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

𝗔 𝘁𝗲𝗲𝗻𝗮𝗴𝗲𝗿 𝗱𝗶𝗱 𝗻𝗼𝘁 𝗮𝗰𝗰𝗲𝗽𝘁 𝘁𝗵𝗲 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝘆’𝘀 𝗰𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀. 𝗛𝗲 𝗿𝗲𝗱𝗲𝘀𝗶𝗴𝗻𝗲𝗱 𝘁𝗵𝗲𝗺. During the pandemic, 17-year-old Benjamin Choi built a mind-controlled prosthetic-arm prototype with an estimated manufacturing cost below $300. He used forehead and earlobe sensors to collect electrical signals, then developed an AI system that translated those signals and head gestures into movement. The algorithm eventually contained more than 23,000 lines of code, while the physical arm went through over 75 design iterations. He developed much of it on a ping-pong table in his basement. What stayed with me was the question behind the invention. Researchers had already shown that thought-controlled prosthetics were possible, but the most advanced systems could require invasive surgery and cost hundreds of thousands of dollars. Choi asked whether the same human need could be served through an entirely different design. I have spent much of my career helping organizations use AI, and I keep returning to the same conclusion: the most meaningful innovation rarely begins with the technology. It begins with refusing to accept that an important human problem must remain expensive, invasive, or inaccessible. That is Genuine Creativity, one of the Humics. It means refusing to treat inherited constraints as permanent truths. Innovation should not be measured only by what technology can do. It should also be measured by who can access what it does. Should accessibility become one of the first benchmarks we use to judge meaningful innovation? #TheHumics #GenuineCreativity #AccessibleTechnology #HealthcareInnovation

𝗧𝗵𝗲 𝗥𝗼𝗮𝗱𝘀𝘁𝗲𝗿 𝗰𝗮𝗻 𝗳𝗹𝘆, 𝗯𝘂𝘁 𝗹𝗮𝗻𝗱𝗶𝗻𝗴 𝗮𝗽𝗽𝗮𝗿𝗲𝗻𝘁𝗹𝘆 𝗿𝗲𝗺𝗮𝗶𝗻𝘀 𝗶𝗻 𝗯𝗲𝘁𝗮. Somewhere, roadside assistance is replacing its tow truck with a crane, while an insurance agent is trying to add “collision with municipal infrastructure at 14 feet” to the claim form. The owner is probably explaining that the vehicle was neither driving nor flying when the accident happened. It was “temporarily exploring a third mobility category.” Meanwhile, the repair estimate has arrived with three words: “Please contact SpaceX.” How would you explain this one to the insurance company? #TeslaRoadster #TechHumor #FutureOfMobility

𝗧𝗲𝘀𝗹𝗮 𝗳𝗶𝗻𝗮𝗹𝗹𝘆 𝘀𝗼𝗹𝘃𝗲𝗱 𝘁𝗿𝗮𝗳𝗳𝗶𝗰. 𝗛𝗥 𝗿𝗲𝗺𝗮𝗶𝗻𝘀 𝘂𝗻𝗺𝗼𝘃𝗲𝗱. You activate the rocket thrusters, fly over 400 cars, enter through the fifth-floor window, and park beside the printer at exactly 8:59. Then you discover the meeting was cancelled seven minutes ago in a Slack channel nobody reads. The future is incredible, but the calendar invitation still says “optional” while your manager quietly takes attendance. Which office problem should SpaceX solve next? #WorkplaceHumor #TeslaRoadster #OfficeLife

