• In fact, a philosophy professor at the University of California recently told Business Insider that workers are using AI at such a significant rate, it’s causing “skill atrophy.”
• the tech isn’t just automating processes; it’s filtering out opportunities for skills-building, according to Anastasia Berg.
• “Skills still matter, but it’s going to be a different set of skills” HR needs to help their workforces hone in the coming year, she predicts.
• his is particularly true for early-in-career talent, who need the opportunities to build these foundational skills.
• U.S. employment among workers under age 24 is the highest it’s been in four years. A study out this summer from Stanford University found that employment among 22-to-25-year-olds dropped 13% from 2022-25.
• others point to different macro factors, including a post-pandemic hiring slowdown, unstable economy and geopolitical tensions.
• After she led the implementation of the AI-powered AskHR—which handles 11.5 million transactions every year—she needed entry-level talent “more than ever.”
• But dialing back entry-level hiring will leave organizations with a significant gap in a few years, when they lack mid-level talent with foundational skills
• IBM is focused on providing workers across levels with domain expertise, alongside critical judgment and decision-making skills.
• Gen Z largely recognizes that need,
• she was impressed not just by their technical prowess but by the ease with which they confront real problems by leveraging technology.
• “They saw a problem and just built an app to solve this problem; they didn’t even think twice.

• “Why would companies not want to bring that talent in?” she asks.

    

     
• Hanson says HR now has an opportunity to help leadership see those opportunities, while providing this incoming generation with the chance to continue to focus on the core skills that will drive success in an AI-powered workplace.
• “If you want to grow, if you want to look for a new market, this is the talent, the energy you want to bring into your organization.

13 more annotations...

• Dynamic work demands continuous clarity, alignment, and feedback. With the right tools and communication, HR can redesign performance to happen in the flow of work, right where work actually happens.
• The shift is structural. You’re moving from annual reviews and episodic check-ins to continuous enablement. You’re aligning goals to the mission. You’re adjusting priorities as strategy evolves.
• “This is less a people manager problem and more a systems problem,” LeAnne said. “The systems that we have in our organization are not facilitating the kind of continuous conversations, continuous feedback, continuous listening in the flow of work that matters for our team members.”
• Together, he and LeAnne opened ChatGPT, pasted in the notes, and started prompting. The tool didn’t generate a script. It surfaced themes he couldn’t see on his own and helped him approach the conversation with more clarity and confidence. “It took fifteen minutes,” she said. “It felt more human, not less.”
• • Better insight. Instead of learning what went wrong at the end of the quarter, AI can surface trends as they emerge: engagement dips, goal drift, shifting workloads, and more. You and your managers get earlier signals and can intervene sooner.
      
  • Better goal alignment. AI can help employees see how their work connects to company priorities, and it can help managers adjust as strategy evolves without slowing the team down.
      
  • More effective coaching. During the demo, Betterworks showed how AI flags unclear or biased phrasing in a manager’s draft feedback and offers more effective alternatives — with explanations. The manager still owns the message, but AI helps it land with clarity and care.
• One of her clients gave employees an assignment: Spend a week exploring how AI could reduce admin load in their roles. They came back with ideas, not resistance. “It didn’t feel fearful, it felt exploratory,” she said.
• Anchor your messages to purpose, not efficiency.
• “We’re experimenting with AI to free you from admin work so you can focus on coaching.”
• They’re replacing annual compliance exercises with continuous opportunities for growth. 

7 more annotations...

