• The fact that people are unimpressed that we can have a fluent conversation with a super smart AI that can generate any image/video is mindblowing to me.”
• Convincing looks sideways. It compares the tool to what I’m using right now. Does this work better than my current solution? Is it more reliable than a junior analyst? Does it solve a problem I actually have?
• One user spent 20 minutes trying to get Copilot to generate a simple Excel list of Terry Pratchett books. It failed, then gave them links they could have Googled in three seconds.
• In every case, older, simpler, and reliable wins. As one commenter put it: “It would be like trying to sell me on my power drill having a conversation with me.”

• A lot of knowledge workers still don’t use LLMs for work. Only about 5% of ChatGPT users pay for premium tiers. Why? Because most people don’t have the “time to figure out how to save time.”

  Technology adoption research confirms it: when introduced to new technology, people reference familiar systems to understand its purpose.

• what I call the Impressive Demo Death Spiral:

  The Flashy Pitch: AI is marketed with “creative” demos : making art, writing poems, or generating videos of Sam Altman riding a horse on the moon.

  The Toy Categorization: The user’s brain files AI under “Creative Toy” or “Entertainment.”

  The Cognitive Lock: When that same user needs to perform a strategic analysis, they don’t think of the “Poem Tool.”

  The Low Adoption Trap: Companies see low engagement in “useful” features and double down on even flashier demos to get attention.

  The Executive Dismissal: Leaders watch the flashy demos, feel unimpressed, and conclude AI is overhyped — missing the real opportunity entirely.

  “A tool that can ‘do anything’ effectively communicates that it does nothing in particular.”

• The data shows that convinced users aren’t doing impressive things. They’re doing boring things.

  Specificity Wins: The most common tasks were granular: “filter emails,” “summarize research,” “edit documents.” Even with general-purpose agents capable of anything, users gravitated toward bounded, repeatable workflows.

  The Utility Migration: Users who stick with AI agents gradually move away from “chatting” and toward “thinking-heavy” productivity tasks (research, document workflows, career development).

  The 57% Rule: More than half of all agent actions fell into “Productivity and Workflow.” Not image generation. Not creative writing. Not the demos.

• Where is the “Snake” game hiding in your organization?

  Information Triage: Is the team drowning in 60-page PDFs? This is where AI agents show the highest adoption, not because it’s impressive, but because it’s genuinely faster.

  Mechanical Workflows: Which tasks involve reformatting data, editing documents, or version management? The Perplexity data shows document workflows as a top use case.

  Research Compression: Where is the gap between a question and an informed decision currently costing you days? This is the “thinking-heavy” work where convinced users increasingly deploy AI.

• The right question isn’t “Is this model impressive?”

  The right question is: “Does this drill make the hole?”

7 more annotations...

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But workplace evidence is uneven
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In customer support, junior agents sometimes gain more than experienced staff. Wage and opportunity effects also look lumpy, not smooth.
• these contradictions are not random. They are consistent with a threshold model of AI productivity
• LLMs behave less like a smooth learning curve and more like a cliff.
• Below it, added AI usage can produce modest gains, noise, or even negative ROI due to integration friction, cognitive overhead, and quality risk.
• Bisardi labels that threshold AI architectural literacy
• This is not about prompt cleverness. It is the operational ability to decompose ambiguous goals into tractable tasks, orchestrate multi-step workflows, bind models to tools and data, and validate outputs systematically.
• Level 1 is surface usage. LLMs are treated as autocomplete, search, or a writing shortcut. The workflow remains mostly unchanged. Studies in software development suggest that for experienced practitioners doing complex tasks, this mode can be neutral or harmful on net.
• Level 2 is integrated usage. Users provide richer context, use multi-step prompting, iterate more deliberately, and develop partial awareness of failure modes.
• Level 3 is redesign. Here the unit of work becomes a system run rather than a chat session. Practitioners build agentic workflows, connect models to APIs and structured data, standardize checks, and increasingly automate verification.
• access and basic adoption are not the moat. The moat is crossing the threshold where AI is embedded into how work is specified, executed, and audited.
• • Identify cliff-eligible workflows. Prioritize domains where tasks are repeatable but cognitively dense: support resolution, analytics pipelines, compliance documentation. These are the places where orchestration and verification can outperform ad hoc chatting.
   
