• Leaders who couldn’t make the conference (the pressure on them too high to imagine three days away) said it even more loudly afterward: I’m exhausted, I’m worried, I’m working harder than ever, and I’m not sure it’s going to be okay.
• AI is now less popular with the American public than ICE. Less popular than a president sitting at -20% net approval.
• Block makes headlines for laying off 40% of its workforce because AI, regardless of a multitude of other rationales.
• The message landing with most workers: you’re next. Unless you’re faster than the guy sitting next to you.
• We’ve gone from “vulnerability with boundaries” to “put on your armor.
• When Jensen Huang says your engineers should be spending $250,000 a year on AI tokens, remember he’s selling the chips that power the tokens.

• Some of the AI displacement narrative is, to be direct, bullshit.
• Oracle isn’t laying off 30,000 people because AI made them so productive they need fewer humans. Oracle is laying off 30,000 people because AI spending drove their shares down 54% since last September, in a market otherwise rewarding AI investment.
• Software engineering is dead? Job openings for software developers are at a three-year high.
• None of this means AI won’t reshape work significantly, it will. Nick Bloom’s research points to roughly 3 million additional Americans out of work over the next three years: Serious, and worth preparing for. Very different from the 20% unemployment figures thrown around at Davos, and the difference matters enormously for how you lead.
• : teams are the center of work. Not strategy, not tools, not AI. Teams.
• Teams that train and experiment with AI together get double the adoption of teams that don’t.

• Shared goals, hands-on leaders, time to experiment: those are the ingredients. AI transformation is a team sport.
• People over technology. Jacqui Canney at ServiceNow: tech is table stakes, people create change. Apple Musni at REI: AI isn’t accountable, humans are. Van Jones nailed it: As AI capabilities commoditize, human talent becomes the only remaining differentiator. This theme showed up in every session.
• Inna Landman at Procore: focused bets on two or three workflows outperform broad transformation programs every time.
• you can’t feed a squirrel enough to grow an elephant.
• massive change requires a level of trust most organizations haven’t built.
• Want to build a better future? Collectively, we have agency. Advocate for a human-first future:
• Think bigger, not smaller. No founder ever bragged about the great headcount cuts that defined their legacy. Instead of chasing 10% cost reductions, ask: where can you 10x the experimentation
• Provide pathways. Customer service is the clearest current example. You can treat it as a cost center to automate away, or you can do what Zapier, Guild, and Anthropic did: redesign jobs around future needs.
• Lend your voice. We’re living through an epidemic of loneliness and a social media environment engineered to divide at a time people need each other more than ever. Many of us feel powerless in the face of it. We are not
• Discretionary effort comes from people with autonomy, visible impact, and the chance to grow. You don’t get that from someone who’s concluded the axe is coming for them. You get compliance. Compliance is a floor, not a ceiling.
• The organizations that look smart in 2028 will be the ones that used this moment to become places where people could do more, build more, and grow more than before. That’s not inevitable. It’s a choice
• Audit your culture and incentives. Are you rewarding individual AI usage and output?
• Find your focus. Stop asking how AI can be applied broadly. Ask which two or three workflows, if redesigned, would create visible proof that this is working
• Create one pathway this quarter. Pick a function facing base-level automation. Work with the people in it to map what the higher-value version of that job could look like

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• both multi-millionaires cited AI as a reason for resigning their posts, arguing that they’re not the right people to manage the coming AI revolution.
• Quincey oversaw plenty of layoffs and strange market shifts in his day — but the AI wave, he insists, is a different beast altogether.
• . And I concluded that, actually, it was time to put someone else on the field for the next wave of growth
• “With what’s happening with AI, I could start this next big set of transformations with AI, but I couldn’t finish,”
• I started thinking about everything that needs to happen over the next few years, and it really caused me to think that now was the right time [to retire],”
• With golden parachutes at the ready and AI-driven growth nonexistent, the tech could very well be playing cover to conveniently-timed exits.
• Other CEOs, like the 62-year-old Adobe executive Shantanu Narayen, have been pushed out of the plane by investors who expected better performance in the age of AI.
• Whether curmudgeonly CEOs or broader economic turmoil are to blame for each company’s respective woes is up for debate, in other words. Either way, the old guard clearly isn’t waiting around to find out.