• ne of the most critical roadblocks to achieving at‑scale impact with digital and AI (D&AI) transformations is having a sufficient bench of domain leaders. These are the N-2 and N-3 executives (that is, those two to three levels below the CEO) who lead a domain (either a business line or function) and drive end‑to‑end transformation with AI (see sidebar, “What is a domain”).
• AI tools are everywhere, but the skills to apply them to real business problems—improving customer experience while driving down unit costs at scale—are not.
• The CEO has to lead the transformation overall, and the C-suite needs to align on priorities and enterprise capabilities to enable AI. But it’s the domain leader who has the operational responsibility for translating the visions and plans into real change and real value.
• companies we have studied derive the majority of their benefits from a few deeply transformed domains.
• The most effective domain owners can speak to customer needs, strategy, organizational design, and operational performance—the traditional executive muscle. But they also have built up a “second muscle”: being tech savvy enough to develop an AI-enabled transformation road map, understand modern software delivery,
• Our analysis of LinkedIn profiles of senior leaders in Fortune 500 companies indicated that just 17 percent of their skill set is technical by nature, and only 5 percent of their careers included holding a technical role
• His vision was to provide homeowners with preapproved lines of credit, a much-streamlined application process, and automated back‑office operations so customers could receive their money in just a few days, compared with the industry standard time of as much as 35 or more days.
• Bringing this vision to reality required Adam to work side by side with the bank’s technology, credit, risk, compliance, strategy, and finance leaders to develop a road map of use cases that would deliver this new offering
• The bank’s leadership was excited about his plan and provided him with four cross-functional development teams.
• Finally, Adam owned the change management end to end—resolving channel conflicts, resequencing operations workflows, and upskilling colleagues across contact centers and branches.
• Reimagine their domain and create a transformative vision with the customer at the center. They understand customer pain points and unmet needs. They know the major sources of waste in their operations. They use creativity and pattern recognition to reimagine the business with AI—not just automate an existing workflow.
• Develop AI-enabled transformation road maps. They know how to work with domain experts and functional specialists to reimagine end‑to‑end processes through AI and technology.
• Oversee tech delivery. While they are not deep technology experts, these leaders have sufficient tech depth to oversee development, help their teams prioritize work, solve problems to overcome roadblocks, and effectively challenge their thinking.
• Lead end‑to‑end change management. Tech-capable business leaders don’t delegate the responsibility for implementing their AI solutions.

• Learn like it’s your mission

   

  Many companies have sophisticated training programs, but at the end of the day, domain leaders understand that they need to own their learning journeys. We call them “fearless learners,” and they are driven by curiosity and a passion to broaden and deepen their knowledge.

• Fixate on value and reimagine what it takes to get it

   

  AI pretenders chase a quick proof of concept to check a box on their performance review. “Using tech” leads their thinking. Van der Winden never starts with technology. Instead, he starts by asking what the biggest problems are, and then he figures out if a technological approach might solve them. “I’m not a data scientist,” he said. “What I’ve learned is to identify the attractive problems worth solving.”

• Understand the operational levers to get value from tech

   

  It’s never just tech when it comes to extracting value from tech. Domain leaders understand that ensuring technology delivers on its potential requires integrating systems—both technical and organizational. This systems-level thinking is essential to successfully rewiring the business.

   

• Get your hands dirty

   

  There is no substitute for learning on the job when it comes to building up your AI muscle. Clearly, business leaders need to rely on skilled technologists and engineers to solve problems. But that doesn’t mean simply delegating the work to them. Tech-savvy leaders get into the weeds with teams to develop solutions.

• Develop a nose for tech talent

   

  Talent has always mattered. But few leaders understand how mission critical that is when it comes to tech talent, where a top engineer or architect can generate ten times more value than their peers. In our Rewired work, we’ve observed that successful leaders spend most of their time on people questions, from recruiting to operating models that allow people to thrive in their work.

• Get comfortable with ambiguity and ‘stage gate’ your efforts

   

  This is one of the hardest things for senior business leaders who are assessed on “making their numbers.” If you break out of incrementalism and truly reimagine how your business could run differently, this will imply a certain level of ambiguity on the path forward. AI‑capable business leaders master the art of stage gating their efforts, achieving some economic benefits with every tranche of investment, and constantly pivoting their efforts as they encounter roadblocks.
• That means you’ll need about 75 to 150 leaders among your N-2 and N-3 population.
• Put the right people and incentives in place. Start by clarifying which core business processes and journeys have the most potential to be transformed by AI and ask yourself if you have the right current leaders in place. More specifically, do they have the leadership traits outlined earlier?
• Launch a strategic upskilling program. While product management is an important aspect of the new skill set, the job of AI‑capable domain leaders is broader and more complex. That’s because domain owners own an end‑to‑end business process with hundreds or thousands of people in business operations and customer service
• Fix the operating model to enable business‑led delivery. No amount of investment in upskilling will overcome an ineffective operating model. Specifically, engineering talent needs to be embedded in teams that work under the leadership of domain owners.
• They combine deep domain expertise specific to your business and the technology, data, and AI know-how that makes them formidable business transformers.