  • Invest in workflow decomposition and runbooks. Create standardized task trees, prompts-as-components, tool schemas, and exception-handling patterns. Treat them like reusable infrastructure, not tribal knowledge.
   
  • Bind LLMs to tools and structured data. The productivity jump is not just generation. It’s retrieval, action, and validation. If the model can’t reliably read your systems of record or execute controlled actions, you are stuck in Level 1–2 forever.
   
  • Build lightweight evaluation into production. Use automated checks, unit-test-like validators, red-teaming for edge cases, and human review gates on high-risk outputs. The fastest way to kill AI ROI is letting quality failures trigger organizational backlash.
   
  • Train for architectural literacy, not prompt tricks. internal enablement should look closer to engineering education than a two-hour workshop. Teach decomposition, orchestration, tool use, and evaluation as a coherent practice.

10 more annotations...

• at Pernod Ricard, buy-in bubbled up as the company carefully nudged employees to embrace new tools powered by artificial intelligence.
• McFowland and Bojinov say the key to smoother adoption is proactively addressing resistance as well as training and supporting employees who are hesitant to give new digital tools a try
• But by 2020, managing an increasingly complex brand portfolio with analog processes felt unsustainable for company leaders. Plus, company leaders believed a digital transformation would help its sales and marketing teams become more data-driven and efficient in their spending.

• The two flagship tools were:

   
   
  • D-STAR, a system that used machine learning to optimize sales representatives’ store visits and product recommendations.
   
  • Matrix, which used AI to allocate marketing spending across brands and channels.
• Initial rollouts revealed the depth of employee skepticism. In France, some marketing managers resisted Matrix’s recommendations to scale back on some marketing initiatives because they challenged managers’ emotional attachment to certain brands. In Germany, sales representatives worried that D-STAR might diminish their strategic efforts—the part of the job many valued most.

• Demonstrate real value through testing.

   

  The company ran A/B tests to prove the tools delivered measurable improvements. When it deployed D-STAR in France, stores where sales reps followed the AI recommendations saw better net market share growth and net sales growth compared to control groups.

• Take the risk out of adoption.

   

  Pernod Ricard restructured performance evaluations to remove fear from the equation. Sales representatives who followed AI recommendations but missed their targets weren’t penalized, while those who ignored the tools and missed targets faced scrutiny.

• Invest in education and support.

   

  The company created dedicated deployment teams for each market rollout, including local change management specialists, data analysts, and trainers. Employees received extensive education on how to use the tools and why they were valuable.

• Leverage internal champions.

   

  Perhaps most importantly, Pernod Ricard named respected employees in each market who could serve as technology ambassadors. “They identified the people who were listened to,”
• The strategy turned digital transformation from something management imposed to something employees actively wanted. “
• The tools delivered sales increases between 1.5% and 4.5% depending on the market, plus up to 15% improvements in marketing efficiency.

• Balancing Control and Accountability

   

  The researchers say a key insight from the study revealed the need for companies to strike a balance between the “span of control” and “span of accountability.” When AI tools reduce employees’ control over their work processes, organizations must also adjust how they hold employees accountable.

• or example, by telling sales reps using D-STAR that they wouldn’t be blamed if they followed AI recommendations that didn’t pan out.

   
• Start with pilot programs that prove value. Don’t roll out tools companywide until you can demonstrate concrete benefits in controlled settings.
• Build dedicated deployment teams. Successful adoption requires people whose full-time job involves managing the transition.
• • technical staff.
   
  • Address the human side early and often. Most digital transformation failures stem from people issues, not technology problems, the researchers say.
• Adjust organizational processes, not just technology. Don’t just drop new tools into existing workflows. Redesign processes, incentive structures, and accountability measures to align with new capabilities.
• Aim for 85% adoption before expanding. Pernod Ricard wouldn’t deploy tools to new markets until existing markets achieved 85% adoption rates
• As AI becomes increasingly central to business operations, the Pernod Ricard case offers hope for traditional companies worried about bringing employees up to speed.
• “It’s not a matter of if you should do this; it’s a matter of when you should do this,” Bojinov argues. “And the answer is yesterday.”

18 more annotations...

