Balancing AI and Human Expertise

AI will always find you the fastest path. The dangerous part is assuming fastest and right are the same thing. 🧠 After 13 years and 200+ enterprise AI deployments, this is the distinction I keep coming back to in every system I build and every leadership team I work with. AI optimises for efficiency. It does not have access to the relationship history, the political context, the ethical weight, or the lived experience that determine whether the efficient path is actually the right one for this specific situation, with these specific people, right now. That is not a flaw to be engineered away. It is the permanent and irreplaceable role of human judgment. Here is a framework I use when working with AI outputs on high-stakes decisions. ➡️ What context does this decision require that AI does not have access to? ➡️ What would I decide if I had not seen the AI recommendation first? ➡️ Am I using this output to inform my thinking or replace it? The third question is the most important. Using AI to inform your thinking is amplification. Using it to replace your thinking is atrophy. And the line between the two is easier to cross than most people realise. What is one decision in your work where you would never let AI have the final say? #ai #leadership #futureofwork #artificialintelligence #aistrategy #teamhuman #criticalthinking #intellectualatrophy

If AI makes every decision better, will humans forget how to decide? The obvious concern about AI decision-making is that it might be wrong. I'm beginning to think the more interesting problem appears when it's right. Imagine AI recommends the correct decision 99 times in a row. Each time, you accept it because the evidence is stronger, the analysis is deeper, and the outcome is better than the one you might have reached yourself. By decision 100, something subtle has changed. Not necessarily the AI. You. Judgment isn't something we simply possess. We develop it by making decisions under uncertainty, getting some wrong, living with the consequences, and slowly learning which signals matter when the answer isn't obvious. If AI removes that experience, it may improve today's decisions while weakening tomorrow's decision-maker. This connects to something I explore in The Human-Agent Orchestrator as Mastery Vacuum. When execution migrates to agents, people can lose not only tasks, but the experiences through which expertise was built. And that creates a paradox I think leaders need to take seriously. The better AI becomes at deciding, the more deliberately we may need to design opportunities for humans to practice judgment. That could mean asking people to form an opinion before seeing the AI recommendation, keeping humans close to genuinely ambiguous cases, and rewarding thoughtful disagreement rather than automatic approval. Human-in-the-loop should not become human-clicks-approve. If AI eventually makes better decisions than we do, should we still preserve some decisions for humans because making them is how we learn to judge? #HumanAgentOrchestrator #MasteryVacuum #HumanJudgment #HybridManagement #AIReadiness

There is a lot of talk about AI, data, analytics, and algorithms. And they all matter. But for the next several years, human judgment is what will matter most. AI can process 1.7 billion variables. The human mind can deal with four to six. That gives us better data, better options, and more consistency. All of that helps. But judgment is still not quantifiable. It’s processed by the brain in ways we don’t fully understand. And we never will. Judgment shows up in places AI cannot reach: 🔹 How you define the problem 🔹 Which assumptions you challenge 🔹 Which risks you take 🔹 Which goals you choose 🔹 How much ego damage you can stand Good judgment comes from bad experiences. That’s been true for centuries. When something goes wrong, reflect: 🔹 Were the facts wrong? 🔹 Was the reframing wrong? 🔹 Was the weighting of facts wrong? 🔹 Or was it a judgment shaped by bias or risk preference? Use AI to expand your thinking. But don’t outsource judgment. Strengthen it. That’s what leaders are admired for.

