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        <title>INSEAD Knowledge</title>
        <link>https://knowledge.insead.edu</link>
        <description>The business school for the world</description>
        <lastBuildDate>Fri, 04 Sep 2026 00:44:57 +0800</lastBuildDate>
        <language>en</language>
        <item><title>A Healthier, More Personalised Future for Asia?</title>
                  <link>https://knowledge.insead.edu/technology-innovation/healthier-more-personalised-future-asia</link>
                  <description> <![CDATA[Healthcare is big business. The industry is currently worth over US$8 trillion globally, with US$4 trillion of that value centred in Asia. In this edition of “The INSEAD Perspective: Spotlight on Asia” podcast series, Sameer Hasija, Dean of Asia at INSEAD, speaks with Suhina Singh, co-founder of Singapore-based Jonda Health, about how the region’s healthcare industry currently stands at an inflection point, driven in large part by the impact of AI.As evidence of this ongoing transformation, Singh, a former clinician, points to the fact that US$37 billion was spent on AI in healthcare in 2025 alone. This figure is growing 40% year-on-year, with AI's share of the sector expected to roughly double to over 30% by 2030. The technology promises to impact the entire continuum of care – from drug discovery, where research timelines are compressing from years to months, to major improvements in diagnostics, treatment and clinical workflows.Focusing on Asia, the demographic pressures of rapidly ageing populations in markets like Japan and South Korea, and youthful, digitally native populations elsewhere, are acting as twin accelerators of AI adoption in the region, albeit for different reasons. With issues ranging from the spiralling costs of treating chronic conditions to a shortage of clinicians in markets such as India, AI is seen as a means of filling the demand-supply gap.Governments are already embracing this potential: Singapore has rolled out a clinical AI scribe that’s being used by more than 2,000 healthcare providers, while South Korea has implemented a voucher-based system so that digital health startups can pay for clinical data processing, purchase, and analysis of national health data. One major challenge, though, is that most of these innovations are concentrated in wealthier markets, with rural and lower-income areas in the region still underserved. Health, for me, is a fundamental right – Suhina Singh Reflecting on her own experience building JondaX, a health-data transformation engine that converts information into structured, AI-ready formats, Singh is candid about the technical challenges of relying on large language models in a heavily regulated field. In such a high-stakes environment, patient privacy, quality control and data accuracy remain non-negotiable. On the flip side, the promise is a future where the individual receives an unprecedented level of care, from frictionless data access, through to highly personalised diagnosis and treatments. And it seems as if parts of Asia are already well on the road to such a future. Taking a broader perspective, Hasija ends by reflecting that AI’s potential to transform a system as complex as healthcare should act as inspiration for other industries, including education, to pursue the same genuine, technology-enabled customer-centricity in their own sectors.]]></description>
                  <pubDate>Tue, 25 Aug 2026 00:51:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48921 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/technology-innovation/healthier-more-personalised-future-asia#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-08/personal_healthcare_asia.jpg?itok=_B6e4B2f" type="image/jpeg" length="483315" /><dc:creator>Sameer Hasija</dc:creator><dc:creator>Suhina Singh</dc:creator></item><item><title>Your Best Sales Tool Isn&#039;t Your Tech Stack</title>
                  <link>https://knowledge.insead.edu/strategy/your-best-sales-tool-isnt-your-tech-stack</link>
                  <description> <![CDATA[The global business community has moved beyond the initial shock of the disruption unleashed by the generative AI revolution and are now looking at how to embrace this new reality. However, the siren song of AI’s potential to transform business models and grow profits has not faded; it has simply changed its tune. In the realm of B2B sales, sales teams are now facing increasing pressure to hand over the wheel to autonomous agents that promise "frictionless" commerce.In a world where AI-driven efficiency has become a baseline commodity, we constantly observe its strategic failure in the making. This approach overlooks the fact that deep, lasting human-to-human relationships have become the only source of sustainable competitive advantage. Drawing on a longitudinal study of 10,000 business relationships and 1,000 transformation projects, we argue that the ultimate differentiator in B2B sales is the ability to use AI to augment the three dimensions of the Triple Fit Strategy: planning, execution and resources, while doubling down on what’s irreplaceable: human judgement.Beyond the transactional trapIn the current landscape, the divide between two growth models has widened to a chasm:Product strategy: Centres on the ability of AI algorithms to autonomously deliver speed and volume. In this model, the seller is often invisible, leading to a transactional race-to-the-bottom where loyalty is non-existent.Customer-centric strategy: Centres on the customer’s business ecosystem. It recognises that the seller’s growth comes from first enabling success for the customer.The Triple Fit Strategy enables a firm operating on a B2B level to focus on a customer-centric strategy by mandating alignment or “fit” with its customer across the areas of planning, execution and resources so that they operate as though they belong to one company.1. Planning fit: guarding strategic dialogueIn an era of automated forecasting, most companies lose their human edge in planning. While AI serves as a powerful engine for analysis, it can’t replace the nuanced understanding required for value co-creation.AI excels at analysing vast amounts of sales data to forecast demand. However, strategic direction – especially one shared between two distinct corporate cultures – is not just an exercise in data-crunching. AI cannot build the mutual trust necessary for a joint three-year vision. Strategy is what both sides commit to, and it can only emerge through nuanced, real-time dialogue.It’s important to recognise that managing a contact is not the same as deepening a relationship. While AI can meticulously map organisational hierarchies, it cannot replicate the rapport built during spontaneous, unscripted moments like coffee breaks or social events, where unspoken needs and unforeseen opportunities can often be voiced.Authenticity can also be hard to replicate when it comes to communication between seller and customer. AI can automate reports and provide real-time translation, but it cannot imbue words with the emotional weight that signals honesty. With the sudden rise of AI-generated content, customers are getting better at recognising and rejecting synthetic sincerity.  AI can play a key role in informing the question, such as "What if we were one company?" but it’s up to the human team to own the mindset, the transparency, and the co-ownership required to spark new blueprints for growth.A case in point is a logistics firm that wanted to deepen its relationship with a home-improvement retailer in the United States. AI tools identified growth potential in last-mile delivery optimisation. But the actual shift – co-investing in new logistic centres –only materialised after cross-functional workshops revealed shared expansion goals into urban markets.2. Execution fit: orchestrating in a messy realityExecution is where strategy meets the reality of present-day supply chains and fluctuating markets. AI is clearly a powerful tool for identifying potential bottlenecks and developing better automated processes. However, it cannot reconfigure value chains that involve diverse corporate cultures. Over-automation can lead to a calcified or brittle operational interface, where the seller is no longer able to adapt to the customer’s specific, non-standard requests.In the same way, legal, financial or IT AI tools may be excellent at improving an organisation’s efficiency in drafting boilerplate contracts, but they struggle to navigate complex agreements involving risk appetite and new business models. They cannot make the critical judgement calls required in uncharted territory where no training data or technical guidelines exist.AI can be good at identifying what is wrong or misaligned in a B2B partnership, but it cannot develop genuine, one-to-one relationships that are the basis of long-term business success.A European chemical supplier used AI to track engagement with a large customer across departments. However, when a product issue occurred, it wasn’t the automated alerts that preserved the relationship, it was the trust built between the supplier’s regional business director and the customer’s COO over years of collaboration and personal rapport. In the debrief meeting, both parties credited this human intervention for bringing things back on track.3. Resources fit: the human x-factorDedicating resources, be it people, capital or capabilities, are proof of an organisation’s commitment to B2B partners, and this is where the human "x-factor" is vital to create real trust. AI struggles to take on the role of “trusted advisor” – someone who can act as a sounding board for the customer and takes risks based on intuition, not just data. Human willpower is also needed to break silos and incentivise collaboration across cultural