𝗪𝗶𝗹𝗹 𝘁𝗵𝗲 𝘄𝗼𝗿𝗸𝗽𝗹𝗮𝗰𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝘃𝗮𝗹𝘂𝗲 𝗮𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗼𝘃𝗲𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲? What if 20 years of experience becomes less valuable than the ability to unlearn what those 20 years taught you? For decades, experience has been one of our strongest indicators of professional value. The longer someone worked in an industry, the more knowledge they accumulated and the more reliable their judgment often became. I still believe experience matters enormously, but AI challenges an assumption we've rarely questioned: will the knowledge that made someone successful yesterday continue to make them successful tomorrow? As agents take over more execution, professionals may discover that the processes they've spent years mastering are no longer the ones their organizations need. Experience helps us recognize patterns and avoid mistakes, but it can also make us attached to familiar ways of working. In The Human-Agent Orchestrator, we describe the Mastery Vacuum, which emerges when agents take over activities through which people developed expertise and professional identity. The challenge isn't simply losing tasks. It's becoming highly experienced in work you no longer need to perform, without knowing what to master next. That's why I think future professional mastery will increasingly involve systems thinking, judgment in ambiguity, stakeholder translation, and agent orchestration. 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝘁𝗲𝗹𝗹𝘀 𝘂𝘀 𝘄𝗵𝗮𝘁 𝗵𝗮𝘀 𝘄𝗼𝗿𝗸𝗲𝗱. 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗵𝗲𝗹𝗽𝘀 𝘂𝘀 𝗿𝗲𝗰𝗼𝗴𝗻𝗶𝘇𝗲 𝘄𝗵𝗲𝗻 𝗶𝘁 𝗻𝗼 𝗹𝗼𝗻𝗴𝗲𝗿 𝘀𝗵𝗼𝘂𝗹𝗱. The strongest professionals may be those who combine both, drawing on their experience without allowing it to limit how they approach unfamiliar challenges. 𝗜𝗻 𝗮𝗻 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝘄𝗼𝗿𝗸𝗽𝗹𝗮𝗰𝗲, 𝘄𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝗿𝗮𝘁𝗵𝗲𝗿 𝗵𝗶𝗿𝗲 𝘀𝗼𝗺𝗲𝗼𝗻𝗲 𝘄𝗶𝘁𝗵 𝟮𝟬 𝘆𝗲𝗮𝗿𝘀 𝗼𝗳 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗼𝗿 𝘀𝗼𝗺𝗲𝗼𝗻𝗲 𝘄𝗶𝘁𝗵 𝗲𝘅𝗰𝗲𝗽𝘁𝗶𝗼𝗻𝗮𝗹 𝗮𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆? #HumanAgentOrchestrator #FutureOfWork #CareerDevelopment #Adaptability #HumanAI

𝗪𝗶𝗹𝗹 𝘁𝗵𝗲 𝘄𝗼𝗿𝗸𝗽𝗹𝗮𝗰𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝘃𝗮𝗹𝘂𝗲 𝗮𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗼𝘃𝗲𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲? What if 20 years of experience becomes less valuable than the ability to unlearn what those 20 years taught you? For decades, experience has been one of our strongest indicators of professional value. The longer someone worked in an industry, the more knowledge they accumulated and the more reliable their judgment often became. I still believe experience matters enormously, but AI challenges an assumption we've rarely questioned: will the knowledge that made someone successful yesterday continue to make them successful tomorrow? As agents take over more execution, professionals may discover that the processes they've spent years mastering are no longer the ones their organizations need. Experience helps us recognize patterns and avoid mistakes, but it can also make us attached to familiar ways of working. In The Human-Agent Orchestrator, we describe the Mastery Vacuum, which emerges when agents take over activities through which people developed expertise and professional identity. The challenge isn't simply losing tasks. It's becoming highly experienced in work you no longer need to perform, without knowing what to master next. That's why I think future professional mastery will increasingly involve systems thinking, judgment in ambiguity, stakeholder translation, and agent orchestration. 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝘁𝗲𝗹𝗹𝘀 𝘂𝘀 𝘄𝗵𝗮𝘁 𝗵𝗮𝘀 𝘄𝗼𝗿𝗸𝗲𝗱. 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗵𝗲𝗹𝗽𝘀 𝘂𝘀 𝗿𝗲𝗰𝗼𝗴𝗻𝗶𝘇𝗲 𝘄𝗵𝗲𝗻 𝗶𝘁 𝗻𝗼 𝗹𝗼𝗻𝗴𝗲𝗿 𝘀𝗵𝗼𝘂𝗹𝗱. The strongest professionals may be those who combine both, drawing on their experience without allowing it to limit how they approach unfamiliar challenges. 𝗜𝗻 𝗮𝗻 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝘄𝗼𝗿𝗸𝗽𝗹𝗮𝗰𝗲, 𝘄𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝗿𝗮𝘁𝗵𝗲𝗿 𝗵𝗶𝗿𝗲 𝘀𝗼𝗺𝗲𝗼𝗻𝗲 𝘄𝗶𝘁𝗵 𝟮𝟬 𝘆𝗲𝗮𝗿𝘀 𝗼𝗳 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗼𝗿 𝘀𝗼𝗺𝗲𝗼𝗻𝗲 𝘄𝗶𝘁𝗵 𝗲𝘅𝗰𝗲𝗽𝘁𝗶𝗼𝗻𝗮𝗹 𝗮𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆? #HumanAgentOrchestrator #FutureOfWork #CareerDevelopment #Adaptability #HumanAI