23 more annotations...

• 84% of AI implementation failures are leadership-driven, not technical.
• the real bottleneck is staring back at you in the mirror every morning.
• • Only 48% of AI pilots reach production (Gartner, 2024)
   
  • Average time to production for successful projects: 8 months
   
  • 90% of GenAI experiments never scale beyond pilot (MIT/McKinsey)
   
  • Two-thirds of organizations expect 30% or fewer experiments to scale in the next 3-6 months
   
  • AI project abandonment jumped 147% year-over-year
• • Organizations launch an average of 24 GenAI pilots
   
  • Only 3 reach production (Asia Pacific data)
   
  • 30% of GenAI projects will be abandoned after POC by end of 2025 (Gartner prediction)

• Laggards focus:

   
   
  • 70% effort on technology acquisition and deployment
   
  • 20% on data infrastructure
   
  • 10% on people and process

• High performers invert this:

   
   
  • 10% on algorithms
   
  • 20% on data and infrastructure
   
  • 70% on people, processes, and cultural transformation

• When surveyed, organizations cite these as their top AI adoption barriers:

   
   
  • 19%: Connecting AI agents across applications
   
  • 17%: Organizational change management
   
  • 14%: Employee adoption

• Shadow AI statistics:

   
   
  • 93% of executives use unauthorized AI tools
   
  • 57% of managers approve unauthorized tools
   
  • Average breach cost: $4.63M (IBM)
   
  • Only 28% have CEO-level oversight
• • 2020: 59% of organizations had an AI strategy
   
  • 2024: 39% have one
   
  • Adoption: 55% → 78%
   
   
• • Only 44% of CEOs believe their CIOs are AI-savvy
   
  • Only 1/3 prioritize training
   
  • No clear ownership for AI
   
   

• . CEO owns the transformation

   

  Monday action:

   
   
  • CEO declares AI a business transformation
   
  • Direct reporting line
   
  • 30% of leadership meeting time goes to adoption issues

• 2. Kill the 70% tech focus

   

  Monday action:

   
   
  • Audit AI spending
   
  • If <50% is people/process, fix it
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• 4. Make the safe choice the easy choice

   

  Monday action:

   
   
  • Measure time-to-access
   
  • If >5 minutes → rebuild process

• 5. Strategic clarity before deployment

   

  Monday action:

   
   
  • Cancel vendor demos
   
  • Hold a strategy session
• The brain resists because of uncertainty withdrawal and loss of confirmation rewards.

14 more annotations...

• Stress can be stimulating; when kept in check, it sharpens focus and fuels energy. Burnout is an outcome of unmanaged chronic stress that develops over time.
• We conducted research with 94 working professionals to unpack the experience of overwhelm at work. Through narrative descriptions coupled with survey data, we captured how they describe it, what causes it, and the impact it has—on their thoughts, emotions, relationships, and work outcomes.
• Alert yet exhausted. People feel wired by stress hormones yet physically depleted—in other words, they’re desperate for rest but unable to shut off.
• Mentally frozen yet wanting to escape. There’s a strong and sometimes highly consequential impulse to flee the overwhelming situation, while rational cognitive processing shuts down simultaneously, leaving you feeling stuck.
• Internally breaking down behind a calm impression. Many hide their distress to maintain composure. “I was calm and collected on the outside, but internally I was screaming.”
• Control (predictability). Overwhelm surged when people felt powerless to influence their situation or didn’t see challenges coming. One participant described it as “a fast-paced game of coping for an unknown duration—you don’t know how long it’s going to last or what else is coming your way.”
• Work standards and expectations (norms and fairness). Many participants felt crushed by unrealistic or unfair expectations, either self-imposed or external. They spoke of harsh inner dialogue: “I’m supposed to be able to do it; I’m paid for it; people expect it from me… maybe I’m just not good enough.”
• Resources (recovery). Overwhelm frequently struck when people were already depleted from a lack of time, staffing, support, or energy

• Spot both the silence and the strain.

   

  Overwhelm often lacks outward expression because employees suppress it to maintain composure, or they freeze in the face of overwhelming demands. Leaders can spot early signs by observing shifts in behavior or energy—for example, withdrawal or decision paralysis during meetings that could be mistaken for demotivation, or restlessness and frantic working with no breaks that could be interpreted as excitement

• Engineer micro-control in a macro-uncertain world.