differences. True cross-functional support requires leaders to make tough resource-allocation calls that are perceived as fair by all stakeholders.In the AI era, companies must upskill employees to become "orchestrators". They need to be the "human-in-the-loop" overseeing AI. This means people should focus on performing roles that demand empathy, mentorship and creativity rather than mere task execution. AI doesn’t mean what it says, and customers can sense when a message lacks sincerity or personal relevance. A medical device company faced R&D delays due to supply chain bottlenecks at a component supplier as predicted by AI. Rather than hiding behind over optimistic forecasts, the supplier's account manager proactively convened an all-hands meeting with joint product teams and proposed revised timelines and contingency plans. This honest conversation reinforced trust and prevented the partnership from stalling.Action plan for leadersSo how can leaders harness the power of AI without losing their personal connections? We suggest a three-step action plan:Embrace AI for speed, humans for vision: Use AI to handle the data-driven heavy lifting, but reserve strategic orchestration for your top sales talent.The 90-day litmus test: Select three key accounts to discuss where AI can aid joint execution. If the AI suggestions don't meet the human intuition test, pivot immediately.Upskill for empathy: Shift your training budgets from mastering tools to orchestrating relationships. The most valuable employees are the ones who can navigate the grey areas where algorithms fail.Orchestrate, don’t automateWith AI becoming a permanent fixture, the danger is not the technology itself, but the erosion of the human bridge that connects two organisations. Solid B2B relationships are built on mutual value creation across planning, execution and resources. This type of orchestration requires levels of collaboration and trust that only humans can deliver.Over-reliance on AI risks turning "garbage-in-garbage-out" into a frightening reality where business relationships lose both their soul and their strategic value. The future of B2B belongs to the leaders who can confidently decide what to automate and, more importantly, what to leave to the humans.]]></description>
                  <pubDate>Sun, 09 Aug 2026 23:28:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48901 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/strategy/your-best-sales-tool-isnt-your-tech-stack#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-08/b2b_sales_stack.jpeg?itok=8O4BVsEp" type="image/jpeg" length="858071" /><dc:creator>Christoph Senn</dc:creator><dc:creator>Mehak Gandhi</dc:creator></item><item><title>Can Brands Desired by Humans Appeal to AI Too? </title>
                  <link>https://knowledge.insead.edu/marketing/can-brands-desired-humans-appeal-ai-too</link>
                  <description> <![CDATA[As large language models (LLMs) increasingly influence consumers’ product discovery and purchase decisions, unpacking how brands are “seen” and recommended – what some call bot psychology – by AI gatekeepers has become critical. Many executives are asking: "How do we get our brand recommended by AI?" A more important question may be: "Does AI understand what makes our brand valuable?" Evidence suggests that AI systems value utilitarian and explicit cues in interpreting meaning and generating answers to consumers’ prompts. Google's own recently published guidance suggests that in order to be seen by LLMs, brands should create clear and compelling content within a legible structure. But this goes against the long-standing playbook of many aspiration and luxury brands, from sports to fashion and even high-end hospitality. Such brands tend to tap symbolic and implicit associations rather than playing up explicit, functional attributes. Cultural schema (“artistic association is valuable”), shared assumptions (“higher means greater status”), embodied metaphors (“thin bottles signal elegance”) and subtle cues (e.g. slow pacing of ads) are used to convey scarcity and heritage to humans, not what can be read at face value. Can AI systems correctly interpret such intuitive and often subconscious cues, read between the lines, and integrate the unsaid? As we wrote in a recent article in Harvard Business Review, the likely answer, based on a series of studies, is “no”.Testing AI brand desirabilityOur experiments systematically tested AI's response to four well-documented luxury cues known to boost products’ desirability: higher physical positioning, association with art, spacious display and slender design and packaging. We used original or near-identical stimuli from prior studies to allow direct comparison. Three models were tested: ChatGPT 5.1, Claude Sonnet 4.5 and Gemini 3 Pro. We sampled each model 150 times for each stimulus, which included chocolates, jewellery, tableware, beauty products, watches and more.The models handled explicit cues such as a stated brand name and the word "luxury" well enough. Everything else was another matter. Unlike humans, the LLMs didn’t prefer products placed at a higher physical position or presented in a more spacious setting (see charts below). Art associations had less impact on LLMs than on humans, and AI models seemed to prefer photographs – especially those of celebrities popular on YouTube or Reddit – over paintings.In short, shape- and proportion-based cues barely registered. Minimalist, spacious visual environments produced negative responses. More white space was correlated with lower perceived value. Our experiment indicates that visibility and recognition play a central role in shaping luxury perceptions among LLMs compared to humans, but algorithms misinterpret luxury attributes and mischaracterise what makes them desirable. The implicit, hedonic cues that luxury brands have relied on for decades to build desire can lead to weaker, not stronger, visibility in AI-generated responses. How brand context shifts AI judgmentIn our second experiment, the three LLMs sized up six car brands – Alfa Romeo, BMW, Ferrari, Mercedes, Porsche and Tesla – against either a plain background or a luxurious one featuring a gilded-framed Van Gogh painting. In all, we generated 5,400 evaluations of the LLMs’ willingness to pay for each brand.The results revealed that Mercedes benefited from the luxury context across all three models whereas Porsche was penalised. Ferrari, meanwhile, divided opinion: ChatGPT assigned it lower value in the luxury context, Gemini was unmoved and Claude valued it more highly. Apparently, for brands whose identity is built around performance rather than heritage, showing up in a luxury context appears to create resistance rather than endorsement.Two further takeaways stand out. First, consumers’ intuitive understanding that a Ferrari occupies a different category from a BMW doesn’t register with AI unless it’s made explicit, such as through rankings, tier labels (e.g. “premium” or “luxury”), comparison tables and awards. Otherwise, models may treat brands far apart on the luxury hierarchy as equally prestigious, which impacts which brands get surfaced and recommended.Second, the models apply different interpretive lenses (see chart below) to the same material. A content strategy calibrated for one model can backfire on another. Brands need to test the same asset across multiple LLMs, identify where models agree and where they diverge, and adjust accordingly.A new luxury marketing playbookLuxury marketing was built on human psychology, but the future will increasingly require that brands design for machine psychology as well. Luxury leaders must close the meaning gap. Rethinking strategy through the famous 4Ps of marketing – product, price, place and promotion – provides a useful organising frame.Product: AI systems favour explicit descriptors over implicit cues. This means craftsmanship, provenance, materials and design intent must be articulated. Brand executives should audit their asset inventory of campaign imagery, product descriptions, taglines and visual language, and assess each for AI readiness. Ask yourself: How much meaning would survive if the implicit signals were removed? Audit your brand through the eyes of AI. Many organisations have mapped customer journeys to the last detail but have little understanding of how AI currently describes, categorises and recommends their brand. How does AI position your brand vs. your competitors? Does it classify you as luxury, high-end, innovative, value-oriented or something else entirely? Where is it getting that information from?An AI context strategy brief, developed alongside traditional brand guidelines, should specify how products should be described, in what use contexts, and relative to which competitor set. A jewellery brand, for instance, should leverage engagements, anniversaries and significant personal milestones to give models the context they need to infer value.Price: Willingness-to-pay experiments give brand managers a practical monitoring tool. If one model labels a product "overpriced" while another calls it "premium", it’s a signal that the cues surrounding the product aren’t sufficient for the model to infer intended positioning. Identifying where AI systematically undervalues a brand or inflates a competitor enables executives to make corrections before wrong or inadequate information shapes consumer decisions.Promotion: Luxury brands risk losing control of meaning when functional product attributes are left ambiguous. In a separate analysis of ski brand Atomic, we found that AI interpreted the rigidity of the brand's skis – a core performance attribute – as a drawback. The solution is to probe the models to understand what descriptors they currently attach to your brand. Define the intended reading, identify the