𝗔 𝘀𝘁𝗶𝗹𝗹 𝗫-𝗿𝗮𝘆 𝘀𝗵𝗼𝘄𝘀 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲. 𝗠𝗼𝘃𝗲𝗺𝗲𝗻𝘁 𝗿𝗲𝘃𝗲𝗮𝗹𝘀 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻. We have spent decades trying to understand moving bodies through frozen images, which is a little like judging an entire film from one photograph. Dynamic Digital Radiography changes that. It captures a rapid sequence of X-rays and turns them into a short moving study, allowing clinicians to observe joints, bones, lungs, and other structures as they actually function. For an orthopedist, the difference can be significant. A conventional image may show how a joint looks while still, but movement can reveal how stability changes under weight, where structures interact abnormally, and when discomfort appears. The real innovation is not simply turning an X-ray into a movie. Fluoroscopy has provided motion imaging for years. The opportunity is making dynamic information available through a faster, more familiar radiography workflow. Better diagnostics do not always require a sharper snapshot. Sometimes they require seeing what happens between the snapshots. Where else are we making important decisions from a static moment when the real answer lives in motion? #MedicalImaging #DigitalHealth #HumanCenteredTechnology #FutureOfMedicine

𝗦𝗵𝗼𝘂𝗹𝗱 𝗿𝗲𝗴𝘂𝗹𝗮𝘁𝗼𝗿𝘀 𝗰𝗲𝗿𝘁𝗶𝗳𝘆 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝗹𝗶𝗸𝗲 𝗽𝗶𝗹𝗼𝘁𝘀 𝗼𝗿 𝗱𝗼𝗰𝘁𝗼𝗿𝘀? A pilot doesn't receive a license simply for knowing how an aircraft works. Competence must be demonstrated under defined conditions, and maintaining it matters just as much as earning it. Shouldn't we expect something similar from AI agents making consequential decisions? I support the principle, but I think certifying an agent could be more complicated than certifying a professional. An agent's behavior can change when its model, tools, permissions, data sources, or operating environment changes. It may perform reliably today but become unsuitable for the same responsibility tomorrow. This is where the Autonomy Matrix in The Human-Agent Orchestrator offers a useful distinction. 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗹𝗼𝗻𝗲 𝘀𝗵𝗼𝘂𝗹𝗱𝗻'𝘁 𝗱𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗲 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝘆. 𝗧𝗵𝗲 𝗰𝗼𝗻𝘀𝗲𝗾𝘂𝗲𝗻𝗰𝗲𝘀 𝗼𝗳 𝗲𝗿𝗿𝗼𝗿 𝗺𝗮𝘁𝘁𝗲𝗿 𝗷𝘂𝘀𝘁 𝗮𝘀 𝗺𝘂𝗰𝗵. An agent summarizing internal documents may need relatively little supervision, while the same technology making decisions about patient care or financial transactions requires much stricter controls. That suggests certification should assess the agent's specific role, operating conditions, and permitted autonomy, rather than simply declaring the technology safe. And perhaps certification should be continuously reassessed as those conditions change. In my opinion, effective regulation should protect people from consequential AI failures without creating unnecessary barriers to low-risk innovation. 𝗦𝗵𝗼𝘂𝗹𝗱 𝗿𝗲𝗴𝘂𝗹𝗮𝘁𝗼𝗿𝘀 𝗹𝗶𝗰𝗲𝗻𝘀𝗲 𝗶𝗻𝗱𝗶𝘃𝗶𝗱𝘂𝗮𝗹 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀, 𝗼𝗿 𝗰𝗲𝗿𝘁𝗶𝗳𝘆 𝘁𝗵𝗲 𝗵𝘂𝗺𝗮𝗻-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗶𝗻 𝘄𝗵𝗶𝗰𝗵 𝘁𝗵𝗲𝘆 𝗼𝗽𝗲𝗿𝗮𝘁𝗲? #HumanAgentOrchestrator #AIRegulation #AgenticAI #ResponsibleAI #AIGovernance