   

  Unpredictability is a core trigger of overwhelm. When overwhelmed, people often begin struggling to prioritize. We’ve seen that supporting your people with small doses of predictability—knowing what’s next, what matters most, and what can wait—dramatically reduces perceptions of demand overload

• Recalibrate standards—starting with your own.

   

  Overwhelm thrives in cultures of perfectionism and invisible expectations, so leaders should model what “good enough” looks like. Replace unspoken performance ideals with explicit, shared standards.

• Create psychological permission to say “I’m at capacity.”

   

  The most effective buffer against overwhelm isn’t an individual coping tactic, it’s a social one. Make it psychologically safe for people to set boundaries without stigma or risk of reprisal.

• Design work for recovery, not endurance.

   

  Overwhelm emerges when resources deplete under sustained demand. Leaders should normalize regular micro-breaks, mental detachment, exercise, and rest as legitimate performance practices.

11 more annotations...

• • L’IA générative fait évoluer les systèmes d’informations traditionnels vers de véritables environnements numériques de travail intégrés, où les agents sont au cœur de la collaboration quotidienne ;
      
  • La bataille ne se joue plus sur la puissance des modèles génératifs, mais sur leur intégration aux outils et habitudes de travail, ce qui donne un avantage décisif aux grands éditeurs déjà massivement déployés, Microsoft en tête ;
      
  • Avec Copilot et son écosystème, Microsoft pousse le concept de « Frontier Firm » : des organisations reposant sur des équipes hybrides où collaborent des salariés et des agents intelligents ;
      
  • L’offre de Microsoft s’enrichit de couches sémantiques qui visent à modéliser en profondeur les personnes, les processus, les données et les connaissances des entreprises afin de comprendre leur fonctionnement et limites ;
      
  • Cette modélisation ouvre la voie à la création de jumeaux numériques des salariés, processus et même d’organisations entières, avec un niveau d’autonomie très élevé, ce qui impose de repenser en urgence nos pratiques et notre culture du travail.
• l’Entreprise 2.0 était un sujet plus confidentiel, mais hautement structurant pour le quotidien des collaborateurs.
• Si tout le monde est d’accord sur l’intérêt et la pertinence de ces environnements numériques de travail unifiés, force est de constater que leur adoption est lente et laborieuse, car les salariés se sont forgé au fil des années un modèle mental reposant sur les emails et fichiers qu’il est très difficile de faire évoluer.
• Le créneau de la collaboration n’est bien évidemment pas en reste, puisqu’il existe de nombreuses solutions de collaboration augmentée reposant sur les modèles génératifs.
• e champ de bataille s’est déplacé : l’important n’est plus de créer des agents, mais d’en créer dans un écosystème intégré, au sein de votre environnement de travail
• Microsoft a ainsi développé tout un discours autour des « Frontier Firms« , les organisations qui reposent sur des équipes hybrides composées d’humains et d’agents intelligents
• Là encore, si tout le monde est d’accord pour reconnaitre l’intérêt et la pertinence des agents intelligents dans un contexte professionnel, leur déploiement à grande échelle est complexe, tandis que leur adoption est lente, car il faut revoir à la fois les habitudes et les outils de travail
• toutes les entreprises fonctionnent aujourd’hui avec des outils informatiques hérités de logiciels conçus au siècle dernier, ainsi qu’un agrégat d’applications en ligne plus ou moins bien intégrées.
• D’où la tentation de faire table rase du passé pour tout miser sur l’internet et proposer une plateforme intégrée dans le cloud. Non seulement c’est plus simple pour les utilisateurs (ils y accèdent avec un navigateur), mais c’est surtout beaucoup plus souple pour les éditeurs qui peuvent faire évoluer leur solution tous les jours.
• • En premier lieu Microsoft avec Copilot qui est maintenant intégré à MS 365 ;
      
  • Google qui pousse Gemini dans Workspace ;
      
  • Les portails SaaS plus récents comme Zoho avec Zia et Zoom avec leur AI Compagnion ;
      
  • Les solutions de stockage intelligentes comme Dropbox avec Dash ou Box avec Box AI ;
      
