661673.jpg?itok=CgOy8sN5" type="image/jpeg" length="1036903" /><dc:creator>Nadav Klein</dc:creator></item><item><title>How Do We Excuse Our Bad Behaviour?</title>
                  <link>https://knowledge.insead.edu/marketing/how-do-we-excuse-our-bad-behaviour</link>
                  <description> <![CDATA[Have you ever splurged on luxury products you couldn’t afford, turned down a donation request or indulged in unhealthy food while on a diet? You’re not alone. Even with the best intentions, people can behave in ways that – by their own and societal standards – are “bad” and have negative consequences for themselves, others and society.Most of us want to see ourselves as good, virtuous people, but the choices we make can sometimes clash with this desired self-image. How do we reconcile this tension? In a paper published in Consumer Psychology Review, I introduce the Pathways for Avoiding Self-Sanction (PASS) model, which explains how consumers can escape guilt or self-punishment when they fall short of their own standards for good behaviour.What does it mean to be virtuous?Many consumers don’t have a problem with maintaining their standards for good behaviour – they may enjoy nutritious food or genuinely want to help others in need. However, at times, some people may feel tempted to buck these standards, leading them to act in ways they perceive as wrong, sinful or simply bad.These situations typically involve either self-control or moral conflicts. The former arise when people must choose between short-term pleasure and long-term goals. Here, examples of virtuous choices include picking a salad over a pizza, saving money instead of overspending, and investing in experiences that support personal growth rather than pure enjoyment. In all these cases, consumers must forsake immediate gratification to benefit their future self.Moral conflicts come into play when consumers need to weigh others’ or society’s welfare against their self-interest. Most people would like to view themselves as having good moral character. To maintain this self-image, they must avoid actions that signal disregard for the well-being of others and society and behave in ways that show they care – think volunteering, donating to charity and buying ethical products.As defined in my study, someone is virtuous when they meet their subjective self-standards for self-control and moral character. But what happens when consumers are tempted to violate these standards by streaming content illegally instead of paying for it or skipping their weekly workouts to relax on the couch? Failing to resist temptation – and realising they’ve fallen short of their own standards – triggers self-sanction, where people judge themselves as lacking self-control, acting out of self-interest or even behaving unethically.Avoiding self-sanctionWhen individuals knowingly breach their own behavioural standards, they anticipate the self-sanction that follows. In order to enjoy the benefits of these violations without the guilt, they may employ pre-emptive strategies.This is where the PASS model comes in. It’s centred on the idea that consumers have a personal self-sanction threshold – the point at which they shift from seeing themselves as good to bad. These thresholds vary by individual and context. For instance, people may have higher diet and fitness standards when they need to lower their cholesterol or stricter moral standards when they know they’re being observed by others.The PASS model assumes that people start on – and are motivated to stay on – the virtuous side of the boundary. When tempted to engage in behaviours that could push them to the “bad” side, they may follow one of three routes to avoid self-sanction: the self-based path, the behaviour-based path or the threshold-based path.Self-based pathConsumers may raise the level of their perceived virtue before breaking a standard, creating a buffer that lets them engage in bad behaviour afterwards without crossing their self-sanction threshold. By evaluating themselves as more virtuous initially, they feel entitled to – or even deserving of – a lapse they may otherwise avoid.They can achieve this through virtuous actions (e.g. donating to charity before overspending), by perceiving themselves as making strong progress towards worthy goals, or by convincing themselves that they’ll engage in future good behaviour. These strategies elevate their initial self-assessment of how virtuous they are, so that subsequent violations don't push them into “bad” territory.Behaviour-based pathAlternatively, people can reinterpret bad behaviour so that it no longer crosses their self-sanction threshold. Some downplay the severity of their actions by stretching or redefining the rules around what counts as a violation. Others shift how much responsibility they feel for their actions or take steps to minimise their agency (e.g. as my other work shows, physically avoiding someone who seems to be soliciting donations).Another strategy is to downplay the perception of harm caused by their actions, either by discounting negative consequences or exaggerating positive ones. As an example, people could justify not giving money to charity by questioning whether the organisation would use it effectively, or emphasise the benefits of sweatshop labour as a source of income and development in emerging economies when tempted by fast fashion. They could also partake in unethical consumption, such as returning used clothing against store policy, to “punish” the company if its corporate stance on a sociopolitical issue is opposed to their own.Threshold-based pathConsumers can also adjust their standards for what counts as a violation. In these cases, they may acknowledge the negative consequences of their behaviour, but either temporarily or permanently move their self-sanction threshold to allow for it.They may do this on special occasions, on designated “cheat days”, if they believe they’ve experienced unfair suffering, or if they anticipate future regret about missing out on having fun in the present. People also take cues from those around them: seeing others litter, overeat or engage in disorderly acts might make them more likely to perceive this as acceptable. They may even encourage others to join in, thereby moving their own threshold to allow them to indulge as well.Getting away with itThe PASS model offers an explanation for why consumers purchase unsustainable products, support unethical business practices, overeat, overspend and watch trashy reality shows – even if these actions contradict their self-standards for virtue. It sheds light on how, in a world where consumers face increasing pressure to be ethical – be it by adopting more sustainable habits, supporting minority-owned businesses or advocating for moral causes – individuals can avoid “walking the talk” without suffering self-judgement. It might also explain why people give in to other harmful consumption patterns, such as excessive drinking, drug use or gambling.Beyond consumer behaviour, the model also illuminates how industry practitioners avoid self-sanction for ethically questionable behaviour. Marketers or salespeople, for instance, may employ strategies to justify violating users’ privacy or misrepresenting attributes of their products, while maintaining a positive view of themselves.The PASS model provides a flexible, comprehensive framework to explain how individuals navigate the evolving realm of virtuous consumption. By adopting the strategies outlined above, consumers can give themselves permission to engage in behaviours they might otherwise avoid – essentially allowing them to have their cake and eat it, too.]]></description>
                  <pubDate>Thu, 05 Mar 2026 01:30:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48196 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/marketing/how-do-we-excuse-our-bad-behaviour#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2025-12/shutterstock_329043617.jpg?itok=xffQoFVO" type="image/jpeg" length="920413" /><dc:creator>Stephanie Lin</dc:creator></item>
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