𝗗𝗮𝗱-𝗯𝗲𝗹𝗹𝘆 𝗱𝗼𝗰𝗸𝗶𝗻𝗴 𝘀𝘁𝗮𝘁𝗶𝗼𝗻. 𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗮𝘁 𝗜 𝗰𝗮𝗹𝗹 𝘁𝗿𝘂𝗹𝘆 𝗵𝘂𝗺𝗮𝗻-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 𝗱𝗲𝘀𝗶𝗴𝗻. The only missing feature is wireless charging, although judging by the posture, power-saving mode has already been activated. Which everyday object deserves this level of user research? #DesignHumor #HumanCentricDesign #WorkplaceHumor

𝗧𝗵𝗲 𝗼𝗰𝘁𝗼𝗽𝘂𝘀 𝘀𝗼𝗹𝘃𝗲𝗱 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝗰𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗯𝗲𝗳𝗼𝗿𝗲 𝗰𝗼𝗺𝗽𝘂𝘁𝗲𝗿𝘀 𝗲𝘅𝗶𝘀𝘁𝗲𝗱. What fascinates me most about the octopus is not its unusual anatomy. It is where its intelligence lives. Much of the sensory processing and motor control happens throughout the nervous systems of its arms, while the central brain provides broader direction. The result is neither complete central control nor eight independent creatures fighting for attention. It is coordinated autonomy. That is exactly the challenge we now face with Multi-Agent Systems. If every AI agent must send every small decision back to one central model, the system becomes slow and fragile. But if every agent acts independently without shared goals, memory, and escalation rules, autonomy becomes chaos. The octopus offers a useful design principle: process information close to where it appears, but coordinate action around a shared purpose. The smartest system is not the one that controls every action. It is the one that knows where each decision belongs. Before adding another AI agent, decide what it can handle locally, what must be shared, and when control returns to a human. Which decisions in your organization are still unnecessarily travelling back to the centre? #MultiAgentSystems #OrchestrationDesign #AgenticAI #AIReadiness

𝗬𝗼𝘂𝗿 𝗲𝘅𝗽𝗲𝗻𝘀𝗶𝘃𝗲 𝗴𝗶𝗺𝗯𝗮𝗹 𝗵𝗮𝘀 𝗯𝗲𝗲𝗻 𝗼𝘂𝘁𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗲𝗱 𝗯𝘆 𝗮 𝗰𝗵𝗶𝗰𝗸𝗲𝗻. Somewhere, an engineer has spent three years refining gyroscopes, motion sensors, and stabilization algorithms. Meanwhile, this chicken arrived, accepted payment in corn, and delivered perfect tracking on the first take. The hardware is compact, battery life is excellent, and customer support becomes aggressive only around lunchtime. The difficult part will be explaining to finance why the new camera equipment keeps laying eggs. Would you trust this chicken with your next corporate video? #TechHumor #EngineeringHumor #CameraTechnology

