Pascal Bornet

🎉 Our Book "AGENTIC ARTIFICIAL INTELLIGENCE" Is Finally Here! 🎉 Friends, today's the day! After months of hard work, I'm beyond excited to announce our new book, "Agentic Artificial Intelligence" — a practical, non-technical guide for business leaders, entrepreneurs, and curious minds. zurl.co/kbbH6 I'm incredibly proud that we brought together 27 brilliant minds from across business, academia, and programming to create this. It wasn't always easy, but we were driven by a shared goal: to bring real clarity to this field based on our hands-on experience implementing agentic AI in many companies. As Bill Gates put it, "Agents are bringing about the biggest revolution in computing since we went from typing commands to tapping on icons." But what does this really mean for you? Our book cuts through the hype to offer: 👉 Practical steps to find where AI agents can create actual value in your work 👉 Real stories of organizations cutting costs by over 25% while making customers 40% happier 👉 Honest advice on avoiding the pitfalls we've seen firsthand 👉 A clear view of how these technologies will reshape business and society Based on the content of the book, we have built the First Executive Masterclass on Agentic AI Strategy and Implementation. Join us here: zurl.co/aDVed This isn't about whether AI agents will transform your industry—it's about how you'll lead that change. I'd love to hear your thoughts once you've had a chance to read it. Your feedback will help all of us push this exciting field forward! #AgenticAI #AIBook #FutureOfWork #AITransformation #Leadership

Nine co-authors. Over a hundred contributors. One book. The Human-Agent Orchestrator is out today. We are the last generation to manage only humans. We wrote the playbook for what comes next, and for right now. I will be honest: this book exists because we got it wrong first. Across hundreds of deployments, we watched organizations — and ourselves — fail at something that looked simple on paper. Not because the technology broke. Because nobody had built the management layer around it. That gap kept us up at night. This book is our answer to it. Four years of research across 432 organizations, and more failed deployments than we would like to admit. That is what this book is built from. Marshall Goldsmith wrote the foreword. Andrew Ng called it out. And somewhere in the middle of all of it, a team of nine co-authors and over a hundred contributors built something I believe will genuinely help leaders navigate what is coming. I could not have done this without them. Today is theirs as much as mine. If this resonates, share it. The more leaders see it, the more it matters. Here is the link to the book: zurl.co/nfAcC Please read it and let me know your views. I look forward to the discussion! #AgenticAI #AILeadership #TheOrchestrator #HumanAgentOrchestrator #FutureOfWork #AIManagement #HybridTeams #ArtificialIntelligence

Professional athletes train most of the time to perform when it matters. Corporate teams perform all the time and get one AI workshop called “transformation.” What worries me is how often we confuse access with ability. Giving people AI tools is not the same as building AI competence. That is not upskilling. That is corporate theater with slides. AI competence is not built in a calendar invite. It is built through practice, feedback, and real workflows. Are companies training people for AI, or just pretending they are? #AI #FutureOfWork #AIAdoption #Upskilling #Leadership

Birdsong is not just sound. It is data made physical. If you could see the air at the exact moment a hemp bunting sings, you would not see empty space. You would see a structured three-dimensional data array. What we hear as a soft “chirp” can be mapped as frequencies, rhythms, amplitudes, and harmonic relationships. A scatter plot turns a fleeting song into a topographic map of sound. And the technical beauty is remarkable: → Sound is a mechanical wave, built from compressions and rarefactions in the air. → The bird controls it through the syrinx, a vocal organ capable of generating two frequencies at once. → Frequency shapes pitch. → Amplitude shapes volume and cluster density. → Timbre creates the unique waveform, the texture of each “sound island.” → Each cluster shows the acoustic proximity of syllables and motifs. What looks like chaos is not chaos. It is a bioengineered signal. Territory. Genetic profile. Hormonal state. Aggression level. Mating fitness. All encoded into patterns of pitch, timing, volume, and timbre. This is why I find it so fascinating. A bird is not simply singing into the air. It is organizing the air. It is carving space into sectors of influence using sound pressure. It is making a physical claim to territory. For some, birdsong is peaceful background music. For others, it is a complex mathematical model calibrated by millions of years of evolution for survival. And here is the urgent lesson for the AI age: We often mistake invisible systems for simplicity. A bird sings, and we hear romance. An AI responds, and we see magic. But underneath both are signals, compression, feedback loops, optimization, and information architecture. The future belongs to those who can read what others dismiss as noise. So I’ll ask you: When you hear birdsong, do you hear music, data, or both? #AI #ArtificialIntelligence #Bioacoustics #Nature #Technology #Data #MachineLearning #Innovation #FutureOfWork #Signals #Evolution

