• 1. When expertise becomes a wall
• Because when you overdo it, departments stop talking and people start optimizing for their own targets instead of the customer.

• 2. The great divide: Planning vs. Doing

   

• But bureaucracy loves to split those things apart: one department prepares, another executes, another supports.

   

  The result? The people doing the work rarely have the power or information to fix problems when they appear.

• 3. The assembly line mindset

   
• When no one sees the whole picture, no one feels ownership. Errors multiply. Creativity dies. People check out.

• 4. The rule-makers vs. The doers

   
• But it also means the people writing procedures rarely understand the real work.

• 5. Siloed management

   

• Coordination? It becomes everyone’s job and no one’s responsibility. Managers protect their fiefdoms. Meetings multiply. Decisions stall.

   

• 6. Managing by aspect, not reality

   
• Each optimizes for their metric, not the mission.

• 7. The three-layer disconnect

   

• Executives design strategies they barely understand. Frontline teams face realities they can’t change. Middle managers burn out trying to translate between the two.

   

• 8. Decision-making in slow motion

   
• In fast-changing environments, that chain becomes a bottleneck. By the time a decision reaches the top, the context has changed.
• But together, they create a monster: an organization where no one owns the whole, everyone depends on someone else, and change becomes painfully slow.

• When no one can see or influence the full picture, every fix adds:

   
  • Another layer.
  • Another report.
  • Another meeting.
  • Another manager

• Morieux’s research shows some telling stats. In bureaucratic organizations:

   
  • Teams spend 40-80% of their time in non-value-adding activities.
  • Managers need an average of seven approval to act.
  • Managers spend 40% of their time on reporting.
  • Managers spend 30-60% of their time in meetings with peers.

• The healthiest organizations we’ve studied, from Buurtzorg to NER Group, share important common traits:

   
  • Teams own complete processes, not fragments.
  • Planning, doing, and learning happen close together.
  • People who set the rules also live by them.
  • Information is transparent, so coordination doesn’t require control.
   

19 more annotations...

• Most leaders I speak with use AI daily in their personal and work lives and see its potential to free us from routine tasks to focus on strategy, creativity, and human connection.
• At the same time, most feel that AI threatens white-collar professionals in a way they’ve never experienced before.
• They’re also leading teams full of people who have differing opinions and fears about the tools, and they’re expected to keep these teams motivated, aligned, and functional as AI is introduced.

• Anxiety driver 1: Lack of control over the speed of change.

   

  The way AI is going to play out in society is something that most of us have little control over.

• Anxiety driver 2: Loss of meaning.

   

  The humiliated executive highlights another reason why generative AI makes us anxious: It undermines values that we hold and the meaning that we find in work.

• Anxiety Driver 3: Uncomfortable emotions we prefer to avoid.

   

  Like any giant, existential change, AI is going to summon lots of messy emotions, and anyone who’s ever tried to suppress or ignore an emotion knows that it just doesn’t work.
• • Anxiety over losing income and livelihood, a career you’ve worked hard to build
   
  • Fear of irrelevance
   
  • Loss of control and self-determination
   
  • Anxiety your kids won’t be able to get jobs or that all the money you’re paying for college isn’t worth it
   
  • Overwhelm at the sheer pace of change
   
  • Anger at confusing or unrealistic demands about implementing AI
   
  • Frustration when AI doesn’t live up to the hype
   
  • Resentment at lost resources or shifting priorities
   
  • Loneliness because interacting with machines is replacing interactions with peers
   
  • Mistrust in your organizational leadership because you suspect they are not telling you the whole truth about what their plans for implementing AI mean for your own job and the jobs of your team
• • How do I feel about AI in general? What excites me? What worries me?
   
  • Am I anxious about losing relevance as a leader or am I excited to see what’s next…or a little of both?
   
  • Do I worry about pressure to cut jobs either now or in the future?
   
  • Do I trust my company to make good decisions when it comes to implementing AI?
   
