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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 11:31:34 +0800</lastBuildDate>
        <language>en</language>
        <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>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>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>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>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>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>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>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>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>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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