Last week, a senior manager presented me with a strategic roadmap during an advisory session. It was polished, grammatically perfect, and filled with current buzzwords. It looked like a fantastic job but my gut feeling gave me a strange feeling I asked one simple question: "𝘞𝘩𝘺 𝘥𝘪𝘥 𝘺𝘰𝘶 𝘱𝘳𝘪𝘰𝘳𝘪𝘵𝘪𝘻𝘦 𝘤𝘩𝘢𝘯𝘯𝘦𝘭 𝘟 𝘰𝘷𝘦𝘳 𝘤𝘩𝘢𝘯𝘯𝘦𝘭 𝘠 𝘪𝘯 𝘘3?" I was not surprised by the reaction. He froze. He couldn't give a proper answer. Why? Because he hadn't made that decision. The algorithm did. He had fallen for the "𝗢𝗿𝗮𝗰𝗹𝗲 𝗠𝘆𝘁𝗵". He treated the AI as a "know-it-all" guru rather than what it actually is: a high-power probabilistic engine. This passive approach is dangerous. When we view AI as an oracle, we stop analyzing and start obeying. We confuse “𝘨𝘰𝘰𝘥 𝘸𝘳𝘪𝘵𝘪𝘯𝘨” with “𝘨𝘰𝘰𝘥 𝘪𝘥𝘦𝘢𝘴 𝘵𝘩𝘢𝘵 𝘐 𝘳𝘦𝘢𝘭𝘭𝘺 𝘶𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥 𝘢𝘯𝘥 𝘐 𝘤𝘢𝘯 𝘸𝘰𝘳𝘬 𝘸𝘪𝘵𝘩”. Here is the uncomfortable reality: LLMs do not "reason" in the human sense; they predict the next most likely word based on patterns. They are designed to sound convincing, not to be factually accurate. If you want to survive the Algorithm Era, you must shift from a passive user to an active driver. Here is how to break the AI toxic dependency:   • 𝗗𝗲𝗺𝗼𝘁𝗲 𝘁𝗵𝗲 𝗔𝗜: Stop treating ChatGPT as a Vice President of Strategy. Treat it as a brilliant but sometimes intoxicated summer intern. It generates volume but YOU provide the judgment.   • 𝗧𝗵𝗲 "𝗝𝗮𝗴𝗴𝗲𝗱 𝗙𝗿𝗼𝗻𝘁𝗶𝗲𝗿" 𝗥𝘂𝗹𝗲: AI excels at creative brainstorming but often fails at simple logical tasks. Never delegate the final decision on high-stakes logic to a black box.   • 𝗜𝗻𝘁𝗲𝗿𝗿𝗼𝗴𝗮𝘁𝗲, 𝗗𝗼𝗻'𝘁 𝗝𝘂𝘀𝘁 𝗔𝘀𝗸: Don't just ask for an answer. Ask the AI to show its work. Force it to reveal its "Chain of Thought" so you can verify the logic, not just the result.   • 𝗢𝘄𝗻 𝘁𝗵𝗲 𝗪𝗵𝘆: If you cannot explain the rationale behind an AI-generated strategy without looking at your notes, you do not have a strategy. You have a hallucination. Let’s be honest: What is the most plausible lie an AI has told you recently that almost slipped into a final report?. I’ll start: AI confidently claimed a competitor had discontinued a specific product line because it seemed "logical." It hadn't. Let me know in the comments. #AIAugmentedProfessional #HybridIntelligence #AiforExecutives #OracleMyth

"How do we ensure that the rapid development of AI is more considerate of harms and the public interest? In our inaugural Responsible AI Impact Report, All Tech Is Human (ATIH) aims to reveal our most urgent risks, emerging safeguards, and public-interest solutions, and provide a roadmap for how we will shape how AI impacts society in the year ahead. We examine the state of Responsible AI (RAI) throughout 2025 and highlight what we consider to be some of the most impactful contributions made by civil society organizations this year to enrich this broad and dynamic field. We believe the Responsible AI field can only thrive if we effectively tackle the complex challenges at the intersection of technology and society. When we refer to “Responsible AI,” we mean AI that is well-regulated and guard-railed, governed and assured (documented, standardized, and benchmarked with relevant measurements), and assessed, evaluated, and red-teamed. As we outlined in our recent Responsible Tech Guide (2025), our organization believes in a human-centered future that values our agency in desired outcomes and rejects tech determinism. As such, we are focused on elevating AI models that do as little harm as possible, for use cases in which risks have been carefully considered and meaningfully mitigated; and ethically deployed AI, in which lofty principles are operationalized with grounded KPIs. This Responsible AI Impact Report highlights the growing focus on Public Interest AI that is of, by, for, and in service to the people. This Public Interest AI should be applied to humanity’s most pressing challenges and enable us to reimagine what a better tech future entails. This report also explores a future in which Public Interest AI is developed on public infrastructures for an AI-literate society. At the heart of the years ahead lies a defining question: who determines the purpose of AI and the kinds of lives it will shape?" Rebekah Tweed, with support from David Ryan Polgar, Sandra Khalil, and Sherine Kazim. 

The gap I am focused on most these days when it comes to AI at work, is the gap between employees and employers. We know that 75% of knowledge workers are using GAI on the job, saying it’s not just helping them save time to focus on more important work but also to bring more human skills to their work, like creativity. But we also know that only 39% of those workers have been trained on AI at work, as companies struggle still to come up with a point of view on AI as well as a strategy for workforce development in the age of AI. If your company is struggling on that part, one thing you can do is look to those who are leading the way. IBM and Siemens are great examples of companies who are two steps ahead of most, moving beyond the incremental early days of AI towards the real, transformative benefits. I was inspired by my conversation a few weeks ago with Nickle LaMoreaux and Brenda Discher who are not only innovating with AI at scale, but keeping people at the center of it all. Across those conversations and many others I’m having, a few key foundational steps are emerging: 1️⃣ Have a pro-human AI point of view and strategy in place. AI has the potential to build a world of work where people can bring their full skills and abilities to bear — but we need to believe in the power of our people more than the power of our tech to realize it. 2️⃣ See jobs as tasks, not titles. Once you boil down a job down into a set of tasks, it’s much easier to see where AI is coming in to change or disrupt some of those tasks and where there are uniquely human skills people will spend much more time on then before. In a world where 68% of skills are set to change by 2030, understanding where this change will hit is crucial to helping your teams stay resilient. 3️⃣ Build learning into the day to day of your company’s culture. As skills for jobs change rapidly – learning is no longer a one-off moment at the start of a career. The ability to learn, unlearn, and relearn is what sets teams apart to stay agile and resilient. 

