• . And yet, for many leadership teams, the sense of organisational progress remains stubbornly unchanged.
• Teams appear busy, outputs multiply, updates arrive faster and in more polished forms. Yet progress at the organisational level feels uneven and fragile.
• High-performing individuals have always been capable of outpacing the systems around them. What AI changes is the scale and visibility of this mismatch.
• Meetings fill with material but resolve little. Strategy documents proliferate without increasing alignment. Initiatives stall because nobody is quite sure who has the authority to make the final trade-off.
• Roles that seemed clear on paper turn out to rely heavily on personal interpretation and historical relationships. Processes designed for predictability reveal how much they depend on shared context rather than formal steps. Authority that was exercised tacitly becomes a bottleneck when decisions arrive faster than consensus can form. Accountability, already diffuse in many large organisations, becomes harder to trace when work is produced collaboratively and iteratively.
• As individual work accelerates, organisational performance is constrained less by technical capacity and more by social dynamics
• Better outputs do not guarantee shared understanding. Leaders are often presented with polished artefacts that conceal unresolved disagreement or divergent assumptions.
• More content does not create clearer intent.
• From a leadership standpoint, this is often where AI begins to feel disappointing. The technology works, but the organisation does not improve.
• One reason social friction is frequently misdiagnosed is that it rarely announces itself directly. Leaders experience it obliquely, as drag, noise, repetition, or a persistent sense that effort is not compounding.
• For CEOs, social friction often appears as a gap between strategic intent and organisational motion.
• Before AI, this showed up as slow execution or inconsistent prioritisation. With AI, it shows up as acceleration in many directions at once.
• A useful micro-technique here is to articulate not only what the organisation is trying to achieve, but what must not be optimised away.

• COOs tend to experience social friction as interruptions to flow.
• Before AI, this was often managed through experience and informal fixes. With AI, those hidden seams become stress points as activity speeds up.
• A practical micro-technique is to treat escalation as a designed feature of the system rather than a sign of failure.
• or CHROs, social friction often surfaces as ambiguity around accountability.
• These patterns existed long before AI. What AI changes is the visibility of contribution, making it harder to distinguish between effort, output, and responsibility.
• One useful micro-technique is to separate responsibility for decision quality from responsibility for decision outcome.
• L&D leaders frequently encounter social friction through learning that fails to compound.

• Before AI, this was frustrating but familiar. With AI, the risk is that individual learning accelerates while organisational memory remains thin.
• What many leadership teams are discovering, often indirectly, is that AI does not simply stretch existing systems, it reveals how much organisational coherence was previously being held together through personal authority, informal influence, and tacit understanding.
• . Experience filled in gaps that were never formally designed. Leaders could rely on intuition, relationships, and pattern recognition to keep the organisation roughly aligned, even when roles, processes, or decision rights were imperfectly defined
• As individual contributors begin producing high-quality work faster than the organisation can interpret, decide, or align around it, those informal mechanisms start to strain.
• What first appears to be a technology challenge becomes a leadership maturity challenge.
• Coherence is not owned by any single role. It is produced through many small acts of alignment across the system.
• This is why social friction feels different at the top of the organisation than it does in operations, people systems, or learning environments.
• And it is why the work of restoring coherence cannot be generic. It must be grounded in the specific coordination challenges each leadership role is already living with.

• • Creating Legibility through Decision Provenance Mapping

  • Ensuring shared mental models with Assumption Walkthroughs

  • Enabling collective sense-making using Decision Reflection Loops

• Legibility improves when people can see not just what a decision was, but how it came to be and who carried which part of the judgment.