• Half of AI-attributed layoffs will be quietly rehired, but offshore or at significantly lower salaries. Forrester pros say this isn’t speculation. It’s based on patterns that are already emerging.
• According to Forrester, 55% of employers report regretting laying off workers for AI.
• companies are laying off workers for AI capabilities that don’t exist yet, betting on future promises rather than proven technology.
• Admit the mistake and rehire at previous salaries, or quietly fill gaps with lower-cost offshore labor. Forrester predicts most will choose the latter.
• replaced 700 employees with AI, but quality declined, customers revolted and the company had to rehire humans
• Amazon’s Just Walk Out retail technology, marketed as AI-powered, actually relies on remote workers in India to monitor in-store cameras.
• While experienced workers are being laid off prematurely, many new graduates can’t find entry points into the workforce.
• The Burning Glass Institute reports that unemployment for 20- to 24-year-olds with bachelor’s degrees has increased from 5.2% (2018-2019) to 6.2% today. Young bachelor’s degree holders now face higher unemployment than workers with associate degrees. And 52% of the class of 2023 were underemployed one year after graduation, according to the institute.

• For new graduates

   

  Non-profit research firm The Burning Glass Institute finds that AI tools like ChatGPT can instantly handle foundational tasks that once taught early-career workers about how to do their jobs. Some of these are drafting, research, basic analysis, forecasting and documentation.

• For experienced workers

   

  According to Forrester, organizations believe AI will eventually replace mid-level roles, so they’re laying people off preemptively based on promised future capabilities rather than proven results. When AI fails to deliver, they quietly hire replacements offshore at lower cost rather than admitting the mistake.

• The readiness gap

   

  While organizations rush to adopt AI and base layoff decisions on its promised capabilities, almost nobody actually knows how to work effectively with AI.

   

  Forrester measures this through what it calls AIQ—artificial intelligence quotient, or AI readiness. Only 16% of individual workers had high AIQ in 2025, according to Forrester’s research. That number is predicted to reach just 25% in 2026.
• w? Organizations aren’t investing in training. Only 23% of AI decision-makers surveyed by Forrester say their organizations offered prompt engineering training in 2025. Employees are largely teaching themselves through solo experimentation.

• The engagement crisis

   

  Forrester identifies a growing segment of employees it calls coasters. These are disengaged workers who don’t think their employer deserves their energy. This group accounted for 27% in 2024, 25% in 2025 and is expected to rise to 28% in 2026.
• Employees watch colleagues laid off for AI that never materializes, see entry-level positions eliminated so new talent can’t join and observe offshore arbitrage disguised as innovation

• According to Forrester, when a quarter of your workforce is actively withholding discretionary effort, no amount of AI will compensate for the productivity loss.

   

• the consequences will extend far beyond individual careers:

   

  • A generation locked out of career advancement
   • Loss of institutional knowledge as experienced workers exit
   • Reduced innovation as fewer people gain hands-on experience
   • Widening inequality between those with networks and those without

   

• Rebuild the entry-level pipeline

   

  Rather than eliminating junior roles, redesign them around human-AI collaboration. Let AI handle mechanical tasks while new graduates focus on learning judgment, relationships and complex problem-solving.

• Challenge premature layoff decisions

   

  Before approving any AI-attributed layoff, demand evidence: What AI system is deployed in production and proven at scale? What are its error rates, and how does it handle edge cases? What’s the rollback plan if AI fails? How will institutional knowledge be retained?
• If business leaders can’t answer with data, Forrester researchers say the layoff is likely premature.

17 more annotations...

• Digital twins are dynamic virtual replicas of a physical object, process, system, or environment. While digital twins are becoming increasingly popular, implemented
• Digital twins are revolutionizing how organizations design, operate, and improve complex systems.
• By combining real-time data with simulation, artificial intelligence (AI), and analytics, digital twins make it possible to test scenarios safely, optimize performance, reduce costs, and uncover insights that were previously inaccessible.
• 1. Digital twins are used in many industries
• including manufacturing, healthcare, retail, law enforcement, and defense.

• 2. Digital twins don’t always mirror a single object

   

  Many industrial twins replicate systems of systems – for example, an entire factory, a supply chain, or even a city. These twins simulate interactions across thousands of components, not just one machine.
• “Many companies are not only experimenting with these technologies but are already employing and scaling concrete use cases that demonstrate the added value of the industrial metaverse.”

