• La quasi-totalité des salariés interrogés déclarent économiser entre une heure et sept heures par semaine grâce à l’IA, et plus des trois quarts estiment être plus productifs qu’il y a un an.
• Workday montre que la majorité des entreprises se trompe d’indicateur.
• Mais lorsque l’on intègre le temps consacré à corriger, à clarifier et à réécrire les productions de l’IA, plus d’un tiers du temps gagné disparaît.
• Un gain de dix heures de productivité se traduit, en moyenne, par près de quatre heures passées à réparer la sortie de l’outil. La productivité apparente progresse, mais la valeur nette se réduit.
• Elle découle d’un modèle d’adoption où l’IA s’ajoute aux processus existants sans redéfinir ce qu’est un travail de qualité
• L’IA accélère la production, mais transfère la charge de la qualité vers les salariés, qui deviennent les correcteurs permanents de systèmes encore instables.
• . Les utilisateurs les plus enthousiastes et les plus intensifs supportent une part élevée du coût caché de l’IA.
• Une large majorité de ces utilisateurs contrôle les productions de l’IA avec au moins autant de rigueur que celles de collègues humains, ce qui transforme chaque gain de vitesse en charge cognitive et en surcharge opérationnelle.
  
• À l’inverse, les profils les plus performants se concentrent davantage dans les métiers de l’informatique et du marketing.
• Dans ces contextes, l’imperfection est tolérable parce que la valeur provient de l’analyse, de l’itération et du recadrage rapide.
• Une même technologie produit donc des effets opposés selon la nature des processus métiers et selon le niveau d’exigence attendu sur la qualité.
• Les salariés utilisent des outils récents dans des cadres de travail conçus avant la diffusion de l’automatisation cognitive.
• Deux tiers des dirigeants déclarent que la formation figure parmi leurs priorités, mais seuls un peu plus d’un tiers des salariés les plus exposés aux reprises constatent une hausse effective de leur accès à la formation
• Workday met en lumière un arbitrage révélateur. Les organisations réallouent aujourd’hui une part plus importante des gains attribués à l’IA vers la technologie et l’infrastructure que vers le développement des compétences.
• Cette différence ne relève pas d’une politique sociale, mais d’une logique industrielle.
• La performance de l’IA ne se pilote plus seulement en heures économisées. Elle se pilote en valeur nette, en intégrant le temps perdu à corriger et les effets sur la qualité des décisions.
• Dans les opérations, cela revient à suivre le rendement au premier passage plutôt que le volume produit.
• À mesure que l’IA devient une infrastructure cognitive, la question centrale n’est plus l’automatisation, mais l’amélioration mesurable des résultats.

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• Emails fly. Meetings multiply. Everyone starts digging through procedures and policies.

   

  Not to understand and fix the problem, but to make sure their own hands look clean.

• But when problems inevitably arise, the focus shifts from solving to blaming.

   
  • Who missed the step?
  • Who didn’t tick the box?
  • Who’s guilty?
• Sociologists call this loop the vicious circle of bureaucracy: a self-perpetuating cycle where the initial problems of a bureaucratic system lead to the creation of more rules and procedures, which in turn create new problems and require further regulations.
• Risk had to travel up a chain of command so rigid that truth got filtered through layers of approval, PowerPoints, and procedure.

• If you follow the rules, disaster unfolds. If you break them, you’re the one at fault.

    

• Initially everyone always wants more of it. Simply because more responsibility equals more power, promotions, and prestige.

   

  Until something breaks.
• Managers will cite compliance. Frontline workers will blame unclear rules. Executives will cite rogue employees.

• Because every rule, every layer, every sign-off adds a hidden tax on initiative.

   

  Instead of improving products or serving customers, organizations spend all their energy protecting themselves from blame.

• What if responsibility wasn’t something to dodge when things go wrong, but something to share?

   

  Progressive organizations around the world are already showing it’s possible.

• You can only create compliance. And compliance is the enemy of learning.

   

  If we want organizations that adapt and evolve, we need to stop designing for blame and start designing for growth.

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• If you wanted to create a tool that would enable the destruction of institutions that prop up democratic life, you could not do better than artificial intelligence.
• But predictive and generative AI systems are not simply neutral conduits to help executives, bureaucrats, and elected leaders do what they were going to do anyway, only more cost-effectively.
• The very design of these systems is antithetical to and degrades the core functions of essential civic institutions, such as administrative agencies and universities.
• the current design of artificial intelligence systems facilitates the degradation and destruction of our critical civic institutions.
• The more AI is deployed in our existing economic and social systems, the more the institutions will become ossified and delegitimized.
• . In short, AI systems are a death sentence for civic institutions, and we should treat them as such.
• However, as we started to build out this paper, we realized that the threat that AI poses to institutions was far more dire than we had anticipated. We also realized we were far too optimistic about the possible appetite for safeguarding institutions.

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• . 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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