• In practice, this means taking a small number of recurring decisions and explicitly mapping four elements in plain language:

  • who is authorised to decide,

  • what inputs materially influence the decision,

  • why certain trade-offs are considered acceptable, and

  • how escalation is expected to occur when conditions change.
• Shared mental models are rarely built by sharing information. They are built by aligning interpretations.
• The technique is usually convened by the leader responsible for integration across perspectives, often a functional head or executive sponsor. Participants are those whose decisions will be affected by the interpretation, not just those producing the analysis.
• Collective sense-making depends on learning that accumulates rather than dissipates.
• The loop is typically owned by the leader accountable for the decision, but the reflection includes those who contributed materially to the judgment. The aim is to understand how meaning travelled through the system.
• Many organisations respond to AI-related friction with more training, clearer communications, or another change programme. These have their place, but they rarely address the underlying issue.
• Reducing social friction requires leaders to engage directly with how power, judgment, and accountability operate in practice.
• Organisations that make progress here begin to experience AI less as a productivity tool and more as a coordination amplifier.
• Leadership in this context shifts subtly but fundamentally. Attention moves away from managing outputs and toward shaping the conditions under which work holds together.

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• The familiar layers of HR - people, process, and policy - are being reimagined around data, intelligence, and experience.
• each iteration chasing efficiency, not always effectiveness.

• These experiences shaped a simple goal for me - to make work better, much better.

  I believe HR has a big role to play in this,

• Nobody actually wants to be transformed.
• The standard HR operating model was developed around the turn of the century and influenced from external factors happening at the time.

• IT and Finance went first, and HR copied this model.

  But if you bake with the wrong ingredients you will get mixed results.
• Primarily because the implementations did not use organisational design techniques, from first principles and incorporating the business context.
• Secondly, many were trying to fit a square peg into a round hole. A model that works in an IT company might not work in a factory.
• I no longer show my slide, “here is the answer, now what is the question?” with respect to the implementation of rigid HR Operating Models
• The external factors that once influenced the shape of HR in the last century, have now changed substantially, from SaaS to AI, and a workforce increasingly outside the payroll.

• One reason is that we continue to use the software licenses we inherited.

  If there was a business case, we don’t know whether the process worked, because we didn’t measure the results.
• Use evidence-based design to solve organisational problems - and avoid the worst mistakes.

• “how do we implement AI?”

  is the wrong question.

  Instead ask about which capabilities your organisation needs to succeed, and how to create a system that will adapt successfully to changes.
• The next decade isn’t about digital transformation - it’s about organisational redesign, as industries restructure.
• . HR’s role won’t vanish; it will orchestrate agility, culture, and capability across these ecosystems.
• But the best-performing teams keep their eyes on purpose and capability, not shiny tools.
• Start with organisational goals, design for how value is created, and help teams build the skills to deliver it.
• Evaluate new technology — but only after you’ve answered: what problem are we solving?
• Get stuck into conversations about the future of the organisations – the workforce strategy will quickly follow.
• Trust remains the true currency of change — for employees, customers, and leaders alike.
• AI without trust is just automation.

• Any successful organisation needs the systems and culture that enables the process of designing work to respond well to the industrial context they are in.

• • Start small, design boldly. Choose one workflow or team and redesign how work gets done using evidence and data - not hierarchy.

  • Map capabilities, not jobs. Use talent intelligence tools to understand which skills drive performance and where the gaps lie.

  • Build feedback loops. Treat every HR process as a prototype - test, learn, and refine.

  • Empower teams. Train managers to co-design roles and workflows with their people, not for them.

  • Measure what matters. Track capability growth, trust, and adaptability - not just headcount or time-to-hire.
• HR can play a key role to create systems that enable teams to design and manage work better, much better.
• Embrace the role of the work designer. Leaders must shape systems that flex as fast as the world changes.
• HR’s next act isn’t about rolling out another transformation project. It’s about designing the systems that let people and technology work brilliantly together.

