I’m excited to share that my new book, Artificial Organizations, is coming out in mid-March! I’ve been working on this over the past number of months, driven by a simple question I keep hearing from executives: How do we pair human intuition with machine insight to actually get better outcomes, not just more activity? This book is for leaders navigating that shift in how we work in high-paced environments. Not AI as a tool. Not AI as automation. But AI as a thinking partner that helps you make better decisions, faster without losing judgment, context, or humanity. Sign up at artificialorganizations.com/ to be notified of the official book release!

People can only perform within the constraints of the system they're operating in. Organizations often respond to performance challenges by focusing on talent, accountability, or execution. Yet many of the issues leaders face, including slow decisions, competing priorities, misalignment, and inconsistent results, are symptoms of something deeper. Performance is shaped by the quality of decisions, the flow of information, and an organization's ability to learn and adapt as conditions change. The strongest organizations don't depend on a few exceptional people to keep everything moving. They create environments where knowledge is accessible, decisions are made with context, and teams can execute with clarity. Over time, that advantage compounds. Sustainable performance is rarely accidental. It emerges from systems intentionally designed to support better thinking, better decisions, and better outcomes. 📖 Explore more ideas from Artificial Organizations: geni.us/artificialorgs 💬 Or comment AO BOOK and we'll send you free sample chapters.

Jim Highsmith breaks decision-making into knowledge, experience, and judgment. AI can help with knowledge. It can surface patterns, summarize inputs, and move faster through the data. But Jim’s distinction between fast and slow decisions matters. Some decisions are rule-based and data-heavy. Others require orientation: context, tradeoffs, and judgment about what kind of situation you are actually in. Conflating the two is how leaders end up automating the wrong work. The question is not just, “Can AI do this faster?” It is, “What capability do we still need people to build by doing the work?” Listen on Unlearn podcast: - YouTube: youtu.be/G_Kh-jIml1o?si=2voY… - Website: barryoreilly.com/explore/pod…

AI should take work off our plate. There are plenty of administrative tasks where automation is the right move. The risk Jim Highsmith points out is more subtle: if we let the machine do all the thinking, we may not notice capability fading until a crisis hits. And by then, confidence is hard to rebuild on demand. That is the leadership tradeoff with AI. Use it to remove drag, but keep people practicing the judgment they will need when the system fails, the answer is unclear, or the stakes suddenly change. Full conversation with Jim on Unlearn: - YouTube: youtu.be/G_Kh-jIml1o?si=2voY… - Website: barryoreilly.com/explore/pod…

Yesterday we wrapped up the final session of the Artificial Organizations: Executive Leadership Series. Over the past few months, we've worked through every chapter of the book together, discussing how AI is changing the way we think, make decisions, and lead. A big thank you to everyone who joined the livestreams, asked questions, shared experiences, and contributed to the conversations along the way. If you missed any of the sessions, or would like to revisit them, all 11 livestreams are now available here: 🎥 youtube.com/playlist?list=PL… One thing I've enjoyed most since the book launched has been hearing which ideas resonate most with readers. Artificial Organizations currently has 46 global reviews on Amazon with a 4.9-star average rating. If you've read the book and found it valuable, I'd really appreciate you taking a few minutes to leave a review. It helps other leaders decide whether the book is relevant for them. 📚 Scan the QR image or leave a review here: amazon.com/review/create-rev… And if you've finished the book, I'm curious: What's the one idea, framework, or chapter that has stayed with you the most?

Working with executives across different industries, I've noticed that many leadership challenges eventually trace back to the same issue: decision latency. Most organizations have access to plenty of data, analysis, and expertise. Yet important decisions still spend weeks moving through meetings, approvals, and alignment cycles before action is taken. The cost rarely appears on a balance sheet, but it shows up in missed opportunities, slower execution, and lost momentum. Over time, small delays compound. Decisions take longer. Learning slows. Progress becomes harder to sustain. That's why I've become increasingly interested in how leaders can reduce decision latency without sacrificing judgment. 👉 Want to read Artificial Organizations? Comment AO BOOK, and we will send you the FREE chapters!