𝗔𝗜 𝗵𝗮𝘀 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝗱 𝗰𝗼𝗿𝗽𝗼𝗿𝗮𝘁𝗲 𝗼𝗻𝗯𝗼𝗮𝗿𝗱𝗶𝗻𝗴. It can confidently summarize a document it barely understood, invent three action items, and repeat “strategic alignment” until everyone assumes somebody else knows what is happening. Honestly, artificial intelligence did not disrupt office culture. It fitted in immediately. The only remaining test is whether it can say, “Great point, let’s take this offline,” without understanding the point or having any intention of taking it anywhere. Which corporate phrase would finally prove that AI has achieved human-level intelligence? #AIHumor #WorkplaceCulture #HybridManagement

You can make every step of a process faster and still make the customer's experience worse. It's the automation mistake I've seen more than any other. Companies automate tasks. Customers experience journeys. Picture it from the customer's side.... The chatbot answers in 2 seconds. The refund still takes three emails, two transfers, and a week. Every step got faster, but the journey got worse!! 3 things that should be noticed here: - Map the journey before you automate the step. - A faster step can still make the whole journey slower, colder, or more confusing. - Measure what the customer feels, not just what the process costs. Efficiency that customers hate is not efficiency. It's a delayed cost. Decide where humans belong before you decide where machines go. The exceptions, the emotional moments, the high-stakes decisions. Let automation handle the volume. Let people handle the moments. Not fewer humans. Better-placed humans. Where has automation made your experience as a customer worse?

Eventually it does result in huge things! Btw, how long can we sustain with the amount of GPU's required?

𝗪𝗵𝗮𝘁 𝗸𝗶𝗻𝗱 𝗼𝗳 𝗽𝗲𝗼𝗽𝗹𝗲 𝗻𝗮𝗺𝗲 𝘁𝗵𝗲𝗶𝗿 𝗸𝗶𝗱 𝗮𝗳𝘁𝗲𝗿 𝗮𝗻 𝗔𝗜? Strange world we are living in. Imagine spending years raising Claude, only for him to grow up and receive 400 prompts a day from people in his pickleball league. Alexa probably stopped answering years ago, while Siri changed her number and moved somewhere without Wi-Fi. Which perfectly normal name will AI ruin next? #AIHumor #WorkplaceHumor #DigitalCulture

𝗬𝗼𝘂 𝗺𝗮𝘆 𝗵𝗮𝘃𝗲 𝗺𝗼𝗿𝗲 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲𝘀 𝘁𝗵𝗮𝗻 𝘆𝗼𝘂 𝗿𝗲𝗮𝗹𝗶𝘀𝗲, 𝗯𝘂𝘁 𝗮𝗰𝗰𝗲𝘀𝘀 𝗮𝗹𝗼𝗻𝗲 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝗻𝗼𝘁𝗵𝗶𝗻𝗴. If the world contained only 100 people, approximately 26 would remain offline, 25 would lack safely managed drinking water, around 9 youth and adults would lack basic literacy skills, and 10 would live in extreme poverty. If you are reading this on LinkedIn, you probably have access to technology, knowledge, and professional networks that billions of people do not. That does not mean you cannot feel stuck, but it does mean you may already possess some of the resources required to move. The real advantage is not access. It is what you turn access into. Here are seven ways to begin: 1. Choose one skill and practise it until it becomes useful. 2. Build a routine that protects time for learning and reflection. 3. Create relationships by contributing before requesting. 4. Improve your financial understanding, because freedom requires visibility. 5. Protect your physical energy instead of treating exhaustion as ambition. 6. Create something each week instead of endlessly consuming other people’s ideas. 7. Turn gratitude into contribution by helping someone with less access than you. This is what I call the Joy and Growth Principle. Technology can give us extraordinary access, but fulfilment comes from using that access to develop ourselves and contribute to others. In the AI era, information is becoming abundant. The discipline to transform it into capability remains scarce. Which advantage do you already possess but still need to convert into action? #JoyAndGrowth #HumanReadiness #AIReadiness #PersonalDevelopment