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• But AI is not just a labor-saving tool. It’s a catalyst to rethink how work is done, how skills are used, how roles are defined, and how value is created.
• The dominant narrative around AI still frames the debate in terms of jobs gained versus jobs lost. That framing is too narrow. What is changing fastest is the content of work—the tasks people perform and the skills they apply.
• More than 70 percent of the skills employers seek today are used in both automatable and non-automatable work.
• people are shifting from producing first drafts to framing questions, validating outputs, and applying judgment.
• For leaders planning the year ahead, the implication is clear: reskilling is not about turning everyone into an AI engineer. It is about helping people develop AI fluency
• Almost all companies (90%) report investing in AI, but fewer than 40 percent report meaningful bottom-line impact.
• Workflows—the connected sequences of activities that deliver outcomes—were built for a pre-AI world.
• The pattern is consistent: productivity rises not because people do less, but because organizations achieve more as people do different work.
• First, are you treating AI as a core business transformation? The question is no longer which tasks can be automated, but which end-to-end workflows should be rethought to reflect what people, AI agents, and robots each can do best
• Second, are you investing in skills as a source of competitiveness? As work changes, skills—not jobs—become the anchor. Organizations will need to develop AI fluency alongside distinctly human capabilities such as judgment, communication, and leadership.
• Third, are you owning the strategic decisions? AI adoption forces trade-offs that cannot be delegated. Leaders must decide how fast to move and where to slow down to build trust; how much to bank capacity gains or reinvest them in growth and workforce transitions; and whether to prioritize certainty or foster experimentation and continuous learning
• organizations that invest in people alongside technology tend to capture more value—and sustain it over time.

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• Compensation, or pay, is the fundamental attribute of most employment contracts
• “You can never get compensation right, but you sure can get it wrong.”
• ou can’t have underpaid, yet high-performing employees.
• The risk of undercompensating your best people is that you never get to discover their ceiling – i.e. their best. 
• As companies morph into megacorps, you naturally turn every activity into a process
• I feel secure when I see repeatable and reliable processes. But in some cases, efficiency can be a trap – compensation is one such area.
• If the employee doesn’t believe that their employer is being fair or valuing them enough, throwing the book at them won’t help
• eing green on an HR indicator isn’t always a measure of fairness.
• Every individual has unique circumstances that describe their context.
• We all know that replacing a high-performing employee typically costs between 1.5x and 2x their annual salary.
• the cardinal flaw in people management is to design your systems for the exceptions instead of the norm.
• By default, people are self-effacing and would much rather go about their business and hope the company’s taking care of them.

• • Take away the information asymmetry between the employer and the employee. Publish (and update) pay ranges for each level in your organisation. Hide nothing, not even the exceptions. 

  • Let people figure out where they sit amidst the pay ranges and determine if they’re overpaid, underpaid, or paid just right.

  • Reduce the distance to power by empowering line managers to discuss pay with their direct reports. That way, if people have an issue with their compensation, they know their first port of call.

  • The best managers challenge their people to perform and care about their well-being. So, encourage managers to be more than just performance reviewers, and to advocate for pay hikes for their people.  
• use indicators like compa-ratios, only as indicators.
• You needn’t be a tech nerd or a people geek to be honest. You only need to be human.

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• According to a 2025 EY survey, while 88% of employees report some AI use at work, only 5% say they’re using it in advanced ways that fundamentally transform how they work—meaning many organizations are likely missing out on up to 40% of potential productivity gains because of gaps in “human readiness.”
• At work, the people who make up a team need to have the right conditions to thrive as humans first before they can unlock innovation and productivity. Respect, compassion, clear communication and a sense of safety must be present to create the ideal conditions for success.
• In the context of AI implementation, taking time to understand human behavior and psychology provides valuable insight into how to successfully curate a human culture that enables technology innovation.
• What will separate the winners from the losers in this moment will be how well they unlock the human capabilities of their people: to adapt, grow and deliver value with AI.

• 1. Establish psychological safety and a culture of experimentation

   

  Foster environments where people feel safe to speak up, share ideas and admit mistakes

• 2. Provide structured learning, training, and support

   

  Most employees currently using AI focus on basic tasks, with only a small fraction using it in transformative ways.

• 3. Align AI tools with people’s work and purpose—don’t just drop tools in

   

  When rolling out AI tools, clearly communicate why: how they map to business goals, how they help reduce tedious tasks and how they free people to focus on more meaningful work.

• 4. Reward experimentation and learning, not just output

   

  Encourage risk-taking and celebrate small wins. Even failed experiments are worth highlighting if they lead to valuable lessons and progress.