• 3. Digital twins can solve sustainability challenges

   

  An international competition brought together university students from the US, Germany, Canada, UK, Ireland, and Brazil to apply digital twin technology to solve real-world sustainability challenges. 

• 4. Digital twins can forecast behavior and evolve continuously

   

  With advanced physics models or machine learning, digital twins can simulate how an asset will behave in the future, predicting failures, optimizing energy use, or simulating what-if scenarios under conditions that haven’t happened yet.
• 5. Digital twins aren’t only for machines
• Human digital twins model things like athletes’ biomechanics, patient-specific organ models for simulation, ergonomic workplace design, and even behaviors in large crowd simulations.
• The H&M instance cited above is a prime example. In March, H&M announced plans to use 30 AI-created models in some social media posts and marketing in the place of humans, if given permission to do so by their human counterparts.

• 6. Digital twins require more data engineering than AI

   

  Perhaps the most challenging aspect of digital twins isn’t the modeling; it’s integrating messy sensor data, synchronizing systems, maintaining mappings between real-world identifiers and virtual counterparts, and managing versioning across lifecycles.

   

• Some more surprising things about digital twins

   
   
  • The global digital twin market is projected to reach US$155.84 billion by 2030, growing at a CAGR of 34.2 percent, according to Grand View Research.
   
  • NASA pioneered the foundational concept of a digital twin. It originated during the Apollo program, where engineers built precise digital replicas of spacecraft so they could diagnose problems from Earth when something failed in space.
   
  • Change management is essential for adoption. Employees must be trained to work alongside digital twins and organizations should foster a culture of innovation to maximize benefits.
   
  • Nations like Singapore and the UK have created large-scale digital twins that simulate traffic flow, infrastructure stress, energy use, and even flood scenarios.
   
   
   

   

   

12 more annotations...

• ’étude « Skills Economy Report 2026 » de Cornerstone met en lumière un déséquilibre systémique entre gains de productivité à court terme et capacité de renouvellement des compétences à long terme.
• Il met en évidence une fragilité plus structurelle, liée non pas à l’absence de compétences disponibles, mais à l’érosion progressive des mécanismes qui permettaient historiquement de les produire, de les transmettre et de les renouveler.
• L’explosion de la demande en intelligence artificielle ne se traduit pas par une multiplication des postes spécialisés, mais par l’infusion de briques de compétences IA dans l’ensemble des rôles.
• la demande en compétences liées à l’IA et au machine learning a progressé de 245 % entre 2023 et 2025, sans correspondre à une explosion équivalente des intitulés de poste associés.
• . Elle invalide les stratégies RH fondées sur des silos d’expertise ou des centres d’excellence isolés.
• La compétence devient une unité économique transversale, combinable et recomposable, qui traverse les fonctions, les secteurs et les niveaux hiérarchiques.
• la valeur ne réside plus dans un métier figé, mais dans la capacité à assembler des compétences techniques et humaines au sein de rôles hybrides.
• Les frontières entre compétences techniques et compétences humaines se dissolvent
• Les rôles historiquement relationnels intègrent désormais des exigences analytiques et data, tandis que les profils techniques doivent développer des compétences émotionnelles et collaboratives
• es ingénieurs IA voient la demande en intelligence émotionnelle progresser de 95 %
• Des compétences telles que l’autonomie, la capacité à travailler de manière indépendante ou l’enthousiasme professionnel connaissent des progressions spectaculaires, respectivement de 850 % et 999 %
• En supprimant ou en réduisant drastiquement les tâches répétitives confiées aux profils débutants, les entreprises assèchent les terrains d’apprentissage qui permettaient historiquement la montée en compétence.
• près de 30 % des heures de travail des postes d’entrée de carrière sont désormais automatisables, précisément celles qui servaient de socle à l’acquisition de l’expérience.
• La disparition progressive des postes juniors ne relève donc pas seulement d’un ajustement organisationnel, mais d’un risque pour la capacité collective à produire les compétences futures.
• Les gains de productivité générés par l’IA et l’automatisation sont réels et nécessaires, mais ils reposent sur une forme de consommation accélérée du capital humain existant
• le risque d’une « bulle de productivité », alimentée par des performances à court terme mais déconnectée de la capacité à renouveler les compétences à long terme.
• Les compétences ne peuvent plus être considérées comme un simple sujet RH ou un centre de coûts. Elles doivent être abordées comme des actifs stratégiques, nécessitant des investissements continus
• Il s’agit de construire des architectures organisationnelles capables de développer les compétences en même temps qu’elles automatisent les processus
• ne performance immédiate au prix d’une fragilisation durable de la capacité d’innovation.