AI is automating tasks. Gen Z is stress-testing workplace norms. This clip is satire, but it points to something real. What stands out to me is not the bed, the camera, or the contract. It is the gap between what companies assume and what employees believe they owe. The future of work is not only about AI tools. It is about expectations. Remote work blurred the office. AI is now blurring the job. And Gen Z is asking the uncomfortable question many companies avoided: What exactly are you paying me for? The output? The hours? The camera? The clothing? The performance of professionalism? This is why the next workplace challenge is not just automation. It is clarity. Because if companies do not define outcomes, AI will optimize tasks, employees will optimize loopholes, and culture will become a negotiation. Where do you think the line is between flexibility and professionalism? #AI #FutureOfWork #GenZ #RemoteWork #WorkplaceCulture #Automation #Leadership #DigitalTransformation

AI: solving the 100-meter walk problem while the car stays dirty. That is the risk of AI without context. It can give a perfectly logical answer: “Walk. It is only 100 meters.” But the goal was never to move the person. The goal was to move the car. This is why I believe AI adoption cannot be only about better prompts. It has to be about better context, better workflows, and better understanding of the actual job to be done. Because a smart answer to the wrong problem is still the wrong answer. Where have you seen AI miss the obvious? #AI #ArtificialIntelligence #ChatGPT #Automation #FutureOfWork #WorkflowAutomation #ResponsibleAI

The evolution of surveillance is quite impressive. In the past, the fear was: “Someone might be listening.” Today, the expectation is: “Someone better be listening, because I need a pancake recipe.” What once felt intrusive is now called AI-powered personalization. Same microphone. Better UX. Better branding. The question is: when does helpful become creepy? #AI #ArtificialIntelligence #DataPrivacy #Technology #ResponsibleAI Image credit: Ralph

An astronaut looked back at Earth and saw something most leaders still miss. Everything is connected. That is what stays with me in reflections like this. From space, there are no borders. No departments. No quarterly silos. No neat separation between economy, society, and planet. Just one fragile system. And that view exposes a truth many of us still resist: we keep managing the world as if the parts can survive without the whole. They cannot. We talk about growth as if it sits above everything else. We treat society like a support function. And we behave as if the planet is just the backdrop. That is the lie. The order is the opposite. → planet → society → economy That is not ideology. It is systems logic. Because without a functioning planet, society destabilizes. And without a functioning society, the economy is just a spreadsheet waiting to break. What I find most powerful about the astronaut perspective is how brutally simple it makes things. Distance removes the illusion. And suddenly our priorities look upside down. To me, that is the real lesson for leadership now. The future will not belong to the people who optimize one part of the system while degrading the rest. It will belong to those who understand that resilience, prosperity, and survival are all part of the same equation. If you stepped far enough back from your business, your country, or your industry, what priority would suddenly look completely wrong? #Leadership #SystemsThinking #Sustainability #FutureOfWork #Innovation #Planet #Society #Economy #BusinessStrategy

Same industry. Completely different economics. And that is exactly why this image matters. At first glance, it looks like a staffing comparison. It is not. It is a strategy comparison. Emirates is built around premium service, widebody operations, and a high-touch customer experience. Ryanair is built around simplicity, speed, standardization, and relentless cost discipline. Both win. That is the part I think many leaders still underestimate. Efficiency is not about having fewer people. It is about building a system where everything matches: → cost structure → customer promise → operating model → pricing power What I keep seeing across industries is this: companies rarely fail because they chose the “wrong” model. They fail because they copy someone else’s model without copying the logic that makes it work. That is where things break. Emirates and Ryanair are both operationally strong. They just optimize for different outcomes. To me, that is the real lesson here. You do not need the same model to win. You need a coherent one. Because the moment your pricing, service promise, and operating reality stop aligning, the whole business starts fighting itself. If you copied one of these models into your company tomorrow, would it create efficiency, or chaos? #BusinessStrategy #Leadership #Operations #Airlines #Efficiency #Innovation #Scaling #FutureOfWork #Management