gaps, and use precise, high-status language consistently across owned and earned channels to close them. Third-party content, including reviews, mass-market comparisons and off-brand associations, continues to be indexed long after a brand has moved on strategically. Managers should conduct a comprehensive audit of existing content and ensure that AI retrieves the most accurate information about the brand when consumers ask for guidance. Placement: Brands’ owned websites are only a fraction of the equation. Our research (presented in the chart below) shows that roughly 80% of an LLM’s citations come from third-party e-commerce platforms, news media, blogs, Reddit threads and YouTube videos. This broader ecosystem is the new frontline for positioning your brand. In an AI-mediated market, visibility is about more than just keywords – it's about understanding machine psychology to ensure that meaning is never lost in translation. This understanding entails treating the content ecosystem holistically, and tailoring content strategies to the algorithms of different platforms.A new mandate for brand leadersLuxury brands have spent decades building what might be called a visual vocabulary that tells a consumer, without spelling it out, that their product is different from the rest. AI systems aren’t immersed in that vocabulary. Luxury brands’ pressing task in the age of LLMs is to find the layer of explicit language that preserves meaning without dissolving the mystique, making the brand readable without rendering it ordinary. This article is adapted from “LLMs Misunderstand Luxury Brands. Here’s How to Optimize Your Content Strategy for AI” published in Harvard Business Review.]]></description>
                  <pubDate>Tue, 04 Aug 2026 10:25:03 +0000</pubDate>
                  <guid isPermaLink="false"> 48896 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/marketing/can-brands-desired-humans-appeal-ai-too#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-08/shutterstock_2382283643_1.jpg?itok=QC_Wr4Gh" type="image/jpeg" length="402149" /><dc:creator>David Dubois</dc:creator><dc:creator>Allison R. Hess</dc:creator><dc:creator>John Dawson</dc:creator><dc:creator>Akansh Jaiswal</dc:creator></item><item><title>How Your Choice of AI Shapes Your Decisions</title>
                  <link>https://knowledge.insead.edu/responsibility/how-your-choice-ai-shapes-your-decisions</link>
                  <description> <![CDATA[AI is used at work by over 65% of managers in the United States. According to a 2025 Resume Builder survey of more than 1,300 managers, 94% use AI tools to assess their direct reports, which in turn, shape decisions about raises, promotions and redundancies. Of these, one in five allow AI to make final decisions with no human review. More worryingly, fewer than a third have received any structured ethical coaching on how to use these tools responsibly.Many managers approach AI as if it were a particularly capable search engine: enter a problem and receive an answer. But research into how large language models behave in workplace ethical dilemmas reveals something more consequential: These tools function as advisory systems, and each one has a distinct set of tendencies that shape the advice it gives.Our study tested eight leading AI models against a series of controlled workplace scenarios based on the same dilemma: a colleague showing signs of possible impairment in a responsibility-critical role. This dilemma was presented across different professional contexts and risk levels, from healthcare to aviation, finance, legal services, IT, construction and public transit, while systematically varying the level of risk, evidence and reporting obligations. Models were asked to choose between handling the matter informally or escalating it through institutional channels. What emerged were four distinct advisory personalities.Four advisors, four approachesChatGPT behaves like a seasoned managerial advisor. Responses are consistently the longest and most structured, grounding recommendations in organisational policy and established process. Even when recommending formal HR reporting, the framing is routine and proportionate rather than alarming. Escalation is presented as an extension of governance, rather than an emergency.Gemini operates more like a systems analyst. In lower-stakes scenarios, responses emphasise transparency and workplace culture. In high-risk settings, particularly in healthcare and aviation, recommendations shift rapidly toward institutional intervention and formal safety processes, and more sharply as risk severity increases than in almost any other model tested.Mistral functions more as an informal peer. Across the entire test, formal escalation was never once recommended. Even in scenarios involving potential patient or aviation safety concerns, responses focused on direct conversations, increased oversight and local problem-solving. This makes Mistral valuable when situations are genuinely ambiguous, but potentially prone to systematic under-escalation when stakes rise.DeepSeek performs like a compliance officer reviewing legal exposure. Advice is clinical and detached, heavily focused on accountability and liability, with references to concepts such as fitness for duty and institutional responsibility appearing far more frequently than in most other models. Well-suited for regulated environments where defensibility matters, but potentially less suited to situations where interpersonal care is important.The point isn’t whether one of these “personalities” is correct, it’s that they exist at all. Each model differs in meaningful ways, and most managers do not realise they’re choosing between these personalities when they open a browser.The blind spotsPerhaps the most counterintuitive finding from the research is how little formal rules matter. The strongest and most consistent driver of escalation advice was potential physical harm. As such, contexts such as aviation and healthcare produced substantially higher escalation rates than finance, legal or administrative settings – even when the organisational consequences in those domains were equally significant. Surprisingly, once the underlying risk level was established, adding an explicit formal duty-to-report obligation doesn’t materially change the recommendation. The models appear to be harm-sensitive rather than rule-following.Model configuration adds a further layer of variability that most users don't tend to consider. Switching the same model from a standard mode to a reasoning or thinking mode can produce markedly different advice. When Mistral was moved to a thinking configuration in a high-stakes aviation scenario, the recommendation shifted entirely, from informal handling to immediate escalation. Gemini showed a comparable shift in certain contexts. These aren't edge cases; they suggest model settings may influence advice in ways that users don't anticipate.Reclaiming decision-makingThe variation between models was large enough for the choice of tool to be, in practice, a consequential one. Different systems displayed different escalation thresholds, reasoning styles and responses to the same risk signals. These tools weren't built to be interchangeable. They were built by companies with different cultural contexts, regulatory environments and commercial priorities. A model trained in one institutional context will interpret accountability differently from one trained in another. Managers using AI for decision support may be receiving meaningfully different advice depending on which tool they use and how it's configured.Used well, these tools can extend a manager's thinking. But what they can't replace is contextual knowledge, relationship history and the moral accountability that sits with the manager.]]></description>
                  <pubDate>Wed, 29 Jul 2026 01:00:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48876 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/responsibility/how-your-choice-ai-shapes-your-decisions#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-07/Screenshot%202026-07-27%20at%2016.30.10.jpg?itok=G1Qo-8kk" type="image/jpeg" length="68812" /><dc:creator>Lara Ketonen</dc:creator><dc:creator>N. Craig Smith</dc:creator></item><item><title>Digital Transformation Starts With Data</title>
                  <link>https://knowledge.insead.edu/operations/digital-transformation-starts-data</link>
                  <description> <![CDATA[In this webinar, Enver Yücesan, Professor of Operations Management and The Abu Dhabi Commercial Bank Chaired Professor in International Management at INSEAD, speaks with Nick Miesen, co-author of the “Henkel Adhesive Technologies: The Digital Transformation Journey” case. They discuss how to define a roadmap, choose the right use cases and align digital investments with business goals. The central challenge they explore is one digitally ambitious organisations face – not which technologies to adopt, but how to choose between them when budgets are finite and the options are almost infinite.Miesen is clear that digital transformation starts with people. His first move at Henkel was not to deploy technology but to hire the right team. That same logic extended throughout the organisation. Every business case had to identify a key user – someone who would own the system locally, take responsibility for its adoption and train others around them. Without that human infrastructure in place, Miesen argues, even a technically successful deployment will fail to deliver value. They also discuss the framework Miesen developed to match digital investment with the complexity and value potential of each site, including the "app store" model, which allows production sites to plug into a shared data backbone and select the use cases relevant to them. Miesen goes on to explain the measurement challenges that plagued early deployment, including the difficulty of isolating digital's contribution to savings when dozens of other initiatives were running simultaneously, and the problem of agreeing upfront on what success would look like. At Henkel, these definitions were sometimes only clarified after the fact, which made it harder to prove value and build the internal credibility the programme needed.Miesen’s core takeaway applies well beyond manufacturing: focus on proving value in a few sites to build organisational buy-in before expanding further. Clean, standardised data is the foundation on which everything else depends, including AI, and no amount of sophisticated tooling can compensate for getting that foundation wrong.]]></description>