Rather than think Human-in-the-Loop - which implies AI-first with human involvement - it is often better to think AI-in-the-loop, with humans in control and AI supporting as useful. A new paper explores the distinctions between these models (link in comments). Some of the insights: 💡 Automation or collaboration? The key difference in the models is how control and accountability are allocated between human and machine. Essentially human-in-the-loop is automation with human involvement, while AI-in-the-loop is human-driven collaboration with AI. 🔄 Patterns of interaction. In human-driven setups, the AI’s role is highly contextual, tailored to the user’s specific needs and expertise. For AI-driven models, the human interaction is often more reactive—addressing errors or fine-tuning outcomes. 🤝 Trust in human-AI collaboration. Trust is earned differently in these systems. For human-driven setups, users need to feel that the AI is transparent and interpretable. For AI-driven systems, trust hinges on the system proving reliable over time and aligning with user expectations. In both cases, trust is critical to generate useful collaborative outcomes. 🛠 Selecting the right system for the task. The choice between human-in-the-loop and AI-in-the-loop systems depends on the complexity of the task. For repetitive or predictable tasks like inventory forecasting or detecting product defects, AI-driven systems can automate effectively. For nuanced and high-impact applications like personalized healthcare decisions or crafting adaptive education strategies, human-driven systems should be chosen. The language and frameworks we use shape the systems we create. The implicit framing that AI leads in the human-in-the-loop phrase is dangerous. In many cases we absolutely want humans first, with AI-in-the-loop where it is useful.

𝗙𝗼𝗿 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝘁𝗶𝗺𝗲 𝗶𝗻 𝗺𝘆 𝗰𝗮𝗿𝗲𝗲𝗿, 𝗜 𝗳𝗼𝘂𝗻𝗱 𝗺𝘆𝘀𝗲𝗹𝗳 𝗳𝗲𝗲𝗹𝗶𝗻𝗴 𝘀𝗼𝗿𝗿𝘆 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗽𝗲𝗿𝘀𝗼𝗻 𝘀𝗶𝘁𝘁𝗶𝗻𝗴 𝗮𝗰𝗿𝗼𝘀𝘀 𝘁𝗵𝗲 𝗻𝗲𝗴𝗼𝘁𝗶𝗮𝘁𝗶𝗼𝗻 𝘁𝗮𝗯𝗹𝗲. Not because he was weak. Not because he was unprepared. Quite the opposite. A while ago, I used AI to prepare for an important negotiation. I asked it to help me think through the conversation. Where to push. When to pause. Which objections would likely come. The output was surprisingly detailed. So I walked into the meeting with something close to a script. And I followed it. The arguments landed. The expected pushback never really came. Everything unfolded almost exactly as predicted. Then I felt something I had never felt before in a negotiation. 𝗔 𝘀𝗵𝗶𝘃𝗲𝗿. Because I suddenly realized the conversation had become one-sided. I wasn’t adapting anymore. I wasn’t improvising. 𝗜 𝘄𝗮𝘀 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗻𝗴 𝗮 𝗽𝗹𝗮𝘆𝗯𝗼𝗼𝗸. And it was working far better than I was comfortable with… Without the machine, I would probably never have pushed that far. I could have continued. I could have won harder. Instead, I stopped. I stepped back. And I offered a compromise I did not have to offer. Because one thing mattered more. 𝗜 𝘄𝗮𝗻𝘁𝗲𝗱 𝘂𝘀 𝘁𝗼 𝗿𝗲𝗺𝗮𝗶𝗻 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗽𝗮𝗿𝘁𝗻𝗲𝗿𝘀 𝗮𝗳𝘁𝗲𝗿 𝘁𝗵𝗶𝘀. For me the decision was whether to maximize the transaction or protect the relationship. And that is where I believe the AI debate becomes serious. 𝗔𝗜 𝗰𝗮𝗻 𝗴𝗶𝘃𝗲 𝘆𝗼𝘂 𝗹𝗲𝘃𝗲𝗿𝗮𝗴𝗲. But it is not accountable for how far you use it. A person is. 𝗜𝗻 𝘁𝗵𝗲 𝗲𝗻𝗱, 𝗷𝘂𝗱𝗴𝗺𝗲𝗻𝘁 𝗰𝗮𝗻𝗻𝗼𝘁 𝗯𝗲 𝗱𝗲𝗹𝗲𝗴𝗮𝘁𝗲𝗱 𝘁𝗼 𝗮 𝗯𝗼𝘁. Because leadership is not only about finding the strongest move. It is deciding whether that move is worth taking. 𝗔𝗜 𝗴𝗮𝘃𝗲 𝗺𝗲 𝗹𝗲𝘃𝗲𝗿𝗮𝗴𝗲. 𝗝𝘂𝗱𝗴𝗺𝗲𝗻𝘁 𝘁𝗼𝗹𝗱 𝗺𝗲 𝗻𝗼𝘁 𝘁𝗼 𝘂𝘀𝗲 𝗮𝗹𝗹 𝗼𝗳 𝗶𝘁. Where do you draw the line between optimization and judgment? #Leadership #Negotiation #AI #DecisionMaking #Judgement 𝘈𝘳𝘵 𝘤𝘳𝘦𝘥𝘪𝘵𝘴 𝘵𝘰 𝘴𝘪𝘣𝘢𝘵𝘢𝘣𝘭𝘦. 𝘍𝘰𝘶𝘯𝘥 𝘢𝘵 𝘢𝘳𝘵_𝘥𝘢𝘪𝘭𝘺𝘥𝘰𝘴𝘦.