Most people still think Agentic AI is just ChatGPT plus tools. They are wrong. This diagram matters because it shows the full stack. Five layers. And in my view, most failures do not happen in the models. They happen in layers four and five. 𝟭/ 𝗔𝗜 & 𝗠𝗟 𝗧𝗵𝗲 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 This is where data becomes decisions. Supervised, unsupervised, reinforcement learning. 𝟮/ 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗧𝗵𝗲 𝗘𝗻𝗴𝗶𝗻𝗲 Neural networks. Transformers. The machinery learns patterns at scale. 𝟯/ 𝗚𝗲𝗻𝗔𝗜 𝗧𝗵𝗲 𝗖𝗿𝗲𝗮𝘁𝗶𝘃𝗲 LLMs generate content. RAG brings context. Multimodal models handle text, image, audio, video. That is the output layer. 𝟰/ 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗧𝗵𝗲 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 This is where AI becomes operational. → planning → tool use → orchestration → context and memory → human oversight This is where systems start becoming fragile. 𝟱/ 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗧𝗵𝗲 𝗦𝘆𝘀𝘁𝗲𝗺 This is the most underestimated layer. And this is where autonomy becomes real. → governance → safety → guardrails → observability → tracing → rollback → failure recovery → cost control → multi-agent coordination That is the real lesson. Clever algorithms do not guarantee reliability. Architecture does. Without audit trails, debugging, reversibility, and clear boundaries, you have not built autonomy. You have built an unpredictable script. So maybe the wrong question is: Which technology should I pick? The better one is: How does this system behave when things go wrong? What do you think breaks agentic systems more often today: weak models, or weak architecture in layers four and five? #AI #AgenticAI #AIAgents #ArtificialIntelligence #Architecture #MachineLearning #Innovation #FutureOfWork #Technology

The best thing companies about AI right now has almost nothing to do with AI. Fix your data. This is the same pattern I keep seeing. Everyone wants the exciting layer: copilots, agents, demos, dashboards. Almost nobody wants the foundation: definitions, ownership, permissions, quality, lineage. And then people act surprised when the AI project stalls right after the demo. To me, the bottleneck is rarely the model. It is the data layer the company built, neglected, or never governed properly. What usually happens is painfully predictable: 1. Teams start with prompts and use cases 2. Then they cannot find the right data 3. Or the data is inconsistent 4. Or access is slow 5. Or nobody trusts the output And suddenly the AI initiative becomes a data problem. If you want AI that ships and scales, start there. Fix access. Improve quality. Clarify ownership. Build trust in the data. It is not the glamorous part. It is the part that makes everything else possible. What breaks more AI projects today: weak models, or weak data foundations? #AI #GenAI #DataQuality #DataGovernance #DigitalTransformation #BusinessStrategy #Innovation #FutureOfWork #Technology

This may be the most honest picture of generative AI. When AI is trained on flawed data, it does not just inherit the problem. It becomes a very efficient amplifier of it. That is the part too many people still underestimate. → bad data in → scalable inaccuracy out To me, this is one of the biggest blind spots in AI. People obsess over model quality. Far fewer ask whether the source material deserves that much amplification in the first place. Because scaling knowledge with AI also means scaling responsibility in data sourcing. Just saying. What do you think is the bigger risk right now: weak models, or bad data being amplified at machine speed? #AI #GenerativeAI #DataQuality #MachineLearning #Innovation #Technology #DigitalTrust #FutureOfWork Photo credits: Ralph

The era of one clear AI leader is fading fast. It is no longer just about who started first. It is about who can stay ahead when everyone is getting stronger, faster, and cheaper. That is what stands out to me now. When ChatGPT launched in 2022, it did more than release a product. It triggered a global AI race. AI moved from a niche technology to a mainstream tool almost overnight, and for a while OpenAI looked clearly ahead. But that window did not stay open for long. By 2024 to 2026, the field had changed dramatically. Google pushed hard with Gemini. Anthropic kept advancing with Claude. New players like DeepSeek showed that powerful models did not always need the same cost structure or the same playbook. That is the real shift. The lead is no longer as wide. The gap is no longer as safe. And the market is no longer as easy to dominate. What also changed is the nature of the competition. This is no longer just a chatbot race. It is now about reasoning models, multimodal systems, lower-cost training, open-source momentum, and who can turn intelligence into something useful across text, code, images, video, and real workflows. → stronger challengers → lower costs → wider access → less room for complacency To me, this is where the story gets much more interesting. The first phase of AI was about surprise. This phase is about convergence. Performance is tightening. Access is expanding. And the advantage is becoming harder to defend on raw model quality alone. That means the next winners may not be decided only by who has the smartest model. They may be decided by ecosystem, speed, distribution, trust, and how well they turn capability into everyday utility. And that is why this moment feels important. The AI race is no longer just about building intelligence. It is about building everything around it faster than everyone else. As top models get closer in performance, what do you think will matter more next: the model itself, or the ecosystem built around it? #AI #ArtificialIntelligence #OpenAI #Google #Anthropic #DeepSeek #Innovation #FutureOfWork #Technology #MachineLearning