                  <pubDate>Mon, 27 Jul 2026 10:30:49 +0000</pubDate>
                  <guid isPermaLink="false"> 48871 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/operations/digital-transformation-starts-data#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-07/shutterstock_1294477114_1.jpg?itok=mf8rywx-" type="image/jpeg" length="72926" /><dc:creator>INSEAD Knowledge</dc:creator></item><item><title>Airbus and the Hydrogen Horizon</title>
                  <link>https://knowledge.insead.edu/operations/airbus-and-hydrogen-horizon</link>
                  <description> <![CDATA[Consumer demand, financial viability and a functioning ecosystem are crucial for an innovation to move from the drawing board to a commercially viable reality. That’s according to INSEAD professors Manuel Sosa and Adrian Johnson, in conversation with Harsh Shah, a leading expert on Hydrogen fuel in the aviation sector. In this INSEAD Knowledge podcast, they discussed Airbus’s 2020 public commitment to bring the world’s first hydrogen-powered commercial aircraft to market by 2035.Despite making major strides in developing the technical innovations needed to achieve this aim, Airbus ultimately had to push back their deadline due to challenges beyond their control. Sosa explains that this case underlines the fact that any company introducing innovations needs to consider the desirability, feasibility and viability framework of innovation if it has any real chance of success. While Airbus had overcome many of the feasibility obstacles needed to fly these ‘zero emission’ planes, a lack of strong commitments by airlines to purchase the aircraft made it difficult to push ahead. Their reluctance to commit was driven in part by the cost implications of switching to a new, and currently much more expensive fuel. This was further compounded by the absence of viable infrastructure to produce, transport and store the hydrogen that fuels this new generation of aircrafts.  It's not about sticking to the original plan. You need to be prepared to pivot." – Manuel Sosa As Shah put it, they were faced with a classic chicken-and-egg scenario: Airlines won't commit without infrastructure, and infrastructure won't get built without demand. His solution was greater government intervention and financial incentives that absorbed cost premiums in the early stages, similar to those in other sectors, such as wind energy.For Sousa and Johnson, the broader lesson extends well beyond aviation. Bold public commitments can catalyse an ecosystem – Airbus's announcement helped spark collaborations like the Goliath hydrogen infrastructure programme – but they only pay off if paired with the patience to iterate, and the humility and courage of leaders to adapt without abandoning the goal. As Johnson puts it, pushing back a deadline shouldn’t be seen as failure; it's what learning through experimentation actually looks like when the stakes, and the ambition, are this high.]]></description>
                  <pubDate>Thu, 30 Jul 2026 00:30:39 +0000</pubDate>
                  <guid isPermaLink="false"> 48856 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/operations/airbus-and-hydrogen-horizon#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-07/airbus_hydrogen.jpg?itok=UrvT8chZ" type="image/jpeg" length="833618" /><dc:creator>Adrian Johnson</dc:creator><dc:creator>Manuel Sosa</dc:creator><dc:creator>Harsh Shah</dc:creator></item><item><title>How Technology Is Changing the Work of Management</title>
                  <link>https://knowledge.insead.edu/technology-innovation/how-technology-changing-work-management</link>
                  <description> <![CDATA[For years, the dominant conversation about workplace technology has centred on a single question: Which tasks will machines take over? Although this is an important question, perhaps a more useful one may be: How does technology reshape the way we organise work, not just the way we do it?  This notion sits at the heart of OrgTech – technologies that change how organisations divide work, coordinate teams, allocate decision-making, monitor performance and resolve exceptions. It was also the topic of a recent webinar hosted by Phanish Puranam, The Roland Berger Chaired Professor of Strategy and Organisation Design at INSEAD. He was joined by collaborators Piyush Gulati from University College London and Arianna Marchetti from Singapore Management University, who both graduated from INSEAD’s PhD programme.Their wide-ranging discussion touched on the differences between technologies that boost managerial-based coordination vs. peer-to-peer coordination; how strong culture can lead to as good a level of coordination as vertical authority; and how collaborative work management technologies can reduce managerial intensity and increase decentralisation in organisations. Inevitably, the conversation gravitated towards the impact of AI on managers. Puranam stated that while the future of managers may be in flux, AI could introduce new opportunities. “It may well be the case that managers, going forward, play the role of [teachers]… who are also training and inculcating the skills of their subordinates, not merely monitoring and coaching them,” he said, while admitting that this remains conjecture at this stage. “Another [opportunity] might be a new class of meta-organisational skills where what you’re doing is managing the coordination between humans and [AI] agents, and constantly redefining that division of labour. These are new tasks [that] would actually reinstate the demand for managers.”A lot of the work that my collaborators and I do today is to think about how we can deploy AI in organisations in a way that improves their goal-centricity... without sacrificing human-centricity. Puranam stressed that as organisations harness technology in a bid to improve efficiency and innovation, they should remember that far from just being goal-oriented machines, organisations are also contexts in which people find meaning. “A lot of the work that my collaborators and I do today is to think about how we can deploy AI in organisations in a way that improves their goal-centricity – improves profits, makes them efficient, makes them innovative – without sacrificing human-centricity,” he said. “We shouldn’t forget that the technology is rapidly becoming a commodity… So, what would you differentiate on? It would be through human capital, and that’s the reason why the question of ‘why are we organising’ has to include some account of keeping these organisations human-centred.”]]></description>
                  <pubDate>Thu, 20 Aug 2026 00:50:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48846 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/technology-innovation/how-technology-changing-work-management#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-07/shutterstock_2322484741.jpg?itok=0dZsQc4d" type="image/jpeg" length="936861" /><dc:creator>INSEAD Knowledge</dc:creator></item><item><title>How Your Firm’s Story Affects Its Share Price</title>
                  <link>https://knowledge.insead.edu/strategy/how-your-firms-story-affects-its-share-price</link>
                  <description> <![CDATA[Have you ever noticed how talkative many CEOs can be, and how quickly they seize an opportunity to wax lyrical about their organisation, its products and services, and the markets it operates in? You might think some CEOs have sizeable egos that need to be maintained by seeing themselves featured in prominent media outlets. You might be right.But there’s something else going on as well, and it matters because of how the stock market determines firms’ valuations. In our research published in Organization Science, my co-author Sang Won Han from Sungkyunkwan University and I examine the relationship between how organisations portray themselves and how they’re understood by others, as well as the ways firms can use self-description to their advantage.Differentiation vs. conformityOrganisations tend to want to stand out and define themselves on their own terms, but they can also benefit from meeting the expectations of outsiders. That means navigating a persistent tension between being distinctive and fitting in. There’s a duality at the heart of this process: Firms describe themselves, and external actors (e.g. financial analysts) signal the extent to which they understand and agree with that depiction.Markets can punish narratives that are confusing or inconsistent and, as a result, cause firms to lose value, particularly during periods of technological change. This happens because firms tend to be judged at the categorical level. When they branch out into other areas or industries, their original assessment criteria may not adapt to these changes adequately to reveal their true performance – which, in turn, lowers their evaluation.Our research first set out to confirm that firms operating across different industries indeed face a valuation penalty. We developed the concept of linguistic alignment, which we define as the