• 5. Treat AI adoption as organizational change, not a one-time project

   

  Use metrics beyond tool usage (e.g. employee sentiment, collaboration, innovation frequency, learning uptake) to track how well people are adapting to AI-enhanced work.
• People and HR leaders can’t simply provide employees with tools. They also need to help employees understand how using tools connects to the business—linking AI usage not just to efficiency or speed, but to long-term strategy, value creation and even individuals’ career paths.
• only a minority have managed to build the capabilities necessary to generate tangible value
• that individuals’ strengths and motivations align with the AI-enabled tasks and that their work advances both personal growth and organizational value
• HR leaders who can show they maximize return from that cost center—by building readiness, fostering culture and enabling human-AI collaboration—will help shape the business’s direction for years to come.
• It’s about empowering people.

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• ci ne signifie en rien la fin du Web3, mais simplement la fin des profits rapides reposant sur la mécompréhension de technologies complexes.
• ann LeCun s’e’est retrouvé isolé au sein d’une nouvelle organisation chez Meta où les priorités ont changé : il n’est plus question de faire progresser la recherche, mais de sortir des produits qui contribuent au chiffre d’affaires
• a recherche sur les modèles de langage s’essouffle. Comprenez par là que les gains de puissance ou de performance sur ce type de modèle se font à la marge.
• la marge de progression des LLMs est maintenant très faible.

• il existe de nombreux types de modèle génératif, et que certains offrent une belle marge de progression :

      
   
  • Les modèles multimodaux (des images et vidéos plus réalistes) ;
      
  • Les modèles de raisonnement (des réflexions plus poussées, mieux cadrées) ;
      
  • Les modèles d’action (toujours plus d’autonomie)…
• La dernière catégorie en vogue est celle des modèles de monde (« world models« ) qui offrent une appréhension plus précise de la réalité physique de notre monde. La finalité des modèles de monde est de générer des environnements réalistes qui s’appuient sur les règles de la physique
• l va ainsi pouvoir poursuivre ses travaux sur la fusion entre les IA et les robots pour pouvoir concevoir des entités 100% autonomes, capables de découvrir un environnement inconnu, de le comprendre et d’enrichir ses capacités que cette base :
• ’il y clairement un phénomène de surchauffe. À partir de ce constat, soit on tente de calmer les espoirs au risque de neutraliser la croissance potentielle, soit on les alimente pour justement stimuler la croissance
• ce qui stimule le marché n’est pas la recherche du progrès scientifique, mais de la capacité à faire rêver les médias et investisseurs pour perpétuer le cercle vertueux du capital risque
• Le prochain palier se fera avec la montée en puissance des IA neuro-symboliques qui reposent sur l’utilisation conjointe d’une base de connaissance et d’un moteur de règles.
• 2025 a assurément été l’année des LRMs (« Large Reasoning Models« ), ceux qui ont permis aux chatbots de passer d’un apport éditorial (mieux écrire) à un apport méthodologique (mieux raisonner = mieux travailler).
• ’évolution logique des modèles de raisonnement est d’en faire des modèles de travail (« work models ») qui qui auront une compréhension fine des différentes activités d’une entreprise (ex : compta, RH…).
• 2025 a également été marqué par les débuts des LAMs (« Large Action Models« ) avec des agents intelligents toujours plus autonomes et des échanges toujours mieux encadrés (standardisation)
• nous allons probablement voir émerger une nouvelle catégorie de super apps qui carburent à l’IA
• Sinon, LE gros chantier de 2026 sera la conception d’algorithmes d’apprentissage continu (contrairement au séquentiel) pour des modèles que l’on améliore en permanence :

• les grandes sociétés technologiques sont sur le pied de guerre :

      
   
  • Google savoure son retour sur le devant de la scène avec Gemini (How Google Got Its Groove Back and Edged Ahead of OpenAI) ;
      
  • Microsoft consolide son ancrage dans le monde de l’entreprise avec Copilot, et peut s’appuyer sur la clairvoyance de son Chief AI pour rassurer les patrons (Towards Humanist Superintelligence) ;
      