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• The initial hype and experimentation of AI has given way to widespread adoption and a heightened focus on ROI; it’s now about impact over innovation.

• That is being embedded in various solutions, such as recruiting and onboarding, but also L&D and even in workforce planning. This is becoming more and more prevalent, which is very important. 

   
• the shift from point solutions to platforms

• a growing focus on measurable ROI on HR technology and workforce outcomes.”

   
• A lot will be related to AI-enabled capabilities, but also particularly in the area of workforce analytics.”
• “While consolidation will be pinging, there will be new products with new use cases, things we haven’t been talking about before. 
• “I don’t really see an emerging bubble here, because when we look at certain deals they haven’t all taken place at the same valuation level.
• “I find that pretty reasonable and I see no over valuation here, which is also pretty much consistent with what we see in the market as part of our transactions.”
• “There’s an increasing alignment here. It’s also a matter of perception among HR leaders, that they also want to be a profit center rather than just a cost item.
• it’s not only a matter of global growth and talent acquisition, it’s also a matter of efficient and effective processes creating value inside organizations.”

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• Sans qu’ils en aient toujours conscience, ces nouveaux leaders observent le collectif comme un organisme vivant : ils voient les connexions, les flux, les zones de friction.
• Ils comprennent que l’efficacité ne vient pas d’un effort individuel mais de la qualité des interactions entre les acteurs et des relations entre les équipes.
• e manager cesse de piloter les gens, il accompagne un mouvement et entame son voyage vers la maîtrise managériale.

• C’est ce qui distingue un manager dépassé, dans l’illusion du contrôle, qui veut tout gérer avec si peu de résultats d’un leader qui prend conscience et qui comprend pour un bien plus grand impact à court et long terme.

    

   

    

   

   

   

   

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• Fairness gets treated as something the tool delivers on day one, rather than something leaders need to monitor and adjust in practice.
• By learning which patterns of traits were associated with success, the system predicted the “fit” of new candidates, omitting names, schools, and other demographic markers from view. The idea seemed simple: Make hiring fairer through consistent, data-driven rules.
• HR prioritized consistency across all candidates, while frontline managers valued local context, recognizing that what makes a strong hire varies by role, market, and team.
• The algorithm, however, turned HR’s principles into rigid rules that were difficult to bypass.

• 1. What versions of fairness exist in our organization?

   

  When companies introduce AI in hiring, they often assume fairness has one clear meaning. In reality, fairness usually has several meanings, and they don’t always align.

   
• Leaders can mitigate these conflicts by uncovering what “fair” actually means across the organization. One practice that helps leaders reveal different definitions is to have data scientists and project leaders shadow the hiring process (as we as researchers did). These third-party observers should spend a meaningful amount of time evaluating what “fair” and “unfair” decisions look like for the different groups, from HR and frontline managers to legal and job candidates themselves.
• Equally important is creating “ethical infrastructures,” or dedicated spaces where diverse groups collectively reflect on and evaluate ethical dilemmas as they arise.

• 2. Who gives AI the power to decide what’s “fair” and on what basis?

   

  AI systems don’t decide what is fair on their own; people give them that power. Algorithms gain influence from the people and departments that choose to implement them, authorize their use, and position them as “objective” or “fair.”
• This dynamic isn’t unique. Across industries, the language of “fairness” and “anti-bias” often helps to push AI tools through and narrow the space for debate. When systems are presented as inherently fair, it becomes nearly impossible to question them without appearing anti-progress.
• Leaders must therefore scrutinize the language of fairness that accompanies AI initiatives. When a system is promoted as “ethical,” “scientific,” or “unbiased,” that is precisely the moment to slow down and ask: Who is making these claims, and what interests or agendas do they serve?
• Leaders can also balance claims by rethinking who sits on implementation teams

• 3. Which version of fairness does AI strengthen, and what gets lost over time?