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• a lot of people figuring out creative ways to get products and services to their customers, and a lot of people spending time on being more resilient.
• And the one thing that cuts across both productivity and resilience is the leveraging of technology, AI, generative AI, et cetera
• I think 2025 marked a bit of a turning point for clients going from testing and learning and making small bits and pieces of progress to real separation from the winners using technology and AI to drive the value that they’re seeing, both in productivity and to help them be more resilient.
• three big imperatives: rewiring operations, accelerating tech-enabled decision-making, and building resilience to drive growth
• productivity is going to be as, or more, important in 2026 than it was in 2025
• the expectation is that the automation will get more and more affordable as time goes on and therefore continue to scale.
• the leveraging of AI is a “once in a generation” opportunity to rethink how end-to-end processes get done.
• it really hasn’t had very good ROI. Where clients have tackled end-to-end processes that can transform their business and leverage AI, generative AI, and automation to build the capabilities of their people altogether, companies have started to see really transformative results.
• s that all those end-to-end-process-related industries have embarked on leveraging not only generative AI and large language models but also agents and even groups of agents—so really building out an agentic workforce.
• . But there it’s a bit more difficult and, as I said, the service industries are really storming ahead here.
• The ability to have more information at your fingertips and the ability for AI, large language models, and agents—as Ruth talked about earlier—to take some decisions and help you take action faster positions companies to be far more decisive and responsive than they have been historically.
• t AI is necessary, but not sufficient. And one of the things that requires you to get to that level of sufficiency is actually building the capability of your employees to understand how they use the agents, how they use the LLMs [large language models] to actually take decisions and move quickly.
• So again, where we’ve seen people just try and point technology at a small issue, we’ve not necessarily seen companies get the ROI
• One thing Ruth said that is certainly true is that a lot of the same technologies and opportunities exist across industries and across regions. But how they’re applied and where they’re worth more or worth less is obviously quite different
• Pretending there’s an off-the-shelf solution is probably not right, but making sure that people are learning from one another, or where the value is actually being created, is the super important balance to get right.
• : I would say a couple things. On the technology front, 12 to 18 months ago, everything was about LLMs, and now people are talking about and pursuing agentic at a pace that hasn’t been seen before
• One thing I think will continue to be true is that the way to drive value is not by asking the questions “How do I leverage agentic?” or “How do I leverage AI?” Instead, ask what are your most important business problems, and how do you leverage a full suite of technology and potential solutions to solve those business problems—being impact focused in how you leverage the technology.
• But today, 80-plus percent of the best plants have generative AI, and roughly 5 percent also already have robots in their plants—so fully autonomous. And I think that is something that we in Europe have to catch up with.
• And that makes me quite positive that, looking at demographics and a potential future shortage of skilled labor, we will still be able to compete.

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• AI can kill the individual DNA of an organization by cleaving to the generic standard, undermining the organizations it seeks to streamline
• organizations become more automated, yet less adaptive; more data-driven, yet less wise; more efficient, yet less legitimate in the eyes of employees and customers.

• People stop thinking

   

  AI systems create a powerful illusion. Their outputs are fluent, confident, and often plausible. That very fluency encourages cognitive offloading; people stop thinking as deeply as they did before because the machine appears competent.

• What leaders can do: Identify critical human capabilities.

   

  Before you introduce AI into any core workflow, be explicit about which human capabilities your organization cannot afford to lose for its own competitiveness. Certain skills do not develop automatically. They include judgment under uncertainty, systems thinking, ethical escalation, and interpretive reasoning, i.e., the ability to view opportunities and challenges through the lens of a chosen strategy. These skills are developed and maintained only through use.

• Rules get buried in systems

   

  In every organization, there are moral choices enshrined as rules—who gets which resources, who is evaluated as successful, whose claims are accepted or denied. Traditionally, these rules have been visible, contestable, and subject to debate. This is how businesses maintain legitimacy and adapt to changing circumstances.
• The essential aspect of these decisions is that they operate through an organizational structure designed to surface conflicts, enable deliberation, and clarify accountability
• AI changes this organizational dynamic by embedding these often-subjective decisions inside technical systems. What once required explicit deliberation happens automatically, often at scale and speed.

• What leaders can do: Protect the structures that produce expertise.

   

  Many of your most valuable capabilities are created through organizational knowledge embedded in established hierarchies, not individual performance. Mapping these dependencies explicitly and documenting how knowledge moves from one level to the next is a fundamental step in assessing whether AI tools are homogenizing complex decision-making processes.