𝗔𝗜 𝗰𝗮𝗻 𝗺𝗮𝗸𝗲 𝘆𝗼𝘂 𝘀𝗼𝘂𝗻𝗱 𝘀𝗺𝗮𝗿𝘁𝗲𝗿 𝘁𝗵𝗮𝗻 𝘆𝗼𝘂 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱. It can transform a half-formed thought into a polished strategy, fill it with convincing language, and remove every visible sign that the thinking underneath is incomplete. That makes teaching more valuable, not less, because the moment you explain an idea to someone unfamiliar with it, polished language stops protecting you. Their questions reveal the assumptions you skipped, the logic you borrowed, and the parts you never fully understood. This is where Critical Thinking, one of the Humics, becomes essential. AI can improve how an explanation sounds, but it cannot give you an understanding you never developed. Fluency can be generated. Understanding must be earned. Before sharing your next AI-assisted proposal, close the tool and explain the idea in plain language. If the explanation collapses without the original wording, the thinking probably needs another round. What is one idea you only truly understood after trying to teach it? #TheHumics #CriticalThinking #AIReadiness #LeadershipInAIEra

𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗯𝘂𝗶𝗹𝗱 𝘁𝗿𝘂𝘀𝘁 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗵𝘂𝗺𝗮𝗻𝘀 𝗮𝗻𝗱 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀? What if the biggest obstacle to trusting AI agents isn't that they make mistakes, but that we expect humans to supervise them in ways that are practically impossible? I've been thinking about this because organizations often respond to AI mistakes with what seems like the most responsible solution: adding more human oversight. When an agent produces an incorrect result, a manager reviews more of its work. As more imperfections appear, confidence declines, and the organization introduces additional checks to make sure nothing slips through. The intention is sensible, but the outcome can be surprisingly counterproductive. In The Human-Agent Orchestrator, we describe this pattern as the Supervision Trap. The more closely managers attempt to supervise every action, the more they become overwhelmed by the volume of work agents can produce. Eventually, the organization may have a system capable of operating at machine speed while depending on human approval for nearly everything it does. And that's where I think the trust conversation needs to change. We tend to approach AI trust as though it works the same way as trust between colleagues. When working with another person, we gradually develop confidence through experience, observing their judgment and giving them more responsibility as they demonstrate reliability. With agents, however, trust needs to be designed into the system before we delegate consequential work. That means defining what the agent is allowed to do, testing its performance against realistic situations, establishing clear escalation paths, and adjusting its autonomy as evidence accumulates. This is what we explore through the Trust Architecture and Autonomy Matrix. Trust should not mean believing an agent will never make a mistake. It should mean knowing what happens when it does. A trustworthy system is one in which failures can be detected, consequences can be contained, and responsibility remains clear even when humans are not reviewing every individual action. There's also a psychological dimension that organizations shouldn't underestimate. Employees are unlikely to trust agents simply because leadership tells them the technology is reliable. They need to understand its boundaries, know when they can intervene, and feel confident that questioning an automated decision is part of responsible work rather than resistance to change. In my opinion, the goal isn't maximum trust or maximum supervision. It's calibrated trust. Because if every action requires human approval, we haven't really created an autonomous system. We've simply created a faster way to generate work for humans to review. Should organizations require humans to approve more AI decisions to build trust, or does genuine trust begin when we learn which decisions no longer need human approval? #HumanAgentOrchestrator #AITrust #HybridTeams

𝗜 𝗹𝗮𝘂𝗴𝗵𝗲𝗱 𝗮𝘁 𝘁𝗵𝗶𝘀, 𝗯𝘂𝘁 𝘁𝗵𝗲𝗻 𝗜 𝘀𝘁𝗮𝗿𝘁𝗲𝗱 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗮𝗯𝗼𝘂𝘁 𝘀𝘂𝗿𝗴𝗲𝗿𝘆. At a restaurant, human unpredictability might mean somebody forgets the extra sauce. In an operating room, it makes me want to know whether the surgeon slept well, had breakfast, and received any messages beginning with “We need to talk” that morning. A surgical robot arrives with steady hands, perfect concentration, and no ongoing dispute about who gets the house. It never becomes distracted by a custody meeting or spends the first ten minutes complaining about its manager. Of course, the human surgeon can recognize an unexpected complication and improvise, while the robot may freeze at 37%, request a software update, and ask whether I accept cookies while I am in no position to read the privacy policy. Perhaps the future is the Human+AI Equation: robotic precision, human judgment, and a firm rule preventing either participant from having a personal crisis or installing Windows updates during the procedure. Although, before replacing every unpredictable employee with a machine, we could also try the radical innovation suggested by this sign: stop abusing people and expecting flawless performance under impossible pressure. Would you choose the stressed human surgeon or the perfectly calm robot displaying “Connection lost”? #AIHumor #HumanAI #HealthcareInnovation #FutureOfWork