   

  AI systems don’t just apply a definition of fairness, they strengthen it. Once principles are encoded in thresholds, models, and workflows, they take on new power. Unless leaders revisit these choices regularly, a single view of fairness becomes locked in while other valuable perspectives are lost out of sight.
• As a result, managers found themselves with candidates they considered strikingly similar because the system favored a narrow personality profile drawn from past employees. And because the organization lost sight of automatically rejected candidates, it didn’t know what potential it was missing.
• Leaders can prevent this drift by treating fairness as something to be continuously evaluated, not just a one-time exercise. Schedule regular reviews where data scientists, HR, frontline managers, and legal teams look at real hiring cases alongside model outputs and ask whether the system still reflects today’s priorities.
• Frontline managers should be central to these reviews. They see early when a threshold that worked in a pilot is constraining a regional pipeline or filtering out atypical but high-potential candidates.
• t gives teams tangible tools like AI Fairness 360 to evaluate models against multiple fairness measures, identify trade-offs, and run “what-if” checks – for instance, seeing how a stricter cutoff or new data input shifts outcomes for different groups.
• If you sponsor or lead AI hiring projects, you are the steward of fairness in your company, not the technology.
• Your job is to make sure multiple views of fairness are made visible during design, rollout, and use; to make explicit who gives the system its authority and on what basis; and to keep evaluating which version of fairness the system strengthens, and what gets pushed to the margins.
• Many companies invest heavily in AI but fail to generate real value from it. The difference between systems that deliver value and those that disappoint is rarely just the model. It’s whether leaders bring the right people into the room, ask the right questions, and keep testing assumptions about the realities of work.

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• it reinforced my emerging understanding of complex systems and how systems thinking, which was largely used in the context of engineering, could be applied to social systems and economies.
• Elements of systems were handled somewhat discretely rather than connected dynamically to the performance of other elements. While we did not abstract or make inferences about what might happen because of those relationships, it left a hole in our understanding.
• Simple and complicated systems work off a more logical mental model grounded in cause and effect. While complicated systems can be very complicated – think airplanes and nuclear plants – their components are integrated through triggers and sequences. In basic terms, there is a beginning and an endpoint that can be clearly defined.
• Complex systems rarely have a clear start or end point. They evolve rather than operate in the traditional sense.
• Many of the current problems I see in society are the result of framing the success of complex systems through a mindset developed to operate complicated systems…
• .…the belief that AI is able to do knowledge work more efficiently because there is a gap in understanding that the value of knowledge is created by trust and relationships, not just the knowledge itself.
• We keep measuring the success of human work by the standards we created to measure complicated industrial system
• A big part of this conflict stems from our assumptions about how to assess and communicate success – or the fear that even if we change how we assess success, others will not see it the same way.
• In complicated systems, which have easy to identify start and end points and predictable dynamics, fixed scores can be used to effectively assess and address gaps in performance
• We face the same conflict in the way we operate organizations. We treat jobs like a bucket of discrete skills, isolated from each other, relationships, and emotion. We treat success as demonstrations of knowledge when progress relies on influence, trust, and the ability to communicate, regardless of the specifics.
• The soft skills are always critical to the effectiveness and value of information – but impossible to nail down into universal observable result
• We use controls to ensure predictable human performance without understanding that controls do not optimize people the way they ensure reliability in machines. In fact, the more we try to control people, the more they resist and the worse their performance becomes.
• Complex systems can not be optimized by controlling for defined outputs. Complex systems are generative and, at their best, produce a wild array of diversity.

• By trying to control complex systems, we cut off the very excellence we want to inspire.

      
• They were assessing, essentially, what was good ‘enough,’ which is not a fixed measure but a measure of my expectations and experience.

• We see progress in complex systems by watching shifts in behavior and engagement, not by a fixed destination or output.

      
• Success is when we have done enough. What enough means is different for everyone and is critically defined differently by everyone.
• That may seem frustrating for those trying to design systems that work for a large organization. However, shifting to measure progress and behaviors vs. outputs is how to create universal measures that support a diverse community. Measuring behavior patterns also produces better intelligence about what boundaries might improve performance.

• The challenge is not fixing the systems we have – but reframing how we think about and understand the system itself.

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