  • Les solutions de capitalisation comme Notion avec Notion AI ou Atlassian avec Rovo ;
      
  • Des solutions plus pointues comme Airtable avec AI Plays ou Superhuman qui vient d’absorber Grammarly et Coda ;
      
  • Enfin des solutions toutes récentes comme Delos ou Genspark avec son Super Agent.
• La promesse qui est faite par ces différents éditeurs est grosso modo toujours la même : s’appuyer sur l’IA générative pour faciliter et stimuler la collaboration afin de gagner en productivité et en créativité.
• Les éditeurs ont ainsi le choix d’exploiter les meilleurs modèles disponibles sur le marché, qu’ils soient proposés sous forme de licence ou d’APIs (GPT, Claude, Gemini…), ou en open source avec des modèles qui proposent des performances à peine inférieures à leurs contre-parties payantes :
• La collaboration augmentée grâce à l’IA générative dans un environnement intégré où se côtoient les collaborateurs et des agents intelligents,
• notamment une présence renforcée de Copilot au sein de l’interface de Microsoft 365, où il est notamment possible de basculer entre les modes « Work » et « Web » pour avoir accès aux documents et applications internes, ou pour exploiter la version grand public du chatbot de Microsoft :
• une intégration beaucoup plus poussée de Copilot dans tous les logiciels de la suite Office,
• Il y a également une version plus simple à prendre en main de Copilot Studio, beaucoup moins impressionnante pour les novices.
• icrosoft semble avoir pris de l’avance avec un environnement numérique de travail complet déjà déployé auprès de centaines de millions d’utilisateurs, qui ont maintenant accès à un chatbot très polyvalent (il est possible de choisir entre plusieurs modèles de référence : GPT, Claude…), et des fonctionnalités agentiques viables
• plusieurs couches sémantiques pour améliorer la connaissance et la compréhension des employés avec le Microsoft Graph, les « People Skills« , et maintenant le Work IQ ; des données avec Fabric IQ ; et des connaissances avec Foundry IQ.
• une modélisation formelle des actifs de l’entreprise (ses employés, ses données et ses connaissances) afin de pouvoir repenser son fonctionnement à travers l’IA et les agents intelligents.
• C’est cette multi-couche d’intelligence qui va permettre de comprendre le fonctionnement d’une entreprise (ses processus, ses frictions, ses goulots d’étranglement…) pour pouvoir y apporter des solutions.
• des solutions équivalentes comme Reejig qui permet d’analyser les flux de travail et de modéliser les processus internes.
• Cette modélisation se fait aussi à un niveau individuel, puisque ce sont les emails et fichiers de chaque collaborateurs qui sont analysés pour pouvoir mieux cerner leur rôle et responsabilités :
• Tout ça est très intéressant, mais cette solution est clairement incompatible avec la culture et même la législation française (c’est carrément du flicage algorithmique).
• ce qui a été annoncé par Microsoft est un vaste chantier d’exploration et de modélisation des actifs numériques d’une entreprise pour en comprendre le fonctionnement exact :
• • Work IQ, un premier niveau sémantique de compréhension du rôle et des responsabilités de chaque collaborateur à partir de ses emails, fichiers, conversations… ;
      
  • Fabric IQ, un second niveau de compréhension du fonctionnement de l’entreprise à partir des outils (internes et externes), des données et des processus ;
      
  • Foundry IQ, un troisième niveau de compréhension des savoirs de l’entreprise à partir des référentiels et projets.
• pouvoir renforcer les équipes humaines avec des collaborateurs synthétiques (les chatbots personnalisés et les agents
• la création de jumeaux numériques pour les salariés, projets, processus…
• un double numérique lui permettant de se faire représenter dans une réunion par un avatar capable de répondre aux questions des participants
• r la création d’agents génériques en fonction du métier et du niveau hiérarchique de chaque collaborateur
• Puis, au fur et à mesure que l’IA apprendrait à mieux connaitre les compétences, habitudes et attributions des salariés (projet, rôles, responsabilités…), elle serait en mesure de leur faire des recommandations pro-actives d’agents personnalisés pour automatiser leurs flux de travail et leur permettre de gagner en productivité
• nous pourrions envisager un fonctionnement en « tâche de fond » où ces jumeaux solliciteraient des agents de façon autonome pour effectuer des tâches qu’ils jugent possible de réaliser avec une très faible marge d’erreur, pour systématiser la capitalisation de connaissance
• Mon objectif n’est pas de vous faire peur, mais de vous aider à mieux apprécier l’importance des annonces de la semaine dernière, et à anticiper la direction que prend l’évolution des solutions de Microsoft.
• Ne nous trompons pas de combat : ce n’est pas celui de l’homme contre la machine, mais de l’homme contre ses pires travers.