Artificial Organizations - Chapter 9: Lead The Future: Your 5-15-30 Roadmap x.com/i/broadcasts/1qKDzzDay…

People often talk about the OODA loop (Observe, Orient, Decide, and Act) as a speed advantage. Jim Highsmith points to the more useful part: orientation. John Boyd’s edge was his ability to change mental models quickly enough to match the situation. That is what let him reverse position on challengers in the air, again and again. That lesson matters for leaders dealing with uncertainty. The delay often happens before the decision, in the moment where we need to admit that our old frame no longer fits what is actually happening. Full conversation with Jim on Unlearn. - YouTube: youtu.be/G_Kh-jIml1o?si=2voY… - Website: barryoreilly.com/explore/pod…

One of the biggest risks of AI isn't that machines get smarter. It's that leaders stop practicing judgment. Over time, automation can quietly remove the very experiences that help us build intuition, pattern recognition, and decision-making capability. In this week's Unlearn episode, I sat down with Jim Highsmith, co-author of the Agile Manifesto, to explore what happens when organizations become process-optimized but judgment-constrained, and why developing better decision-makers may be the most important work leaders have ahead of them. A thoughtful discussion on executive judgment, AI, uncertainty, and the leadership capabilities that still can't be automated. Read the latest Unlearn newsletter 👇 Or listen here: barryoreilly.com/explore/pod…

The more leaders I speak with about AI, the more one question keeps coming to mind: If we were building our companies today, knowing what AI can do, would they look anything like they do now? Most conversations still sound like: "How can AI make us more efficient?" How can it help us write reports faster, automate tasks, reduce costs, or improve productivity? Those are reasonable questions. But they assume the organization itself is already designed correctly. What if it isn't? What if the bigger opportunity isn't improving the current operating model, but questioning whether we'd build it this way at all if AI existed from the start? Many of the management practices we take for granted were designed for a different set of constraints: - Information was scarce. - Expertise was concentrated. - Coordination was expensive. As a result: - Meetings became the way context moved. - Hierarchies became the way decisions scaled. - Processes became the way knowledge was preserved. AI changes those constraints. Access to information is no longer the primary challenge for most organizations. When information is abundant, knowledge is searchable, and analysis happens in seconds, the bottleneck shifts somewhere else. The bottleneck becomes judgment. The leaders creating the most value with AI aren't simply deploying more tools. They're rethinking how decisions get made, how learning happens, where authority sits, and how people and machines work together. AI adoption is quickly becoming table stakes. Organizational redesign is where the advantage will come from. Because once everyone has access to similar models, the differentiator won't be the technology. It will be how effectively people and machines work together inside the organization. The companies that pull ahead won't ask: "Where should we add AI?" They'll ask: "If we were building this company today, knowing what AI can do, what would we design differently?" That's a much harder question, but it's also a far more valuable one.

The knowledge that matters most in many organizations is rarely written down. People learn it over time. Who needs to be involved before a decision gets made. Which tradeoffs are acceptable. What success actually looks like when priorities collide. Most companies don't notice how much they depend on this knowledge until they try to scale it. New leaders spend months learning how decisions actually get made. Teams revisit conversations that have already happened. Context gets rebuilt instead of transferred. For years, that was simply accepted as part of organizational life. Now something is changing. As information becomes easier to access, a different challenge is becoming harder to ignore. I explore that idea in my latest blog, which I co-authored with Melanie Steinbach, 4X CHRO at McDonald’s, Cameo, Miliken, and MasterClass. Read the full blog here: barryoreilly.com/explore/blo…

Everyone is talking about AI, but what fewer people are talking about is what AI is actually exposing: the quality of our decisions. Recently, I joined @ShawnFlynnSV on The Silicon Valley Podcast @PODCAST_SV to discuss what I call #ArtificialOrganizations—organizations that use AI not just to automate work, but to improve how decisions are made. We explored: → Why leadership today is less about gathering data and more about synthesizing it → How to accelerate AI adoption without damaging trust or culture → Which decisions should remain human, and which are better delegated to machines → The 5–15–30 roadmap I use to help leaders navigate AI transformation One question I often ask executives is: "If you removed your entire AI stack tomorrow, what remains?" The answer usually reveals whether AI is truly creating leverage—or simply adding complexity. The future won't belong to organizations that deploy the most AI tools. It will belong to leaders who learn how to think, decide, and adapt differently. 🎙️ Listen on: - Apple Podcasts: podcasts.apple.com/us/podcas… - Spotify: open.spotify.com/episode/5R2… - Youtube: youtu.be/DNA8GP-5YVs #ArtificialOrganizations #AILeadership #SiliconValleyPodcast