  • Meta dépense sans compter, mais le pivot depuis le métavers semble plaire aux investisseurs et actionnaires (Investors Are Still Bullish on AI Stocks) ;
      
  • Amazon est en embuscade avec Alexa+ (With new Alexa website, Amazon’s consumer AI vision finally comes together) et ses offres dans le cloud (AWS Updates Its Nova Models To Compete With Google, Anthropic and OpenAI et AWS puts Kiro and other AI agents to work on truly autonomous software development)
   
      
• Ainsi, l’éditeur de ChatGPT pourrait bien créer la surprise en 2026 avec le lancement prochain de modèles sectoriels, notamment dans le secteur financier pour lequel ils ont recrutés des anciens salariés des cabinets d’audit
• Sur le terrain des usages professionnels, Claude reste LA valeur sûre : le modèle le plus puissant et l’éditeur le plus responsable, ce qui plait beaucoup aux investisseurs institutionnels
• un élargissement de la fracture numérique entre ceux qui n’utilisent pas l’IA ou se servent de ChatGPT comme de Google (pour faire des recherches), et ceux qui investissent du temps et des efforts pour comprendre les innovations et explorer les nouveaux usages.

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• s these tools become more capable, many organizations hope they will spark higher levels of creativity, enabling employees to generate more novel and impactful ideas.
• A recent Gallup survey found that only 26% of employees who use generative AI report improvements in their creativity.
• We find that generative AI can indeed boost employee creativity, but the gains are not universal. Specifically, employees with stronger metacognition—the ability to plan, evaluate, monitor, and refine their thinking—are more likely to experience creative gains from using generative AI,
• to unlock AI’s potential for boosting workplace creativity, organizations must go beyond simply rolling out new tools; they also need to invest in developing employees’ metacognition and promote the thoughtful, strategic use of AI so employees can translate AI outputs into more effective creative performance.
• Employees produce more creative ideas when they have sufficient cognitive job resources.
• These resources include two key elements: a) information and knowledge, and b) the opportunity to adjust work methods and tasks, such as switching between complex and simple tasks and taking mental breaks.
• First, by expanding knowledge: Although employees’ own knowledge is limited, generative AI can provide large amounts of information within seconds. This expands employees’ knowledge base and enables them to integrate insights across domains. Second, by freeing mental capacity: When generative AI handles tasks such as summarizing texts, managing data, and drafting content, it reduces employees’ cognitive overload, allowing them to redirect resources to complex problem-solving.
• However, access to AI tools alone does not guarantee that employees can acquire the cognitive resources needed for creativity
• For example, employees with strong metacognition usually think through the steps to perform a task, keep track of how effective their approach is, and adjust when they notice a lack of progress.
• By contrast, employees low in metacognition are more likely to accept AI’s first answer, rely on default outputs, and fail to check whether AI’s suggestions are accurate or relevant.
• To examine these ideas in real work settings, we conducted a field experiment with 250 employees at a technology consulting firm in China.
• Employees with stronger metacognition became more creative when they used AI—they generated ideas that were judged as more novel and more useful.
• Generative AI does not automatically make employees more creative. What matters is whether employees have the metacognition to use AI in a reflective way.

• 1. Help employees use AI to expand the cognitive job resources that fuel creativity.

   

  Generative AI can enhance employees’ creativity by expanding their access to information and knowledge and freeing up their mental capacity for creative problem-solving. Leaders should encourage employees to use AI to gather diverse information, explore multiple angles, and offload routine tasks to restore cognitive capacity.

• 2. Raise awareness that metacognition is the engine of AI-supported creativity.

   

  Leaders might assume that integrating generative AI into their workflows will automatically make all employees more creative. Yet our research shows that creative gains tend to occur among employees who can actively monitor their own thinking and then evaluate, question, and refine AI outputs.

• 3. Build metacognitive skills through targeted and scalable training.

   

  Leaders should consider employees’ metacognitive abilities when implementing AI and invest in developing these abilities through training. Notably, metacognitive skills can be strengthened through various methods. Companies can offer short training sessions that introduce metacognition and walk employees through real examples of AI errors, asking them to anticipate, detect, and correct those mistakes.