32 more annotations...

• omething most surface metrics completely miss: adoption does not equal belief. And that disconnect isn’t irrational—it’s arguably the most clear-eyed response to technology we’ve seen in a decade.
• As of late 2025, 84% of developers are using AI coding tools daily. But only 33% trust their accuracy. More than that—45% report that debugging AI-generated code now takes longer than just starting from scratch.
• Fifty-three percent of Americans are more concerned than excited about AI—up from 38% in 2022. Among skeptics, 88% believe AI will reduce jobs, and 76% of adults say it’s vital to know when content is AI-generated
• Fifty-four percent of people are wary about trusting AI, and even in advanced economies where adoption is high, the majority report feeling both optimistic and worried.
• his is mainstream professional behavior: adopt the tool, distrust the output, spend more time verifying than you saved generating.
• Code reviews that weren’t needed before. Double-checking. Rewriting. Quietly defaulting back to manual processes for anything critical. Adoption shows up beautifully in your top-line metrics
• But the cost of all that verification? That friction? It doesn’t show up anywhere. It hides in extended timelines, frustrated teams,
• . Content teams adopt AI writing aids, then quietly rebuild entire processes to “humanize” outputs for brand credibility. In education, teachers are pushing back hard—going back to in-class essays and oral exams because digital work has made cheating too easy to detect
• Physical environment: Communities are blocking data centers and lobbying for regulation to manage resource competition and noise.
• Creative work: Boycotts are fierce and fast. When brands overstep with AI-generated creative, users don’t write complaint letters—they leave.
• Education: This one frustrates me because it’s so telling. Schools are doing what I’m calling “analog correction”—reverting to pen-and-paper exams because the verification cost of digital work now exceeds its value
• Professional anxiety: Workers are caught in a competence crisis I don’t think we’re talking about enough. They’re expected to adopt tools or risk irrelevance.

• Use AI for tasks you hate. Protect tasks you love.

   
• Let automation handle the routine. But keep creative, relationship-driven, or reputation-critical work protected.
• I’m genuinely uncertain whether most organizations understand what’s coming. The next 18 months will show who’s closing the gap between adoption and trust, and who’s just adding automation because everyone else is
• Design for verification from the start
• Measure the hidden costs, not just the surface savings
• Preserve human judgment on high-impact decisions.
• Be honest about how and when AI is used.
• Technology that delivers value and keeps trust earns a place. Technology that undercuts trust gets quietly abandoned—no matter how impressive the adoption curve looks this quarter.

18 more annotations...

• OpenAI most recently committed to spending well over a trillion dollars before the end of the decade as it continues to burn oodles of cash each quarter, enormous losses that are having investors asking some hard questions.
• Put simply, as IBM CEO Arvind Krishna told The Verge‘s editor-in-chief Nilay Patel during a recent episode of the “Decoder” podcast, the math isn’t adding up.
• “It takes about $80 billion to fill up a one-gigawatt data center,” he said. “That’s today’s number. If one company is going to commit 20-30 gigawatts, that’s $1.5 trillion of [capital expenditure].”
• “It’s my view that there’s no way you’re going to get a return on that because $8 trillion of [capital expenditure] means you need roughly $800 billion of profit just to pay for the interest,” he concluded.
• despite what the Wall Street Journal recently described as the lack of a “clear financial model for profitable AI.”
• According to a recent analysis by investment bank HSBC, OpenAI won’t be making any profit for at least another four years, and will need to keep burning over $200 billion to keep up with its growth plans in terms of additional debt, equity — or new avenues of generating revenue,
• IBM executives are asking candidates whether they believe we’re in an AI bubble, a question that purportedly has no right or wrong answers.
• Besides some murky math, experts have long questioned Altman on his push to realize AGI, a term that remains nebulous at best, with OpenAI repeatedly being accused of shifting the goalposts to give the impression of progress.
• l. “I am not convinced, or rather I give it really low odds — we’re talking like 0 to 1 percent — that the current set of known technologies gets us to AGI.”
• he believes that generative AI will be “incredibly useful for enterprise” and “unlock trillions of dollars of productivity.
• getting to AGI will necessitate technological breakthroughs that will take us beyond large language models, Krishna argued.
• “If we can figure out a way to fuse knowledge with LLMs,” we stand a chance of reaching AGI, he said. “Even then, I’m a maybe.”