match between the language that organisations use to describe themselves and the expectations the wider environment imposes on them. This alignment depends on how well the firm’s self-description matches that of comparable organisations as seen through the eyes of financial analysts, whose comparisons and assessments ultimately shape the firm’s standing in the market.We hypothesised that organisations with higher linguistic alignment would see stronger stock market performance. In the case of firms with a multi-industry presence, this could help offset the negative impact of diversification on its stock performance.The role of linguistic alignmentUsing 10-K filings from publicly listed firms in the United States between 1998 and 2021, we compiled a series of self-descriptions by firms. To capture external expectations, we drew on data from the Institutional Brokers’ Estimate System, which provides comprehensive future earnings forecasts and recommendations by financial analysts. We measured linguistic alignment by the co-occurrence of words or phrases in firms’ 10-K filings and the language used in financial analysts’ coverage of those same firms. We determined the extent to which firms spanned multiple industries by counting the unique words used in their 10-K narratives.Our analysis shows that linguistic alignment has a positive effect on firms’ stock market performance. It also confirms that organisations are indeed punished for operating across multiple industries, but that greater linguistic alignment softens the impact of that penalty on share price. But linguistic alignment can shift for two distinct reasons – either because a firm changes how it describes itself or because its audience’s expectations change, or both. We tested which of these forces was driving the results we observed and found audience expectations to be the primary factor: Performance improves when financial analysts adjust their expectations to match a firm’s self-description, not when a firm tweaks its narrative to match expectations.That doesn’t mean organisations have no influence over their linguistic alignment. They can shape it indirectly by crafting self-descriptions that influence and eventually shift what their audience expects, which then drives the performance rewards that come from linguistic alignment. For example, when financial analysts found Shell’s “energy transition” narrative confusing, the company switched to using more focused language emphasising the energy sources in which it has the most expertise. Of course, this process needs an incentive to work. If a closer match between external expectations and how a firm portrays itself benefits the firm, then executives shaping the organisation’s public image have good reason to move towards those expectations. Our findings confirm that this incentive exists: A firm's self-description leads to a convergence of external expectations towards the firm’s description, which, in turn, benefits the firm and further shapes it. The process is one of mutual adaptation, with the firm and its audience gradually converging on a shared account of what the firm is about.Why storytelling mattersFor much of the late 20th century, firm valuation in US markets followed a logic of categorical coherence. In other words, firms were expected to fit cleanly into established industry categories, and those that didn’t were often penalised through poorer valuation. Analysts and investors tend to favour such mental shortcuts, rewarding clarity and conformity over ambiguity or hybrid business models. Value, in other words, came from fitting neatly into a recognisable niche.That structural logic has since given way to something more dynamic and centred on narratives. Today, many firms operate across far broader scopes than their formal classifications suggest, yet are still valued favourably, which suggests that structural conformity is no longer the only path to legitimacy. Indeed, some researchers have argued that investor behaviour is driven as much by compelling stories and shared narratives as by hard data.Although firms can be penalised for spanning multiple categories, they can soften this considerably through how they choose to tell their story. In practical terms, our findings offer leaders a way to gauge whether the market understands and buys into their firm’s self-description, and how improving linguistic alignment can boost firm value. This matters most for organisations that operate across multiple categories. Although firms can be penalised for spanning multiple categories, they can soften this considerably through how they choose to tell their story.Consider Apple. It operates across industries – from consumer electronics and computer software to digital entertainment and fintech. Despite this breadth, the public generally perceives the company’s story as coherent because of a common thread across most of its products: stylish, consumer-friendly, easy-to-use offerings built for everyday users. The key word here is “perceive”. Apple has woven a narrative that the market broadly accepts, and it is this acceptance that allows financial analysts to confidently recommend the stock to investors, which drives up its share price. So, whatever motivates CEOs to keep talking about their organisations, there’s a real payoff at stake. A firm’s stock market valuation isn't just a reflection of the value it creates. It’s also a reflection of the story it tells – and how well that story lands with the audience judging it.]]></description>
                  <pubDate>Thu, 03 Sep 2026 02:00:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48841 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/strategy/how-your-firms-story-affects-its-share-price#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-07/shutterstock_2263343989.jpg?itok=lXoS8bXQ" type="image/jpeg" length="965651" /><dc:creator>Henrich Greve</dc:creator></item><item><title>What Your Board Needs to Know About AI</title>
                  <link>https://knowledge.insead.edu/strategy/what-your-board-needs-know-about-ai</link>
                  <description> <![CDATA[AI is forcing boards into unfamiliar territory, and many are unsure where to start. Theodoros Evgeniou, Professor of Technology and Business at INSEAD, has spent years advising companies on AI strategy and governance. In the latest episode of the INSEAD Explains Governance series, he sets out what boards need to understand about AI, from assessing organisational readiness to building a governance framework fit for the technology.Setting the ambitionOne of the first things a board must understand, says Evgeniou, is how disruptive AI is likely to be for their industry. In some sectors, it’s a fundamental threat to the business model; in others, it’s largely about improving efficiency. That assessment shapes everything downstream, including where resources should go – be it towards foundational capabilities like data and infrastructure or more experimental, longer-term ideas.Moving too fast can unsettle a workforce already anxious about AI's implications, while moving too slowly risks losing ground to competitors. Evgeniou stresses that AI deployment is not value-neutral. Every choice involves trade-offs, such as between privacy and security. Which of these matter most depends on the company and its industry.Before any of this, boards need a clear picture of their own AI readiness. Evgeniou points to three core enablers worth assessing: data, infrastructure and talent. Understanding the gap between where a company stands today and where it needs to be is the essential starting point.Broad AI literacy matters more than deep expertise in any single area. Building the blueprintAI governance is a blueprint that every company needs to build for itself, working through various layers: principles, policies, roles, processes and the tools to support them. Banks, Evgeniou notes, have effectively been doing a version of this for years through model risk management. He also points out that AI governance isn't just about managing the downside risks but helping organisations capture the upside.For board members specifically, Evgeniou makes the case for broad AI literacy over narrow technical expertise. Understanding the technology matters, but so does comprehending its organisational impact, its relationship to data, the regulatory landscape and the geopolitical dimensions of the AI stack. A working knowledge across all of these areas, he argues, is more valuable than deep expertise in just one.]]></description>
                  <pubDate>Tue, 21 Jul 2026 05:00:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48831 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/strategy/what-your-board-needs-know-about-ai#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-07/shutterstock_2657007993_0.jpg?itok=WP-xpioW" type="image/jpeg" length="178595" /><dc:creator>INSEAD Knowledge</dc:creator><dc:creator>Theodoros Evgeniou</dc:creator></item><item><title>Quantum’s Transistor Moment</title>
                  <link>https://knowledge.insead.edu/technology-innovation/quantums-transistor-moment</link>