As AI rapidly transforms our industry, I've been thinking about which tech roles will survive – and which won't. Testing code used to require specialized skills. Today, AI can write test scripts that rival those created by mid-level engineers. Tomorrow? 𝗕𝗮𝘀𝗶𝗰 𝘁𝗲𝘀𝘁 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗮 𝗰𝗼𝗺𝗺𝗼𝗱𝗶𝘁𝘆 𝘀𝗸𝗶𝗹𝗹. This isn't fear-mongering. It's our new reality. The engineers who thrive won't be those who simply write test code, but those who architect entire testing environments, design integration strategies, and optimize the full delivery pipeline. I recently watched this transformation happen in real-time with Rody, a test automation specialist with 13 years of experience. He recognized the shifting landscape and made a critical decision: to rise above the commodity skills and master DevOps. His journey began with a challenge: implementing test automation for a company without a test environment. Instead of treating this as "not my job," he collaborated with a DevOps engineer to build a Kubernetes-based testing environment from scratch. This experience sparked something profound: the realization that the most valuable engineers aren't just coders – they're architects and problem solvers 💡 Over 18 months (while balancing a new baby, a move, and job changes), Rody transformed his skill set. He now creates Flask applications deployed in Kubernetes clusters, builds Terraform projects integrated with Jenkins, and automates server configuration with Ansible. The AI revolution creates two distinct career paths for engineers: 1. 𝗧𝗵𝗼𝘀𝗲 𝘄𝗵𝗼 𝗰𝗼𝗺𝗽𝗲𝘁𝗲 with AI at tasks it will inevitably master 2. 𝗧𝗵𝗼𝘀𝗲 𝘄𝗵𝗼 𝗹𝗲𝘃𝗲𝗿𝗮𝗴𝗲 AI while focusing on skills AI struggles with: system design, integration strategy, and holistic problem-solving Rody chose the second path. He's no longer at risk of becoming another replaceable test engineer in a sea of mediocrity. This pattern will repeat across our industry. The engineers who survive won't be those writing the most code – they'll be those who 🟢 understand how systems connect 🟢 can architect solutions across multiple domains 🟢 continually adapt to change Read his full story here: https://lnkd.in/dXEUBFmP 💬 What skills are you developing that AI can't easily replicate? 💬 How are you ensuring you stay on the right side of this divide?

New report from the Imagining the Digital Future Center: Being Human in 2035 How is AI reshaping what it means to be human? In this thought-provoking study, nearly 300 tech and foresight experts from around the world share insights on how AI is transforming the ways we think, feel, act, and relate to one another. While some see positive change, many voice concern about AI’s potential to negatively affect our cognitive and emotional lives. The report breaks down expert views across 12 key dimensions — from trust to empathy — offering a rich, forward-looking snapshot of our digital future. Huge kudos to researchers Janna Quitney Anderson and Lee Rainie for this powerful work. Dive into the full report here: https://lnkd.in/gR2GhgSN #AI #DigitalFutures #BeingHuman #Foresight #ITDF Elon University