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• OpenAI forecasts revenue of $345 billion between 2024 and 2029. Assuming this is all cash, we then calculate $488bn of spending, mainly to pay for access to compute, to arrive at their projected negative cumulative free cash flow of $143bn. And that was before the most recent announcements of $1.4 trillion in data centre commitments. A broker has more recently said the cash burn could exceed $200bn by 2030.
• We added in OpenAI’s expected cash burn and also included its rival Anthropic, also using data from the Journal. I double-checked the historic numbers against Bloomberg data and they were broadly in line.
• Time Warner’s $99 billion loss in 2002 and a similar size loss for AIG in 2008. Meanwhile, Fannie Mae and Freddie Mac lost $77bn and $59bn respectively within three quarters when the GFC struck.
• "Happy Birthday, ChatGPT?” includes exclusive transactions data from dbDataInsights (dbDIG) showing how subscriptions in major European markets have more or less flatlined since May
• But at present, no start-up in history has operated with losses on anything approaching this scale. We are firmly in uncharted territory.

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• Resource Management

   
• • Predictive Analytics: AI analyzes historical and real-time data, allowing COOs to order supplies and manage inventory efficiently.
   
  • Automated Scheduling: Once a pattern is established, COOs can automate ordering schedules and assign resources to align with projects.
   
  • Real-Time Monitoring: The technology can monitor inventory and processes in real time, ensuring COOs are on top of delivery schedules and know when to place orders.
   
  • Enhance Decision-Making: COOs can leverage artificial intelligence and analytics to make smarter operational decisions.
   
  • Process Optimization: AI can identify work inefficiencies to ensure COOs develop the most effective processes. It also balances workflows evenly among teams, ensuring no one is overworked.

• Performance Monitoring

   

• With AI, performance monitoring is enhanced with the following features.

   
   
  • Automated and Objective Data: AI tools can use various metrics to measure each employee’s performance. The non-biased view ensures everyone is evaluated equally.
   
  • Real-Time Monitoring: Performance is monitored in real time, and alerts are sent when a company falls behind its goals, ensuring improvements are made.
   
  • Personalized Feedback and Development: The technology can provide performance testing and assess results, helping workers improve in specific areas.
   
  • Predictive Analytics: This strategy can be used to predict future outcomes based on past performance trends.

• Cross-Functional Collaboration

   

• AI has made these processes easier with the following systems.

   
   
  • Streamlines Workflows: AI tools can identify bottlenecks across departments, ensuring teams work well together.
   
  • Data Insights: The technology gathers data from across departments to break down silos, predict trends, make recommendations, and ensure all departments have access to the same, up-to-date information.
   
  • Improved Communication: Automated systems can help COOs create reports that can easily be shared among departments. It can also identify and connect individuals across departments to support innovation.

• Additional COO Tasks

   
• • Choosing Technologies: A COO must now determine which technologies are best suited to boost operations in their company, considering security, ease of use, and efficiency. They may partner with the CIO to make these decisions. However, their insight will be necessary.
   
  • Compliance: AI accesses data, utilizes the internet, and is, therefore, prone to security issues. The COO must ensure that the technology they use does not pose any compliance risks. It should align with GDPR and ensure transparency, explainability, and bias mitigation.
   
  • Operational Transformation: The COO must lead the operational transformation within their department, guiding the culture, and ensuring everyone is trained on new technology. They must address anxieties that may arise from the shift.
   
  • ROI Focus: Returns should be at the heart of every business decision. COOs must ensure the new technology helps generate revenue and boost efficiency without significantly eating into the company’s budget.
   
  • Adaptability: Though more a skill than a task, COOs must learn to be adaptable in today’s age of transformation. They should be ready to adopt new technologies quickly and pivot from one tool to another if situations change or systems fail.

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