                  <description> <![CDATA[In a webinar hosted by Andrew Shipilov, Professor of Strategy at INSEAD, Liam Goodman, Chief Technology Officer at Fidamy, referred to 2025 as the "transistor moment" for quantum computing – a tipping point where years of incremental progress have quietly shifted the technology from experimental to strategic. Investment in the sector grew 50% year on year, with public funding across more than 30 countries already passing US$40 billion.Where a classical computer works through a problem step by step, a quantum computer can explore vast numbers of possible solutions simultaneously, making it a game changer for problems that are simply too complex for today’s machines. Zulfi Alam, Corporate Vice President of Quantum at Microsoft, pointed to chemistry as a prime example: Quantum computing’s ability to simulate atomic interactions at scale could reshape drug discovery and much of what we manufacture.They were joined by Freeke Heijman, founder of Quantum Delta Netherlands and Vice President of the European Quantum Industry Consortium, and Laura Converso, Principal Director at Accenture Research, at the recent Tech Talk, where they discussed why quantum computing is no longer a long-term bet, and what business leaders should be doing about it now.The cost of waitingThe companies best placed to benefit from quantum computing are not waiting for the technology to mature before they act. Building the algorithms, training the talent and identifying the right use cases take years, and those who start late will find the ground already taken.There is also a security dimension that makes waiting dangerous. Heijman warned of a risk known as “harvest now, decrypt later”, the idea that sensitive data being encrypted today could be stored by bad actors and decoded once quantum computers become powerful enough to crack current security standards. Thus, the time to start migrating to quantum-safe encryption is now, not when the threat becomes visible.Importantly, quantum computing is not just about adopting a technology but being part of an ecosystem. No single company can do this all alone; instead, consortia, partnerships and national programmes all have a role to play.]]></description>
                  <pubDate>Thu, 09 Jul 2026 00:34:29 +0000</pubDate>
                  <guid isPermaLink="false"> 48821 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/technology-innovation/quantums-transistor-moment#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-07/shutterstock_1635458275_1_0.jpg?itok=7tHcwKu1" type="image/jpeg" length="400778" /><dc:creator>INSEAD Knowledge</dc:creator></item><item><title>What AI Adoption in Global Firms Really Looks Like</title>
                  <link>https://knowledge.insead.edu/leadership-organisations/what-ai-adoption-global-firms-really-looks</link>
                  <description> <![CDATA[We often think of AI as a technological revolution that will transform industries, disrupt jobs and change the nature of competitive advantage. Inside organisations, though, it is unfolding in a much more complex and less predictable way. In many firms, AI is still more narrative than operational reality, making it hard to move from discussion to meaningful action.This tension was revealed by senior leaders of global firms at a recent workshop in Paris co-organised by INSEAD’s Stone Centre for the Study of Wealth Inequality, its counterpart at University College London, and the Centre for Economic Policy Research (CEPR). The senior executives, who came from professional services, retail, creative, consumer goods and other sectors, spoke on condition of anonymity. They said AI is reshaping how work gets done, how decisions are made, and how value gets created and distributed, but the real challenge is how firms make sense of the AI transformation. Underneath the surface of experimentation and investment are deeper questions around organisational design, human capability and the future of work. From what the participants said, four key tensions stood out.1. What does AI adoption really mean?Across organisations, the term “AI adoption” is widely used but understood differently. For some firms, AI is primarily a tool for incremental productivity gains, improving efficiency at the margins. For others, AI is empowering a more fundamental shift: Employees across functions are building solutions themselves, enabling innovation by many rather than a select few.In other contexts, particularly in knowledge-intensive sectors, AI raises questions beyond productivity: think authorship, data origins and credibility. When outputs are generated by models trained on vast amounts of external data, it raises the question of who creates value and how that value should be recognised.A key insight of the workshop, which had some 30 participants, is that shared language and AI literacy are closely connected. When organisations use the same term internally to describe fundamentally different phenomena, this can lead to misalignment. Leaders may see a coherent strategy, while employees experience a fragmented set of initiatives with no clear purpose, resulting in a gap between AI ambitions and their limited connection to everyday operations. People in the same organisation need both a common understanding of what AI is and the knowledge and confidence to use it effectively. Firms that begin with language and learning – that develop a shared, working understanding of what AI is, what it’s not and what it’s expected to achieve – tend to move more effectively from experimentation to impact.The challenge for firms is to... integrate AI in ways that continue to develop human capability. The implication for leaders is simple but important: Before scaling AI, define it not just in technical terms but in strategic ones. What specific problems is the technology meant to solve? Where is it expected to create value and where is it not? What does success look like, and over what timeframe?This isn’t just about semantics. One workshop participant from a not-for-profit described a split within their organisation between AI enthusiasts and sceptics, noting that there’s often “a mismatch between what AI does and the sentiment we give to it”. Without a shared understanding, the lack of common meaning becomes, in the words of the participant, “prohibitive to the discussion”, let alone to working together.2. Overcoming organisational bottlenecksIn addition to addressing ambiguity, the issue of organisational readiness is equally important. Access to tools, data or technical expertise is often cited as a barrier to AI adoption. In practice, however, most firms can access powerful systems, but the bigger challenge is integrating them into existing structures and workflows.This reflects the “J-curve” effect seen in previous waves of technological change. Initial adoption is typically followed by a period of adjustment, during which productivity may decline. One leader of a firm providing coaching and mentoring services augmented by AI offered a candid account of this transition. Their firm spent 18 months in a building phase that generated little visible output. “It was an unproductive time,” they acknowledged, “but gains take time to realise.” To its credit, the firm understood it as a necessary investment – and has since seen returns. The J-curve demands patience as much as capability.Several participants described this phase as lasting 12-18 months, often with limited visible gains. Understanding the J-curve not as failure but as a necessary phase helps organisations manage expectations and sustain commitment. Firms that persevere are those that invest in complementary capabilities, including training, redesigning workflows and change management. As AI enables more widespread experimentation, employees across levels and functions can test, adapt and build. This calls for a shift in both structure and mindset, with leaders supporting a degree of decentralisation and ambiguity while staying aligned with strategic priorities.3. Spreading AI’s benefitsPerhaps the most immediate effects of AI adoption are being felt in the human experience of work. AI is creating divisions, as employees who are able and willing to use these tools effectively often gain a disproportionate advantage. The rest risk being left behind due to lack of access, training or confidence.These differences aren’t only technical but behavioural and cultural. Some people are more inclined to experiment, while others are more cautious, particularly when the technology challenges existing ways of working or raises concerns about quality and reliability. Firms that invest in broad-based AI literacy and create environments for experimentation tend to distribute benefits more evenly. One academic participant argued that leadership needs to take responsibility. “If we don’t do anything, it will increase inequality,” they argued. Indeed, the divergence in hiring trends suggests that a gap is already opening between the AI-savvy and the rest.There’s also a deeper dimension related to meaning and identity. In many roles, especially those involving creativity or human interaction, AI raises questions about authorship, autonomy and the value of work. Some employees may find meaning in tasks that others are quick to automate, highlighting the need for careful and inclusive decisions about where AI is applied.Additionally, AI is increasingly being used in people management processes such as hiring. Although this can improve efficiency, it also raises new challenges, such as making it harder to distinguish among candidates when many now use AI tools to craft their CVs. This places greater emphasis on human judgement and the interpersonal aspects of recruitment. For leaders, the challenge is to ensure that AI adoption does not create a tiered or hierarchical organisation. This is not only a question of fairness but also of performance.4. The nature of human work and developing talent As AI systems become more capable, they are increasingly performing tasks that were previously central to many roles. Activities such as drafting reports, writing code and conducting analysis are now automated or significantly accelerated. Roles are evolving, with a shift from execution to oversight and judgement. A useful way to think about this shift is in terms of “semi-autonomy”. Rather than fully delegating decisions to AI, organisations are designing systems that augment human judgement while preserving human responsibility for consequential