   

• Social ties are weakened

   

  Businesses are not just systems of rules and workflows; they are also social structures built on trust, mutual understanding, and shared purpose. These qualities of an organization’s culture emerge from human interactions which are sometimes cooperative and sometimes combative. AI threatens this fabric in two ways.
• it displaces interactions that once required people to work through problems together
• when decisions appear to come from opaque algorithms, traditional sources of authority—CEOs, managers, professionals—lose credibility.

• What leaders can do: Rebuild the communities that AI thins out.

   

  Your organization’s capabilities persist because people develop shared understanding through sustained interaction. AI often replaces those interactions with individualized, machine-mediated workflows. Don’t let that happen by default.
• Brightview’s solution focused on rebuilding strategy muscle via visible expertise. It established a policy banning AI-generated content from client-facing presentations. Teams must create original strategic narratives explaining their thinking. The agency also restructured client relationships so that every account has a designated “strategic lead”—a senior person whose job it is to challenge AI outputs and advocate for creative approaches the algorithms might miss.
• AI can enhance your organization’s performance—but only if you recognize what it cannot do.
• . Those remain irreducibly human functions. If you allow AI to crowd out these capabilities, you risk your organization becoming technically advanced but competitively fragile.

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• À chaque fois, la pression d’adaptation était réelle, mais à chaque fois, les entreprises disposaient d’un certain délai pour absorber le choc, expérimenter, former ses équipes et ajuster ses processus.
• le rythme d’évolution et la profondeur des implications ne laissent plus le temps de la réflexion
• Avec l’avènement de l’IA agentique, il ne va très certainement rester que deux positionnements viables : proposer l’offre la moins chère ou la meilleure.
• ne doit pas être considérée comme un nouvel outil informatique, mais comme un nouveau paradigme de l’outil informatique
• les entreprises ont toujours tendance à sous-estimer l’ampleur des changements à venir et à surestimer leur propre capacité d’adaptation. E
• t la dette numérique. Ce terme désigne le retard accumulé par les entreprises et organisations dans leur adaptation au numérique
• les véritables transformations ne portent pas que sur les outils ou données, mais aussi sur les processus et les mentalités
• Trois décennies de grands bouleversements des usages qui ont forcé les entreprises et organisations à revoir leurs outils, canaux et modes de fonctionnement.
• ce ne sont pas des tâches mécaniques ou répétitives qui sont déléguées aux IA, mais des tâches cognitives, analytiques, voire décisionnelles.
• vous ne pouvez pas déployer des agents sur un chaos informationnel
• Agentisation des opérations (de la production). C’est l’étape qui est normalement déjà en cours, celle qui concerne les tâches répétitives : traitement d’informations et de données, génération de contenus, gestion des requêtes courantes
• Agentisation du savoir-faire (de la conception). C’est l’étape émergente, celle qui commence à toucher les métiers créatifs, analytiques et d’expertise.
• Agentisation du pilotage (de la planification et des décisions). C’est l’étape à venir, celle qui n’est pas encore là, mais qui se dessine clairement. La capacité des agents à synthétiser des masses de données, à simuler des scénarios, à formuler des recommandations stratégiques
• Il n’y a ainsi aucun intérêt à vouloir gérer une activité au XXIe siècle avec des outils et méthodes du XXe siècle
• un tel chantier ne peut se faire sans l’adhésion des managers et des salariés.
• s’adapter même sans avoir une vision claire de ce à quoi pourrait ressembler le quotidien d’une entreprise une fois sa transformation agentique opérée.
• L’orchestration, c’est ce nouveau rôle qui émerge pour les travailleurs du savoir : non plus exécuter des tâches eux-mêmes, mais piloter des agents pour qu’ils le fassent à leur place (définir des objectifs, évaluer les résultats, corriger les dérives, garantir la qualité et la pertinence des actions effectuées…).
• 16,3 % des emplois privés et publics en France pourraient être menacés par l’IA agentique
• La révolution des cols blancs est en marche, car elle a déjà été initiée avec l’adoption rapide des chatbots.
• s bouleversements en cours sont trop importants et trop rapides pour que les réponses traditionnelles soient à la hauteur
• les dispositifs institutionnels n’ont pas été conçus pour absorber des chocs de cette rapidité et magnitude :
• les IA prendront les places que l’on laissera vacantes, alors à nous de ne pas nous effacer devant elles
• ’adoption structurelle de l’IA est particulièrement faible dans les PME/TPE : respectivement 31% et 15%. Pourtant, le taux d’adoption par les cadres ou dirigeants eux-mêmes est supérieur : 35%.
• l’IA générative est pour le moment perçue de façon erronée et utilisée en surface
• les retardataires, une fois convaincus, ont l’avantage de ne pas avoir à servir de cobayes, ils bénéficient directement d’outils optimisés et de meilleures pratiques, sautant ainsi des étapes d’exploration / expérimentations.
• si le chantier d’agentisation semble être une haute marche à franchir, il s’inscrit dans une continuité
• La première priorité, c’est la structuration de vos informations, données et connaissances
• La deuxième priorité, c’est la modélisation de vos processus.
• La troisième priorité, c’est la montée en compétences de vos équipes
• La quatrième priorité, c’est la gouvernance
• Les IA prendront les places que l’on laissera vacantes. Cette formule implique une responsabilité active : ne pas laisser de vides là où votre présence, votre jugement et votre expertise humaine font réellement la différence.
• L’histoire de la transformation numérique nous enseigne que les entreprises et organisations qui s’en sortent le mieux ne sont ni celles qui adoptent le plus vite, ni celles qui résistent le plus longtemps, mais celles qui savent transformer les contraintes extérieures en choix stratégiques internes.