HP may have just made the desktop PC feel a little outdated. What caught my attention is not just that they built a full computer into a keyboard. It is what that changes. The HP EliteBoard G1a turns a familiar object into the machine itself. Plug it into a monitor, and your keyboard becomes your full PC. That is the innovation. Not just smaller hardware. A different model of computing. And that matters because work no longer happens at one fixed desk, in one fixed setup, with one fixed machine. People move. Desks change. Workspaces are shared. And the old desktop model keeps creating friction. This design pushes in the opposite direction: → full PC inside the keyboard → AI-ready performance built in → portable between desks → desktop experience without the usual bulk To me, that is where this gets interesting. This is not just a clever hardware trick. It unlocks real use cases: → hot-desking without setup chaos → cleaner workstations in smaller offices → easier mobility for hybrid teams → personal computing that travels like a notebook but works like a desktop That is a meaningful shift. Because once the computer disappears into the object you already use every day, the whole idea of the desktop starts to change. Less machine. Less friction. More flexibility. Would you actually want your full PC built into your keyboard? #AI #HP #Innovation #FutureOfWork #Computing #Hardware #Technology #WorkplaceInnovation #CopilotPlusPC

What worries me is not that children are growing up with technology. It is that too many are growing up with less space to imagine without it. And in an AI-driven world, that matters more than ever. Because the qualities we will value most in the future are not the easiest ones to automate: → curiosity → creativity → connection → judgment → humanity That is why I keep coming back to this question: Are we giving children enough room to create something from nothing? When I look at how fast screens, algorithms, and instant entertainment have taken over childhood, I do not think the problem is technology itself. The problem is what gets squeezed out when every spare moment is filled for them. Unstructured play. Boredom. Mess. Trial and error. The awkward, beautiful process of making something without being told what to do next. That is where so much real creativity begins. And I think we underestimate what children lose when that space disappears. To me, this is the deeper issue. AI is powerful. Screens are powerful. But neither should replace the experiences that teach children how to imagine, relate, struggle, and grow. So if I had to focus on a few things that matter most, it would be these: → curiosity over constant consumption → creativity over passive entertainment → connection over distraction → real-world problem solving over endless screen comfort → human values over digital convenience Because the future will not belong only to the children who know how to use machines. It will belong to the children who still know how to be deeply human alongside them. Are children losing their creative edge, or are we simply not protecting the conditions that help creativity grow? #AI #Parenting #Creativity #Education #FutureOfWork #ChildDevelopment #Learning #Technology #HumanSkills

China is turning fire trucks into drone launch systems. And that is a much bigger shift than it sounds. What interests me here is not just the hardware. It is the new logic of emergency response. Instead of relying only on ladders and human entry, these systems pair fire trucks with drones that can reach high-rise fire zones quickly, fly into smoke, and send live intelligence back to crews. That is what is new. The truck is no longer just transport. It becomes a mobile aerial response base. And that matters because in dense high-rise environments, access is often the real bottleneck. To me, this is where the story gets interesting. This is not just about fighting fires better. It is about changing who gets exposed to danger first. → drones go where ladders cannot → commanders get visibility earlier → crews make faster decisions → fewer firefighters enter blind conditions That is a serious innovation. And it opens up important use cases: → faster high-rise reconnaissance → targeted suppression from outside upper floors → better coordination in smoke-heavy environments → safer response where humans cannot reach quickly That is why I would not dismiss this as just another drone demo. It is a glimpse of what emergency response looks like when robotics, data, and frontline operations finally converge. What do you think matters more here: faster firefighting, or the fact that robots may now take the first risk instead of humans? #AI #Robotics #Drones #Firefighting #Innovation #EmergencyResponse #SmartCities #FutureOfWork #Technology