decisions. The role of the human is moving from being inside the loop to being on top of it.This has implications for organisational design and talent development. In professional services firms, junior employees were traditionally trained in detailed analytical work under supervision. If AI reduces the need for such work, how will expertise be developed and assessed? At the same time, the skills that differentiate individuals are changing. The ability to think critically, connect distinct pieces of information and exercise judgement in uncertain situations is becoming increasingly valuable. Ditto interpersonal skills such as empathy, communication and leadership. Several leaders expressed concern about “intellectual offloading”, where individuals defer to AI outputs without fully engaging with the underlying reasoning. The risk of skipping foundational steps was captured vividly in the discussion. If the pace of AI-assisted work is prioritised over depth of understanding, organisations may accumulate blind spots. Panellists noted that new graduates may still need fluency with underlying data even if they no longer produce it directly. As one participant put it, some young people today can’t change a light bulb because the task has always been handled for them. By the same logic, if detailed knowledge is never acquired, it can’t be drawn on when things go wrong. The challenge for firms is to ensure that speed doesn’t come at the cost of the judgement that only arises from getting one’s hands dirty. In other words, integrate AI in ways that continue to develop human capability. This may require different training, redesigning career pathways and creating new opportunities for experiential learning. Leading through the transition Beyond internal organisation, AI is pushing firms to rethink how they create and capture value. In professional services, the traditional model based on billable hours is increasingly under pressure and gradually being replaced by value-based pricing.At the same time, sources of competitive advantage are evolving – proprietary data, strong client relationships and the ability to build ecosystems are becoming critical. Beyond generating solutions, firms must have the data and context to create something that scales and delivers long-term value.Leading through this transition requires deliberate, not just fast, action. Organisations should be clear about what they are trying to achieve, thoughtful about which decisions should remain human, and intentional about how AI aligns with organisational design and purpose.]]></description>
                  <pubDate>Mon, 29 Jun 2026 01:00:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48761 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/leadership-organisations/what-ai-adoption-global-firms-really-looks#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-06/shutterstock_2600708729_1.jpg?itok=6NS4K7t1" type="image/jpeg" length="124448" /><dc:creator>Ridhima Aggarwal</dc:creator><dc:creator>Mark Stabile</dc:creator></item><item><title>Want More Innovation? Stronger Trade Secret Laws Help</title>
                  <link>https://knowledge.insead.edu/strategy/want-more-innovation-stronger-trade-secret-laws-help</link>
                  <description> <![CDATA[The conversation around intellectual property (IP) is typically dominated by patents and copyright, but there is one often overlooked pillar: trade secrets. Trade secrets represent over US$5 trillion in value and roughly two-thirds of all intangible assets in the United States alone. They are also, by definition, invisible. A study Colleen Cunningham and I recently published in Management Science provides rare, granular evidence on how firms manage confidential information in high-stakes environments. Our findings suggest that stronger trade secret protection can induce firms to innovate even when no registered IP, like patents, is claimed. What’s more, it can spur firms to share innovations with other companies.A quasi-natural experiment in the oil patchManagers face a persistent dilemma that can be called a “knowledge leakage trap”: The more valuable an innovation is, the riskier it may be to use, because deploying a new process or recipe requires sharing it with employees, contractors or partners who may eventually take that knowledge to a competitor. One CEO we interviewed put it bluntly: “Our trade secrets are constantly getting passed around.”This fear results in firms holding back their most advanced inventions. The problem is particularly acute in sectors where employee mobility and uneven bargaining power between partners are common. Besides oil and gas, such sectors include chemical, cosmetic and food products, semiconductors, and advanced manufacturing.Our study looks at over 47,000 hydraulic fracturing wells in the US between 2014 and 2018. In certain US states, stringent regulations require firms to disclose the chemical ingredients in their fracturing fluids unless those ingredients are substantiated trade secrets. This creates rare documentation of actual trade secret use at the level of individual wells.The Defend Trade Secrets Act of 2016 (DTSA) served as the external shock to trade secret protection for our study. This was the first US federal legislation to create a unified jurisdiction for trade secret cases in the country. Legal experts at the time described it as the most significant expansion of federal intellectual property law in at least 30 years. By comparing how firms in states with weaker pre-existing trade secret protections responded to the DTSA against those in states where protection was already stronger, we could isolate the effect of strong protection.We found that in states where the legal change represented the biggest shift – Arkansas, Louisiana and Pennsylvania – the proportion of wells using at least one trade secret ingredient rose by 22 percentage points after the DTSA came into force. The share of ingredients kept secret per well increased by 3.7 percentage points (from a baseline of 2.2%). Not only did firms introduce new secret ingredients at higher rates, but they also didn’t use fewer disclosed novel ingredients or reduce their patenting activity. Wells with trade secret ingredients produced, on average, 26% more oil and 6% more gas than those without.Perhaps the most counterintuitive finding was that firms ostensibly shared more secret information with other firms after the law changed, as measured by sourcing trade secrets from third parties. In other words, firms trusted that the legal environment would help protect them from leaks. The conventional assumption is that stronger trade secret protection means more hoarding. Our data suggest the opposite may be true.What this means for executivesSecrecy tends to work best where reverse engineering is difficult, when the pace of technological change is fast, and where patenting is not the automatic default. Chemical formulations, algorithms, manufacturing processes and production techniques all fit that profile.Treating secrecy as an afterthought to a patents-first strategy means leaving significant value unprotected and, in many cases, unused. Our research also suggests that secrecy and patenting aren’t mutually exclusive. Increased trade secret use among the firms in our sample did not come at the cost of disclosed innovation. Instead, firms used more secrets and introduced more novel disclosed ingredients, and didn’t patent less. Treating secrecy as an afterthought to a patents-first strategy means leaving significant value unprotected and, in many cases, unused. In fact, rather than viewing trade secrets as a passive alternative to patents, executives can use them as a catalyst for experimentation and competitive advantage. Our study suggests several ways to do so:Leverage trade secrecy in high-leakage environments: Stronger legal frameworks like the DTSA allow firms to increase trade secret use, particularly in regions where non-compete enforcement is low or competition is stiff. The European Union’s Trade Secrets Directive serves a similar purpose.Prioritise secrecy for "complex recipes": Inventions that involve a combination of ingredients or processes – which are harder to reverse engineer but valuable and hence tempting for a partner to misappropriate – benefit most from trade secret protection.Secrecy as a catalyst: Managers should exploit legal protections to move innovations out of the lab and into real-world applications where they can reap immediate ROI. Contrast this with patenting, where approval could take years. To a large extent, this is why firms in the business of fast-moving technologies such as software have relied heavily on secrecy and trade secret protection. An example is the recent pivot by Alphabet’s DeepMind to more secretive measures.Complement patenting with secrecy: Secrecy doesn't have to replace patenting or other types of disclosures. In fact, we found that firms often increased their use of both disclosed and secret novel inputs post-DTSA. This suggests that a robust secrecy strategy can enhance a firm’s broader innovation portfolio.A book chapter I recently co-wrote provides further detailed advice to managers on how to create and capture value from their business secrets.Secrecy and openness can complement each otherPrior research has suggested that stronger reliance on secrecy could dampen the flow of knowledge that feeds invention, leading to fewer patents filed and less disclosures for rivals to build on. Our findings suggest that the picture is more nuanced. What we observed was more experimentation, not less. Bolstered by stronger legal protection, firms deployed novel recipes, sourced more broadly, disclosed new inputs more, and didn’t retreat from patenting. Secrecy and openness, it turns out, are not as opposed as one might think.]]></description>