Visa is a great example of how too many companies are getting off on the wrong foot with AI. They spent 18 months giving employees access to AI tools. Leaders talked about it. They encouraged people to use them. They made the tools available across the organization. And still, they didn’t see the breakthrough they were looking for. Why? Because tool access doesn’t change human behavior. The turning point came when Visa brought its top 300 leaders together for two days of hands-on AI training. They weren’t just shown tools. They practiced real workflows. They applied AI to real decisions. They explored how it could support the work they already do every day. That distinction matters. Most organizations start with tools. Buy the licenses. Open access. Publish policies. Build training catalogs. Then they wonder why people don’t suddenly change how they work. The better starting point is the leader and their personal traits. - How do you do your best work? - Where does your judgment matter most? - Which routines slow you down? - Which decisions deserve better preparation, synthesis, or follow-through? - What should be amplified, and what should never be automated? That’s the shift I describe in Artificial Organizations as the 3T Model: Traits → Tasks → Tools. Start with how leaders naturally do their best work. Then identify the tasks where their judgment matters most to create value. Only then choose the tools that help them improve the way they think, decide, and lead. That’s why AI adoption has to be leader-led. The first step isn’t giving 30,000 employees AI licenses and hoping transformation appears. It’s helping the top 100 to 300 leaders become confident practitioners first. Not experts. Practitioners. Leaders need a safe space to try this work, compare what’s working, admit what isn’t, and build confidence together. That’s how new behavior spreads. Not through mandates, but through role modeling. This is what we’ve learned building an AI venture studio and coaching Fortune 500 leadership teams through this shift. The organizations moving fastest with AI aren’t waiting for the workforce to figure it out. Their leaders are going first, and learning in public. My latest book, #ArtificialOrganizations, shows the way. Our programs help leaders get there.

Artificial Organizations — Chapter 8: Measure and Manage Your AI Operating System Safely x.com/i/broadcasts/1MKgNNMAL…

Years ago, success inside many organizations depended on learning things that were never formally documented. You figured out who to ask. You learned which meetings mattered. You picked up the unwritten rules through observation and experience. Much of that knowledge lived in people rather than systems. AI is changing the economics of information. Finding answers is becoming easier. Accessing expertise is becoming easier. Yet one challenge remains surprisingly persistent. → Context Understanding how decisions get made. Recognizing what matters in a particular situation. Knowing which signals deserve attention and which do not. That capability increasingly separates organizations that move with clarity from those that create more activity without better outcomes. Melanie Steinbach and I recently explored why Work Charters matter and why context may become one of the most valuable assets an organization can build in the age of AI. Read it now: barryoreilly.com/explore/blo… 💬 A question for leaders: If a talented new executive joined your company tomorrow, how much of what they need to succeed is documented, and how much still depends on finding the right person to explain it? Share your thoughts in the comments!

A CEO asked me this question today: What practical frameworks or strategies do you recommend for leaders who want to build more adaptable and resilient organizations? It peaked my interest, and reminded me I always come back to a few practical principles. The first is unlearning. Leaders have to identify the behaviors, beliefs, and systems that once made them successful but now limit them. Adaptability starts by letting go. The second is evidence-based decision-making. Don’t ask, “Do we like this idea?” Ask, “What would we need to learn to know if this idea is worth pursuing?” That shift changes everything. The third is small bets before big bets. Create portfolios of experiments. Test assumptions early. Make learning cheap. Scale only when the evidence is strong enough. The fourth is empowered teams with clear intent. Adaptable organizations don’t centralize every decision at the top. Leaders set direction, constraints, and outcomes, then give teams the space to discover the best path. The fifth is human and machine intelligence by design. Don’t randomly sprinkle AI tools across the organization. Map where human judgment matters most, where AI can augment capability, and where new workflows, roles, and guardrails are needed. Resilience is not about predicting the future perfectly. It’s about building the capacity to sense, learn, and respond faster when reality changes. All these points and more are captured in my new book, Artificial Organizations: Build Better Judgment, Speed, and Results with Human and Machine Intelligence

