HiPPO shows how leaders’ opinions still override evidence. In uncertainty, authority replaces method, and data becomes justification instead of guidance. The issue is structural, not personal. Only clear roles, valued dissent, and practices like “the boss speaks last” break the cycle.

AI doesn’t fail, leaders do. Most projects collapse not from tech limits but from missing clarity, ownership, and aligned decisions. The tech is ready; organizations aren’t. Only committed leadership and real operational change turn AI into impact.

Overwhelm is a sudden overload that stays hidden behind apparent performance. It arises when control, clarity and recovery collapse, pushing even top performers to the edge. The issue is organizational, not individual, and only redesigned work can prevent silent breakdowns.

AI-native firms embed data and learning from day one. Others must retrofit rigid structures and slow processes. Becoming AI First is a design shift, not a tech upgrade, and the real question is how close an organization can realistically get.

AI is widely adopted but trust is collapsing. Usage is high, yet teams routinely doubt outputs and re-verify work, creating hidden operational friction. Without reliability and auditability, adoption rises while credibility falls, and trust becomes the real infrastructure of AI integration.

Culture reshapes technology, not the reverse. Each innovation moves from fear to creative reuse, social filtering and slow integration. Even with AI, cultural resistance sets the pace, ensuring tools adapt to society rather than transform it overnight.

AI adoption alone changes nothing. It produces scattered uses and local gains without reshaping work. Only true appropriation, which reorganizes activities and workflows collectively, creates lasting impact. Adoption is a surface metric; transformation comes from redesigned work.

Microsoft place les jumeaux numériques au centre du futur du travail, en modélisant rôles, processus et connaissances pour déléguer des tâches à des agents. Au-delà de l’automatisation, c’est un nouveau paradigme collaboratif qui émerge. Reste la question clé des limites de cette délégation.

AI is widely adopted but barely productive. McKinsey shows 88% use it, yet only 7% fully deploy it. BCG sees a 5% elite capturing most gains. Both point to the same issue: firms aren’t structurally ready, and AI amplifies organizational weaknesses instead of fixing them.

IBM’s Arvind Krishna says the #AI spending race makes no economic sense. A 1-GW data center costs $80B; scaling to 100 GW means $8T, requiring $800B profit yearly. He sees #AGI as a near-zero-chance bet.

OpenAI is operating at an unprecedented financial scale: Deutsche Bank estimates 2024–29 revenues of $345B but $488B in costs, implying up to $200B in negative cash flow by 2030. No startup has ever burned cash at this level, while growth in paid adoption is already slowing in Europe.

AI reshapes the COO role: predictive tools refine resource planning, automated metrics sharpen performance tracking, and new duties emerge around tech choices, change support, and ROI. The job becomes more technical, strategic, and adaptive.

AI’s boom, built on hype, circular funding, and costly low-ROI models, unravels through quiet failures rather than a single crash. What follows is a downsized, local, efficient AI grounded in economic and operational constraints.

Agentic transformation isn’t a linear climb. It’s about mapping how coordination and control shift as autonomous agents take an active role in decisions and workflows. Organizations evolve by managing these tensions, not by following a universal maturity ladder.

“AI First” is mostly a slogan. Coined by Google without real structural change, it spread despite its vagueness. Companies like Amazon or Netflix use AI without the label. Today it signals ambition more than maturity, lacking clear architecture or organizational meaning.

AI adoption is collapsing after the hype: only 11% of US employees at large firms use it, and generative AI uptake is falling. “AI fatigue” grows as results disappoint, stalling progress despite massive investment.

Work before methods. Don Kieffer shows why copying Lean rituals fails and why Dynamic Work Design matters: focusing on real work, not artifacts. Its five principles help reveal friction, redesign workflows, and make good behaviors emerge naturally, especially in knowledge work.

AI projects fail not for lack of technology but for lack of cultural change. Despite heavy investment, most initiatives stay stuck in pilots because trust, leadership, incentives, and workflows never evolve. The few successes treat AI as a full organizational redesign, not a tech upgrade.

Tech has sped up communication but not collaboration. It amplified information flows without improving coordination, and AI now accelerates low-value “workslop.” The challenge is no longer speed but restoring meaning and enabling genuine collective contribution.

L’IA reste marginale en France : 27 % des salariés l’utilisent au quotidien, surtout pour des tâches simples. Manque de formation, craintes et absence de stratégie freinent l’adoption. Seules quelques entreprises structurent vraiment talents, apprentissage et culture.

Adoption massive mais superficielle : seules 4 entreprises sur 10 voient un impact. La valeur apparaît en passant d’un simple assistant interne à des agents métiers, puis à l’agentique multisystèmes et enfin à une IA industrielle intégrée aux opérations.

AI doesn’t create problems : it exposes the lack of organizational design. It amplifies inconsistencies, turning human-handled gaps into rigid rules. Klarna shows how AI can perfectly execute an incomplete design and erase distinctiveness. Organizations must be designed for AI, not by AI.

AI is accelerating digital product decay: tools bloat with gimmicky AI added for investor appeal, not user value. Teams neglect reliability, mix probabilistic AI with deterministic systems, raising costs and breaking UX. Enshittification becomes costly, opaque and driven by capital, not innovation.

The barometer shows employee experience signals organizational maturity more than HR effort. Only well-structured firms gain real ROI. Elsewhere, initiatives stay fragile, under-tooled and seen as costs. EX is not a program but the outcome of how work and processes are designed.

Vibe coding speeds execution but erodes differentiation: if AI writes most code, competitors can copy features in weeks. Shlomo warns that while prototyping becomes trivial, real difficulty shifts to architecture and durable product design, turning intelligence from an advantage into a commodity.