  • What feels uncertain about my company’s AI strategy right now? How does that make me feel?
   
  • How does my team feel about AI?
   
  • Am I overwhelmed by the speed of change?
   
   
• AI triggers anxiety about an uncertain future, but for a lot of us, the thought of AI taking over our lives also clashes with values we hold dear.
• Identifying your values helps create a strategic decision, guided not by emotions or impulsivity, but rather aligned with values.
• To be clear, action isn’t always the answer; sometimes it’s enough just to give your emotions an audience and then tell them to simmer down.
• Action, too, can take many forms. Your AI action plan could involve using your heft to change policy or practice around AI implementation. It could involve your own upskilling or new learning about how to implement the new technology.
• “If you’re not feeling unsettled, you’re probably not paying attention.”
• AI anxiety is a logical threat response to uncertainty and the speed of change, and if we listen to it, it can guide us to take the right actions.

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• A study by MIT Sloan, Microsoft Research, and GitHub determined that gen AI coding tools can cut programming time by 56%, for instance.
• many people devote their extra hours to problem-solving, interacting with others, or learning new things
• we should leverage this new free time to improve our well-being and even to move to a four-day workweek.
• The researchers found that gen AI saved the managers about two hours and 50 minutes a week, on average
• many in that group failed to fully capitalize on their freed-up time: Thirty-six percent of them admitted to wasting more than half of it. Similarly, 83% of people in the gen AI user group who saved time admitted to wasting at least a quarter of it.
• “Reallocating time savings requires people to track when they’re saving time,”

• Unfortunately,” she notes, “many people don’t immediately notice it.” That was true for 37% of the managers in the study who said that AI had saved them time.

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• Be strategic.

   

  Focus on gathering information on how much time gets shaved off the tasks for which the most significant benefits are expected. But don’t start doing that with too large a group of people; begin with a small pilot.

• Ask people to log their time.

   

  Once you’ve decided what and whom to track, get the chosen employees to record the minutes they save each day.

• Develop a blueprint for reallocating time.

   

  Once you have a handle on how many hours your team will save with gen AI, design a plan to use them in a meaningful way—for instance, in activities that promote employee well-being, productivity, and personal growth.

• Monitor how the time is redirected.

   

  Regular monitoring and feedback are crucial to help employees make the most of their newfound time. Managers should stay engaged, observing what their reports do with time windfalls and promoting effective uses.
• s. This gives us additional time to measure and remeasure accuracy and security. However, every role and task has benefited from gen AI.
• I say I want to help them use the time windfall to further streamline tasks that hinder their progress. I ask people to imagine how they’d like to do their jobs three, five, and 10 years from now.
• It not only improved our efficiency but also demonstrated AI’s potential to help employees drive innovation.
• I suggest A/B testing productivity before and after implementing AI. Using control and variation groups, managers should also run rigorous tests and validate how employees have budgeted the time windfall.

13 more annotations...

• It sounds like a one-off “human error” — but Airbus data shows these incidents happen about three times a day worldwide.
• That's not rare. That's a systemic problem.
• it was “human error.” But it was also a predictable human error, one that the system didn't fully anticipate or mitigate.

• Mistake-proofing — or poka-yoke in Lean terminology — is never perfect when it depends solely on humans to execute every step correctly.

      
• . Some newer Airbus aircraft now incorporate a feature called “Watchdog,” developed by Airbus subsidiary KID-Systeme.

• That's not about blaming the crew. It's about designing for humanity.

      
• we don't ask, “Who messed up?” We ask, “How did the system make this mistake possible?”
• Good systems don't rely on perfect people. They assume mistakes will happen — and they're built so those mistakes don't lead to expensive, dangerous, or embarrassing outcomes.
• But how do you better mistake-proof the opening of a door that a flight attendant erroneously thinks is unarmed when it's still armed?

• the work of improvement never ends.