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• : quels effets cette rupture technologique produit-elle sur la conception même des organisations ?
• Pourtant, leur principe de structuration du travail reste largement inchangé : elles continuent d’être pensées par fonctions.
• Ce modèle, hérité de la division du travail, a permis de développer l’expertise, d’optimiser les activités en s’adaptant à la configuration du terrain de jeu de l’entreprise. I
• L’émergence de l’intelligence artificielle, et plus largement des systèmes capables de traiter, transformer et orchestrer des flux d’information à grande échelle, introduit une rupture profonde.
• Les usages se multiplient, souvent de manière expérimentale, dispersée et hétérogène.
• Mais les promesses de productivité décuplée et de ROI faramineux se brisent sur le mur des réalités car il n’y a pas encore de remise en cause globale du fonctionnement des structures organisationnelles,

• • Quand les organisations sont structurées en « fonctions », l’IA opère en flux de traitement.

  • Quand les organisations segmentent, l’IA traverse sans se soucier des frontières organisationnelles et des silos

  • Là où les organisations coordonnent, l’IA « enchaîne » en flux continu.
• : Il ne suffira pas d’intégrer l’intelligence artificielle dans les organisations existantes. Il faudra transformer les organisations elles-mêmes.

• Si jusqu’à présent, la question structurante était :« Quelles sont les tâches à réaliser, et comment les répartir efficacement ? »

  Demain, la question devient :« Comment la valeur circule-t-elle, et comment organiser les flux qui la produisent ? »
• Les transformations induites par l’intelligence artificielle vont conduire les organisations, y compris non techniques, à adopter des modèles d’organisation inspirés de l’IT, structurés autour des flux, des interfaces et des capacités de service.

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• he value of HR is how much productivity and growth you can create for the rest of the company, not how much can you ring out the cost of HR itself.”

• As a result, HR technology isn’t just about HR anymore; the aim of these tools is to enable “employees to be more productive”.

   
• Bersin noted that these big vendor agents are “not necessarily as transformational as you may think.”
• making the current environment and workflows more functional, but they aren’t redesigning how work gets done
• automating the way the driver drives isn’t the same thing as building a self-driving car.”
• Our opinion is that if you’re not re-engineering the process, you’re probably not going to get the value of that you want.”

• “we’re not going to lose the HR department.”

   
• HR teams will need to be the managers and caretakers of AI agents. This means supervising agents designing to support specific HR functional areas – whether that’s compensation, benefits or recruitment – but also super agents that oversee the entire HR process.
• For Bersin, it’s time for HR to “get over” their fear and intimidation over AI
• Agents can “stich” together all “the domains of expertise that we have in HR” so that they can truly solve the business problems
• “individuals cannot reinvent their jobs.”
• requires HR to conduct a radical rethink of leadership within their organizations.
• achieve “cultural adoption” of AI, it cannot just be about “coming up with a great idea and giving people a bunch of tools, but bringing leaders along with you.”
• He called for vendors to get on board with not just domain-specific agents, but also these super agents that orchestrate big processes

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