L’IA générative, avec près de 30 % d’erreurs, impose vérification et supervision. Elle ne remplace pas l’expertise humaine mais la renforce, révélant limites, effets inattendus et besoin de jugement. Loin de supprimer les emplois, elle revalorise compétences et analyse.

For those interested in the employee experience, I have tried to compile and organize articles that I have written on the subject over the past few years, which I consider to be somewhat of a foundation. #EX

#AI adoption is often fragmented. Value Pathways link scattered pilots into a coherent flow of value, letting local wins reinforce each other. Leaders focus on designing the environment and feedback loops so AI becomes a connected system, not a collection of isolated projects.

The Goal reframes organizations as systems where performance comes from managing flows and constraints, not optimizing parts in isolation. Local fixes can harm global results. Real improvement targets the core constraint and value creation, a lesson that also applies to modern knowledge work.

Sutskever signals the end of the LLM illusion: scaling is stalling, models generalize poorly, and new neurosymbolic approaches are needed. His warning exposes a trillion-dollar AI bubble built on weak assumptions, with broader economic risks if the narrative collapses.

AI use in HR is rising, but structured adoption lags. While 80% of HR staff use AI personally, only 31% of firms embed it in processes. Costs are the main barrier, expectations grow with generative AI, and HR’s strategic role strengthens as leaders seek clearer ROI.

AI is replaying Lean’s cycle: big promises, little impact. Misused as cost-cutting, it creates mistrust, and 95% of projects show no financial gains (MIT). Real progress needs field-driven design and productivity used to improve work, not cut jobs.

#AI #adoption suffers from a perception gap: 76% of executives think employees welcome AI, but only 31% of non-managers agree. Firms that genuinely focus on employee needs are seven times more likely to succeed, because involvement, clarity and co-design drive trust, uptake and meaningful use.

Massive AI spending echoes the dot-com bubble, but open source shifts the balance. With fast enterprise adoption and diversified hardware and cloud options, no monopoly can emerge. By 2030, a fragmented multi-model ecosystem will dominate, where agility outweighs scale.

Digital transformation is hitting its limits: tech spending rises, but only two in ten employees feel equipped to work effectively. A complex, poorly integrated digital workplace and a bias toward tools over adoption block performance. Real impact now depends on work design and employee experience.

Digital technologies and AI are reshaping our habitus, shifting us toward fast, fluid, dematerialized environments. This “liquid habitus” boosts adaptability but fragments attention, alters social behavior, and weakens the anchors that once structured identity and collective belonging.

Adaptive resilience requires abandoning hierarchical control for flexible, network-based structures. Classic hierarchies optimize for stability, not change, making firms blind to weak signals. Heterarchies and loosely linked teams adapt faster, absorbing tensions and enabling continuous renewal.

Over-optimization can erase a company’s identity. When systems override culture, experiences become efficient but impersonal. Klarna shows how excessive automation weakens customer connection. Identity comes from daily choices; technology must support the promise, not replace it.

Bouygues Telecom déploie Be360+, un outil d’IA générative basé sur Salesforce pour assister 6000 conseillers, bientôt 10 000. Résumés clients automatiques, réponses documentées et tâches simplifiées améliorent précision et satisfaction. L’IA soutient l’humain sans remplacer le contact client.

Operational excellence in 2025 is a strategic discipline where AI, data and clear processes drive anticipation and faster decisions. Value comes from simple, friction-reducing use cases and reliable data, with humans focused on analysis, adaptation and leading change.

Generative AI boosts productivity and customer experience, but few firms scale beyond pilots. Impact needs solid data, modular tech, agile teams, and a receptive culture. With shared learning and distributed governance, AI shifts from isolated tests to a true performance engine.

Titanic shows a system failure: misaligned incentives, ignored warnings and flawed priorities made disaster inevitable. The iceberg was only the trigger; as Deming said, systems produce the outcomes they’re designed for.

Calls to merge HR and IT misread the problem. AI adoption isn’t about tools but redesigning work with employees. HR can guide change, yet managers must own it. Without real leadership commitment, integration stalls and produces only performative adoption.

COOs must move beyond firefighting to scale AI-driven, resilient operations. With productivity lagging and labor tight, they align strategy and execution, avoid pilot-trap initiatives, and act as system architects who anticipate risks and enable continuous learning.

Growing doubts surround the AI boom. Optimistic investors dismiss bubble fears, but voices like Sundar Pichai and Clem Delangue warn of irrationality. High valuations, massive infrastructure spending and weak revenues raise concerns. Markets stay volatile and investors now want real returns.

Rethinking work must come before adopting #AI. AI exposes strategic gaps and forces companies to clarify who they are and where they want to go. Frameworks help, but intent matters more. Real value comes from redesigning work before automating or enhancing it.

AI isn’t the threat to junior roles. The real issue is intent: young talent wants exploratory paths, not ladder steps. As AI removes repetitive tasks, entry-level jobs must become spaces to discover strengths and direction, not waiting rooms for promotion.

AI doesn’t transform organizations; it reveals their weaknesses. Early wins hide unstable processes, poor data and unclear governance. AI shows where workflows must be redesigned and where human judgment stays essential, proving that real value depends on operational excellence.

The Hype Cycle reflects collective psychology more than tech evolution. Hype inflates promises, collapse punishes misaligned expectations, and maturity comes from internal capability. Organizations escape the cycle by aligning governance and learning with real use instead of market tempo.

AI streamlines standard HR tasks, but fails on human-sensitive cases. A coached employee quits after weeks of unnoticed distress, revealing AI can’t judge or escalate. The challenge isn’t adoption but discernment: set clear limits, keep human responsibility.