                  <pubDate>Mon, 15 Jun 2026 01:00:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48726 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/strategy/want-more-innovation-stronger-trade-secret-laws-help#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-06/shutterstock_2642632623_1.jpg?itok=mwDecZvN" type="image/jpeg" length="193808" /><dc:creator>Aldona Kapačinskaitė</dc:creator></item><item><title>The AI Maturity Pyramid</title>
                  <link>https://knowledge.insead.edu/strategy/ai-maturity-pyramid</link>
                  <description> <![CDATA[Many executives talk about AI as if it’s a force that will arrive and transform the business on its own. In the philosopher Daniel Dennett’s terms, they’re waiting for a skyhook: a miraculous lift that suspends the hard work of change from nowhere, requiring little redesign of roles, workflows, incentives or governance. But AI deployment isn’t only about deciding which technology to purchase – it’s an organisational change project. The highest level of AI maturity, where true strategic differentiation arises through new and defensible business models, can’t be reached without mastering the foundational stages. In short, organisations need to climb what we call a maturity pyramid.Level 1: Individual productivityAt the base of the pyramid, AI enhances individual productivity. Knowledge workers use generative AI to do everything from drafting reports and summarising documents to analysing spreadsheets and generating code. Engineers rely on coding assistants, marketers use AI to produce campaign variations and analysts automate initial research.Besides speeding up tasks, AI can improve the quality of output and decrease the cognitive load of users. But although the impact is real, gains are localised. AI use improves efficiency at the margins without fundamentally changing how the organisation creates value. And since such tools are widely available, competitors can adopt them quickly. The result is cost parity, not differentiation.Level 2: Group productivityThe second level shifts the focus from individuals to teams as AI becomes embedded in collaborative workflows. At this level, systems summarise meetings automatically, support deliberation and consensus formation, track action items, route tasks across departments and retrieve institutional knowledge in real time. Information flows more smoothly and coordination improves, letting teams move faster.As with level 1, competitors can implement similar solutions. Although collaboration becomes more efficient and effective, the underlying business model remains unchanged, and the organisation becomes better but not fundamentally different.Level 3: Business process efficiencyAt level 3, AI is integrated into core operational processes: think banks automating underwriting decisions, retailers using demand forecasting and manufacturers deploying predictive maintenance systems. These gains can be significant as costs decline, error rates drop and responsiveness improves, allowing organisations to scale more effectively. Take Yinson, an energy infrastructure and technology company. It's embedded a strong digital core across its operations, integrating real-time operations data and making it usable across various workflows. AI helps the company improve efficiency and reduce accident rates by offering real-time analytics for predictive maintenance, route optimisation and automated management.But even this level is largely defensive. It lets firms compete more efficiently within existing industry structures without necessarily altering what customers value or how revenue is created.Level 4: Business model transformationWhile success at the first three levels of the pyramid is a source of advantage, it’s likely to be transient, as it’s more easily replicable. The highest level of the pyramid is qualitatively different. At this stage, AI doesn’t simply improve how work is done but reshapes how value is created and captured.Consider Rolls-Royce, historically a producer of jet engines, which now increasingly sells “power by the hour”. Airlines pay for uptime, not ownership. This performance-based model predates AI, but predictive analytics and AI-enabled monitoring make it viable at scale. The company shifted from being a capital-equipment manufacturer to a long-term service partner with recurring revenue.Or take banking and financial services firm DBS. Since 2014, it systematically built AI and data capabilities into its operational core. Then, a qualitative change happened, emerging from the operational and data capabilities DBS built patiently across years. The bank began launching consumer marketplaces for the likes of cars, property and travel, generating revenue from facilitating major transactions rather than just financing them. DBS’s business model and competitive positioning fundamentally changed.Competitive advantage rests not only on algorithms (which are increasingly considered a commodity), but on data, redesigned workflows, human capital and organisational capabilities that competitors can’t easily copy. That’s why level 4 cannot be mandated into existence – it emerges from mastery of the levels below.Why firms stall before transformationMany leaders are tempted to leap straight to transformation. However, vision alone, in the absence of capability, can’t reshape the business model. Without widespread AI fluency and operational integration, there is the risk of top-down strategy becoming disconnected from execution. Most importantly, without buy-in from employees, no change initiative – which is what AI adoption is – can succeed. Yinson’s leadership didn’t treat AI as a pilot or a collection of disconnected experiments, but as an organisational journey: build broad fluency and workflow muscle first (levels 1 and 2); use that base to redesign core processes (level 3); and, only after that, make selective large-impact business-model bets (level 4). Had leadership jumped straight to a large transformation bet, it would likely have stalled due to insufficient distributed AI fluency.At level 1, Yinson’s emphasis was on widespread individual adoption and capability-building. In our survey of over 300 employees, respondents reported using AI across 6 out of 10 work activities on average. This breadth matters because it creates shared fluency and normalises experimentation as a default way of working.At level 2, the focus shifted from “I use AI” to “we use AI in how we work together”. Examples cited by employees include embedding AI into team routines, such as generating post-discussion action items and handover templates. Recognising these patterns and automating such repeatable tasks turn isolated productivity wins into reliable collaboration practices.With that foundation in place, the company could expand into level 3 opportunities that sit within operational and functional workflows. In fact, among Yinson employees who proposed substantive AI opportunities, around 26% pointed to ideas that scale up current operations or make them more efficient. This solid base provides Yinson a pathway to level 4, where AI reshapes value creation and capture by enabling differentiation-oriented activities. Top-down or bottom-up adoption?A common debate around AI adoption is whether it should be top-down or bottom-up. But this is a false choice, because the answer is both – though with distinct roles. The problem with the skyhook is not that it comes from above; it’s that it has no basis to be hung. In reality, the value of AI requires an anchor on which to be mounted: one built on clear decision rights, trusted data pipelines, model governance, accountable process owners and metrics that distinguish novelty from impact. When those anchors are missing, top-down AI initiatives become theatre and bottom-up efforts remain local experiments that don’t translate into organisational capability.Leadership must create enabling conditions, which include investing in widespread AI literacy and infrastructure, setting guardrails and defining criteria for success in scaling initiatives. Without this scaffolding, experimentation remains fragmented. At the same time, innovation can also flow upwards, whereby employees experimenting at the individual level generate insights and teams embedding AI into daily workflows identify improvement opportunities.Seen this way, the crux of the question isn’t about top-down or bottom-up; it’s about which level of the pyramid your company is currently on, what must be built to reach the next level, and who owns and leads that process.Advantage comes from strong foundationsConsider Accenture. Its AI journey didn’t begin with proclamations about radically new consulting business models. Instead, the company announced that all 700,000 employees would become AI literate. Why? Because even for a firm that helps others transform, level 4 rests on levels 1, 2 and 3. Before AI can reshape client offerings, it must reshape how consultants work. Before it can redefine value capture, it must become embedded in everyday workflows. Before strategy shifts, behaviour must change.In AI, as in mountaineering, you don’t leap to the summit. You climb.]]></description>
                  <pubDate>Wed, 17 Jun 2026 02:30:00 +0000</pubDate>
                  <guid isPermaLink="false"> 48616 at https://knowledge.insead.edu</guid>
                  <comments> https://knowledge.insead.edu/strategy/ai-maturity-pyramid#comments</comments>
                <enclosure url="https://knowledge.insead.edu/sites/knowledge/files/styles/panoramic_large/public/2026-05/shutterstock_2742406687.jpg?itok=H7i2AL5X" type="image/jpeg" length="1010968" /><dc:creator>Vivianna Fang He</dc:creator><dc:creator>Phanish Puranam</dc:creator></item>
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