      

• Leaders should ask:

      
   
  • Are our systems designed to expect human fallibility?
      
  • Do our safeguards and cues make errors less likely — or more likely to go unnoticed?
      
  • When mistakes happen, do we respond with curiosity or blame?
• the question isn't who made the mistake. It's what in the system allowed it
• • Engage people at all levels in sustainable improvement
   
  • Shift from fear of mistakes to learning from them
   
  • Apply Lean thinking in practical, people-centered ways

11 more annotations...

• L’AI Manager Club, présenté comme le premier réseau national des responsables IA en entreprise, sera inauguré le 6 novembre à Paris.
• sur plus de 2 500 profils LinkedIn identifiés comme actifs dans l’IA.
• La création du club répond clairement au besoin d’accompagner une fonction émergente, souvent isolée, appelée à structurer les usages de l’IA au sein des organisations
• Pourquoi limiter la communauté à ces seuls 1 000 professionnels identifiés, sur des fonctions très techniques ou stratégiques ?
• créer un entre-soi élitiste, là où le véritable défi réside dans la diffusion des usages auprès des managers de proximité et des métiers opérationnels.
• mais former les managers tout court – RH, commerciaux, chefs de projet, responsables qualité, DAF – devient aujourd’hui indispensable
• Ce biais structurel, concentrer l’effort sur une élite technologique de l’IA, peut nuire à l’adoption généralisée
• Or les freins se situent souvent plus bas dans l’organigramme, comme la méconnaissance des cas d’usage, la peur de l’obsolescence, la résistance culturelle, l’absence de support dans les décisions opérationnelles.
• pour catalyser une adoption réellement organisationnelle de l’IA, l’effort devra s’élargir.

7 more annotations...

• Inspired, Curious, and Unbossed, isn’t a set of slogans but a daily guide for how work gets done.

      
   
  • Inspired means connecting everything back to patient impact.
      
  • Curious means learning continuously, across functions and roles.
      
  • Unbossed means trusting people to take ownership rather than managing through control.
   
      
• Novartis created behaviors in action—specific examples that help employees discuss, observe, and measure how culture shows up.
• t’s not just about why people join, but why they stay,”
• “Let your team do their work because they know their work better than everybody.”

• Leadership at Novartis now centers on three expectations:

      
   
  1. Lead and develop people.
      
  2. Navigate complexity.
      
  3. Deliver collective impact.
• “Thirty percent of what you do should be leading your people,”

• Data Starts the Conversation

      

  While AI tools are making leadership analytics more sophisticated, Rob warns CHROs against letting data replace human judgment. Metrics should start the conversation, not end it.
• the real value comes from what happens next: dialogue, reflection, and action.
• a blueprint for where HR is heading: not toward automation for its own sake, but toward augmentation
• the future of leadership belongs to those who make work more human, not less.

8 more annotations...

• Work is a way all of us keep roofs over our heads, offers social mobility and other opportunities and for many (and dare I say this?) in my experience how especially men define their purpose in life. What will happen when that changes and are we ready?
• One week, headlines announce mass layoffs, the next, companies are rehiring after being let go (Klarna and IBM to name but two).
• an economy where machines outperform humans not only in physical labour, but also in cognitive and creative tasks.
• . Professions and career paths as we know them across many sectors are predicted to disappear and some already have.
• Elon Musk recently said, “AI and robots will replace all jobs. Working will be optional, like growing your own vegetables instead of buying them from the store.”
• echoes Karl Marx’s utopian vision of post-labour freedom: “To hunt in the morning, fish in the afternoon, rear cattle in the evening, and criticise after dinner.”
• Automation and AI aren’t just changing the nature of work; they’re changing how we think about life.
• too many leaders seem to be taking a “FAFO” approach (F** Around and Find Out) rather than developing real strategies for a post-work world
• . In the United States, with its tech concentration and lighter employment protection structures, the the shift is already accelerating
• Some forecasts say by the 2040s we will probably see work becoming something people choose rather than need
• which will require safety nets on a wide scale
• , much to the frustration of the US tech sector, Europe, has a slower rate of transition: lower productivity growth, but stronger social systems mean it is estimated that its version of the post-work future will likely unfold between the 2040s and 2050s.