Everyone wants the AI penthouse. Almost nobody wants to pay for the basement. What I keep seeing is the same pattern: companies want AI outcomes without investing in AI foundations. The exciting layer gets funded first: → GenAI pilots → strategy decks → dashboards → executive demos The foundational layer gets ignored: → definitions → data quality → metadata → lineage → ownership And then people act surprised when things start to crack. AI rarely fails because the vision was too ambitious. It fails because the foundation was too weak. That is the expensive mistake. Foundations are not the boring part of AI. They are the part that keeps everything else standing. What do you think kills more AI projects: weak vision, or weak foundations nobody wanted to fund? #AI #GenAI #DataQuality #DigitalTransformation #DataGovernance #BusinessStrategy #Innovation #FutureOfWork #Technology

A few self-driving taxis in San Francisco just demonstrated the real problem with autonomy. They were too rational. For a brief moment, several robotaxis aligned at an intersection and created a perfectly polite deadlock. No aggression. No improvisation. No human-style “you go, I’ll go.” Just algorithms waiting for clarity. And that is exactly why this moment matters. What interests me is that this was not a failure of sensing. The vehicles could see. The problem was social judgment. Because cities are not just physical systems. They are negotiation systems. They run on: → tiny signals → hesitation → assertiveness → eye contact → imperfect timing That is where autonomy gets much harder than people think. We are not only teaching machines how to detect objects and follow lanes. We are asking them to operate inside messy human environments where the right move is not always the most logical one. To me, that is the deeper lesson. The next frontier in self-driving is not just better perception. It is better judgment under uncertainty. And that is a much more difficult problem. What do you think matters more for autonomous vehicles now: seeing the road better, or learning how to navigate human ambiguity? #AI #AutonomousVehicles #SelfDrivingCars #FutureOfMobility #Innovation #Technology #SmartCities #MachineLearning #FutureOfWork

Ants may be better than humans at one thing that matters more than most teams realize: working together without getting in their own way. What I find fascinating is how much their advantage comes from simplicity, not intelligence. Ants solve problems as a group with simple signals, fast alignment, and very little friction. Humans bring more creativity, judgment, and experience. But we also bring ego, variation, and competing priorities. That is why this matters in supply chains. Performance does not break down only because of strategy. It often breaks down because coordination gets messy. → too many handoffs → too many conflicting priorities → too much communication, not enough clarity That is the real lesson here. Sometimes the issue is not capability. It is noise. And the teams that perform best are not always the smartest individually. They are the ones that align faster and move with less friction. What do you think matters more in operations: smarter individuals, or a system that helps people move together more effectively? #SupplyChain #Leadership #Operations #Teamwork #Productivity #Innovation #BusinessStrategy #Collaboration #FutureOfWork

There are many jobs people fear AI will take. Scrubbing toilets is not one I feel compelled to defend. That is what struck me about the Zerith H1. It is a wheeled humanoid robot designed to clean hotel bathrooms and public spaces, handling the kind of work most people are happy to avoid: toilets, showers, sinks, floors, restocking. And honestly, this is one of the clearest examples of where robotics makes immediate sense. Because this is not glamorous work. It is repetitive. It is physically exhausting. It is chemical-heavy. And in many places, it is hard to staff consistently even though it is essential for everyone’s comfort. That is why I think this matters. If robots can take over the most draining, unpleasant, and low-value parts of these jobs, the real opportunity is not just efficiency. It is dignity. It is the chance to move people away from the most punishing tasks and into roles that are safer, more stable, and more human. → less physical strain → less exposure to harsh chemicals → more consistency in essential services → more room for people to do work where judgment and care matter more To me, this is one of the few areas where the AI debate becomes refreshingly clear. Not every job should be handed to a machine. But some tasks probably should. And toilet cleaning is making a very strong case. Is this the kind of job robots should take first? #AI #Robotics #Automation #FutureOfWork #Innovation #Hospitality #Technology #SmartCleaning #HumanoidRobots

😂 After years of completely unscientific fieldwork conducted in my living room, my findings are now clear: Dogs consistently outperform. People deliver mixed results. AI is powerful, but still occasionally invents reality with confidence. That is what makes this image land so well: 👉 AI can impress you, then hallucinate. 👉 People can promise a lot, then disappear. 👉 Dogs just show up, wag their tail, and act like loyalty is a strategy. No prompt engineering, personal branding, strategic ambiguity. Just presence, consistency and results. The trust ranking is becoming awkwardly clear. What is your current trust order: AI, people, or dogs? #AI #ArtificialIntelligence #Humor #Trust #Technology #FutureOfWork #Dogs #TechCulture