𝗪𝗶𝗹𝗹 𝗔𝗜 𝗲𝘃𝗲𝗿 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗯𝗲𝘁𝘁𝗲𝗿 𝘁𝗵𝗮𝗻 𝗮 𝗵𝘂𝗺𝗮𝗻? For a long time, I assumed context would remain one of the safest human advantages. AI could process more information than us, but humans understood the room. We knew the history behind a decision, the relationship between two colleagues, why a customer said yes when they really meant maybe, and why the technically correct answer could still be the wrong thing to do. I'm becoming less comfortable with that assumption. As agents gain memory and access to more organizational information, they may eventually know more of the documented context than any individual human. An agent could remember previous decisions, customer interactions, project outcomes, policy changes, and exceptions while connecting patterns across years of work that no manager could realistically keep in their head. But I think there's an important distinction hiding inside the word "context." 𝗧𝗵𝗲𝗿𝗲 𝗶𝘀 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗰𝗮𝗽𝘁𝘂𝗿𝗲, 𝗮𝗻𝗱 𝘁𝗵𝗲𝗿𝗲 𝗶𝘀 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝘆𝗼𝘂 𝗵𝗮𝘃𝗲 𝘁𝗼 𝗶𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁. An agent may know that a customer rejected three previous proposals, but a human may understand that the fourth conversation feels different because trust has finally changed. It may know every performance metric for an employee, but not fully understand why pushing that person harder today could be exactly the wrong leadership decision. This is why I think the human advantage may move away from possessing context and toward deciding which context matters. We shouldn't assume humans will always know more, because increasingly they won't. Instead, we need to become better at interpreting ambiguity, challenging what the system believes is relevant, and recognizing when circumstances have changed enough that historical patterns no longer deserve the same weight. Perhaps AI will eventually have more context than we do. But is having more context the same as understanding it? What do you think? Could AI eventually understand context better than humans, or will interpretation remain fundamentally different from information? #HumanJudgment

𝗬𝗼𝘂 𝗰𝗮𝗻𝗻𝗼𝘁 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗳𝗼𝗿 𝗮𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝘁𝗵𝗲𝗻 𝗯𝗹𝗮𝗺𝗲 𝗽𝗲𝗼𝗽𝗹𝗲 𝗳𝗼𝗿 𝗽𝗮𝘆𝗶𝗻𝗴 𝗮𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻. Gen Z did not invent infinite scrolling, personalized recommendation engines, streaks, notifications, or business models that turn every additional minute of attention into revenue. Yet we often discuss their behaviour as though millions of young people independently decided to become distracted at exactly the same moment. Individual responsibility still matters, and people can build healthier habits, spend more time offline, and make deliberate choices about how they use technology. But personal discipline is only one part of the equation when the environment has been designed to make disengagement difficult. A park is valuable, but it is not competing on equal terms with a personalized feed that studies every interaction and continuously learns what will keep someone watching. This matters even more as AI makes those systems more persuasive, adaptive, and intimate. In Better AI, the objective cannot simply be maximizing engagement because the metric is profitable. We also need to ask whether the product supports human agency, connection, and growth. Technology companies should not be blamed for every social problem, but they should be accountable for the behaviours their incentives deliberately encourage. When a platform profits from maximizing attention, how much responsibility should it carry for the behaviour it helps create? #BetterAI #AlgorithmicDesign #DigitalWellbeing #ResponsibleTechnology