• 1. Tier One: The Tech Bros

  At the top we will have Tech Barons, “deviced- up,” sporting wearables from head to toe, living on adrenaline until there is a power outage or a drop in network signal. Scott Galloway warns that while technology creates immense wealth, it tends to concentrate that wealth in fewer hands, making us fragile.
• 2. Tier Two: Resilient Workers
• Highly-skilled technologists: these will be top level engineers who design, programme and maintain the AI and robotics systems.
• Supervisors of machines, humans giving oversight to these systems monitoring and managing
• AI resilient professionals: artists, caregivers, educators, therapists, performing artists, crafts people, skilled trades etc, the kind of AI resilient work that requires empathy, imagination, and physical human presence.
• Ethicists, policymakers, governance and social architects who ensure automation aligns with human values

• 3. Tier Three: The Displaced

  The dumped. Potentially millions (or billions) of people displaced by automation, living in an economy where working is optional or opportunities are scarce. This group will define the true human cost of AI-driven progress.

• Our job is to stop that divide from becoming permanent.

  That means:

  • Building AI literacy across the workforce, not just in the IT department.

  • Identifying future skills early and creating pathways for employees to evolve alongside technology. This should not be based on age and we will need the roles of the CLOs (Chief Longevity Officers) with strong policies for managing multi-generational workforces with inbuilt Longevity Literacy, not just AI literacy

  • Redefining performance metrics to include adaptability, ethical judgement, and collaboration with AI systems.

  • Designing trauma-informed offboarding, replacing today’s brutal mass layoffs with humane transitions that preserve dignity and possibility.

  • Lifelong purpose-building: which is possibly a new concept for redundant employees, preparing them for a world of No work.
• We can’t afford “AI slop, poorly trained systems or poorly trained people.
• We need strategies for a job-less world and I wonder how far along we are with this.
• Do we finally see the rise of Universal Basic Income funded by the very corporations profiting from automation?
• What happens when we lose the sense of purpose that work provided?
• Loneliness , (already a feature of our current workplaces) and loss of purpose have already been cited as key concerns.
• “The greatest threat of AI, hands down, in my view, is loneliness. I think we are on the precipice of disaster, combining synthetic relationships and AI”.
• The workless future isn’t science fiction anymore. It’s coming fast and we need to get ready. If work once gave us purpose, perhaps the next great task is to learn how to build meaning without it.

• The future of work is being rewritten but people must stay at the centre of the story.

27 more annotations...

• De "#MeToo", à "Occupy Wall Street", de "Black Lives matter" aux populismes et à Gaza aujourd’hui, ce changement n’a pas seulement polarisé le débat public, il l’a transformé pour en faire un espace où désormais les émotions l’emportent sur tout autre critère. Comment fonctionnent ces émotions collectives ? Selon quelles règles ? Quels pièges tendent-elles à l’opinion ?<!----><!----><!----><!--]--><!--]--><!--]--><!--]--><!--]--><!---->

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• "les émotions poursuivent le travail de la société en nous, ni erreur, ni vérité, elles sont des moments stylisés de l'existence. (...) la valeur morale des émotions est indécidable." En effet, ces émotions sont décorrélées des faits et de la vérité.

• Jean-Frédéric Schaub décrit ce mouvement universitaire décolonial né en Amérique latine, il souligne cette contradiction à savoir que les chercheurs n'ont pas appris la langue autochtone, il énonce : "ceux-là même qui disent cela ne font absolument pas partie des gens qui s'attachent à essayer de comprendre dans leurs mots ce qu'a pu être l'univers mental des gens dont on dit que leur univers a été réduit à néant".