• 4. Design workflows that promote active, iterative engagement with AI.

   

  Leaders should design workflows that position AI as a thinking partner rather than a shortcut. Instead of encouraging employees to use AI for quick answers, leaders should establish processes that involve generating multiple perspectives, comparing and critiquing AI outputs, and refining ideas across several rounds.

• Caveats and Limitations

   

  When applying these insights, leaders should consider several limitations. First, our findings are based on a single organization in China. Although the underlying mechanisms are likely to generalize, people’s attitudes toward AI may vary across countries and industries.
• Second, other personal traits—for example, motivational traits such as a strong desire to learn or to pursue ambitious goals—may also influence how effectively employees engage with AI to enhance creativity.
• hird, our study examined short-term effects within a single week. The long-term consequences of sustained AI use remain open questions.
• In sum, our research shows that generative AI can meaningfully enhance creativity—but only for employees with strong metacognition. By pairing AI deployment with deliberate support for metacognitive thinking, organizations can unlock deeper insights, accelerate innovation, and ensure that employees drive the tool rather than letting the tool drive them.
• As generative AI becomes woven into global workflows, cultivating employees’ metacognition will be what separates organizations that are merely adopting AI from those that are truly unlocking its creative power.

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• Productivity improves in pockets of the organization. Yet, somewhere between year one and year three, momentum fades.
• Transformation programs quietly stall, not because the technology failed, but because the organization never evolved how decisions were made,
• Most transformation discussions focus on what to implement. Fewer address how the organization itself must change to sustain that implementation.
• The operating model defines how work actually gets done. It includes decision rights, funding mechanisms, accountability structures, performance incentives, and governance rhythms.
• In many enterprises, digital initiatives are layered onto legacy operating models designed for stability, predictability, and functional silos.
• The result is friction. Teams are asked to move faster, but approvals remain slow. Leaders want agility, but funding cycles are rigid.
• Initial automation or AI initiatives often target contained problems. They improve a single workflow, optimize a specific function, or enhance a narrow customer interaction. These efforts succeed because they operate within existing structures and require minimal organizational change.
• Leadership interprets this success as proof that the transformation strategy is working, but scaling is different from piloting.
• Without explicit changes to how decisions are made and who owns outcomes, transformation stalls.
• In mature digital organizations, decision rights are clear.
• Technology teams build solutions, but business leaders retain final authority. Data teams generate insights, but operational leaders decide whether to act on them.
• AI models make recommendations, but no one is accountable for trusting or challenging the output.
• This ambiguity creates hesitation. Teams defer decisions upward. Leaders delay action. Innovation slows.
• Many enterprises still fund transformation initiatives as projects rather than capabilities. Budgets are approved annually, tied to fixed scopes and predefined deliverables
• Digital capabilities, however, require continuous investment.
• Leaders often underestimate how deeply funding mechanisms shape behavior. Teams optimize for project completion rather than long-term impact
• Cross-functional initiatives involve multiple stakeholders, each with partial responsibility but no clear ownership.
• When outcomes disappoint, accountability fragments. Issues are attributed to data quality, user adoption, integration complexity, or regulatory constraints.
• This diffusion of responsibility is one of the most damaging effects of a misaligned operating model.
• process optimization alone cannot overcome structural misalignment.
• True transformation requires aligning process excellence with operating model evolution.
• First, leaders must explicitly define decision ownership for digital capabilities.
• Second, funding models must shift from project-based investment to capability-based investment.
• Third, accountability mechanisms must be strengthened, not diluted, as complexity increases.
• Digital transformation is often described as a journey. In practice, it is a discipline.
• The difference is not ambition or investment. It is whether leadership is willing to redesign how the organization works, not just what tools it uses.
• The true test of digital leadership is not whether an organization can adopt new technology. It is whether leaders can evolve the operating model to support continuous change.
• Technology enables possibility. Operating models determine whether that possibility becomes performance
• Digital transformation does not break because leaders lack vision. It breaks because the organization remains designed for a world that no longer exists.

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