“A great articulation of how leaders need to move from meetings overload to intelligent decision-making.” That's how @gobrienau described #ArtificialOrganizations while reading it from the beaches of Bali. Many leaders spend their days moving from meeting to meeting, processing information, and reacting to requests. The challenge isn't a lack of activity. It's creating enough space for better decisions. One of the opportunities AI creates is helping leaders spend less time managing information and more time thinking, deciding, and applying judgment where it matters most. 💬 Comment AO BOOK, and we'll send you free sample chapters from Artificial Organizations. 📚 Or check Artificial Organizations on Amazon: geni.us/artificialorgs

One challenge came up repeatedly in conversations with CFOs at @Gartner_inc Finance Symposium: How do you invest in AI today while still being confident it will create value tomorrow? During the event, I had the chance to chat with Ilana Estrich, CFO of @PPFA Planned Parenthood Federation of America. What stood out was her balanced perspective. She uses AI every day for research, communication, and making complex topics easier to explain. At the same time, she's focused on ensuring her organization adopts AI for the right reasons, at the right time, and in the right places. That mirrors a challenge many finance leaders are facing right now. AI experimentation is accelerating, but connecting spend to measurable outcomes remains difficult. Leaders are being asked to make investment decisions before many organizations have clear frameworks for evaluating what success should look like. This is one of the core ideas and frameworks in #ArtificialOrganizations: how leaders can move beyond AI activity and build systems that improve decision-making, performance, and outcomes over time. Ilana was one of the finance leaders who picked up a copy from the 400 books we gave out at the event following my keynote. Where does your organization sit today: experimenting, scaling, or still figuring out how to measure AI is creating value?

Decision quality is heavily influenced by the internal state of the person making the decision. Two leaders can have access to the same information and arrive at very different outcomes. One operates from urgency and pressure. The other operates from clarity and calm. The difference may seem small in a single moment. Last week on Unlearn, Chris Walker shared an idea: your internal state influences how you interpret risk, communicate with others, and respond when uncertainty increases. It's one of the reasons I explored decision-making so deeply in #ArtificialOrganizations. As AI gives leaders access to more information and faster execution, the quality of decisions increasingly depends on the person making them. The best leaders I’ve worked with aren't defined by how they perform when conditions are easy. They're defined by their ability to create clarity when the pressure is highest. What helps you maintain clarity when making decisions under pressure? 🎧 Listen now on the #UnlearnPodcast: - YouTube: youtu.be/3yJE-Pq2NFc?si=Xpt7… - Spotify: barryoreilly.com/explore/pod…

One of my guiding principles and favorite activities is spending time with Interesting People, Doing Hard Things (I wrote a blog about it a few years ago below) Last week I was in Washington, D.C. to keynote at the Gartner CFO Conference. Whenever I’m in town, I try to catch up with people who are doing outstanding work in tough environments. Justin Fanelli (LinkedIn: linkedin.com/in/justinfanell…) is always one of the first people who comes to mind when I’m in Washington He’s one of those rare people who combines technology, innovation, storytelling, experimentation, and service in a way that feels deeply practical and deeply human. As CTO of the @USNavy, he operates in one of the highest-stakes environments on the planet. The kind where technology is not a toy. It has to work. It has to matter. It has to create better outcomes. What I appreciate most about Justin is how hard he works to bring new ways of working into environments where change is difficult, stakes are high, and the cost of getting it wrong is real. Breakfast with people like this is always energizing. Not because they make the work sound easy. Because they remind you that meaningful work rarely is. If you’re interested in what it takes to do something genuinely different, apply technology for positive outcomes, and keep experimenting in high-stakes environments, Justin is someone worth following. Hard things are hard for a reason, but the right people make you want to keep doing them. 🔗 Blog ‘Interesting People, Doing Hard Things’: barryoreilly.com/explore/blo…