David Monnerat

Product + AI | Systems Thinker | Enterprise Reality

Tag: product

  • The Lesson We Keep Not Learning

    The Lesson We Keep Not Learning

    Organizational learning is supposed to be how we get better over time. Here’s why we keep skipping it — and what AI reveals about that failure.

    I’ve been the person who knew better.

    Not once. More than once. You have data. You have a model. You have something that genuinely works, that you’ve tested, that you believe in. And you walk into a room with the business and you expect the quality of the thing to do the persuasion work. Because it’s good. Because you can see what it will do for them. Because if they just understood it the way you understand it, they’d want it too.

    It almost never works that way.

    The business isn’t wrong to resist. They have a roadmap. They have commitments. They have things they’re already behind on. You’re asking them to absorb something new, change how they work, take on risk, and trust that the upside you’re describing will actually materialize. The idea being good doesn’t answer any of those questions. It just asks them to take your word for it.

    I’ve watched this pattern play out more times than I can count — in myself, in colleagues, across organizations. Someone builds something they believe in, takes it to the business, gets a lukewarm response or an outright no, adjusts the what, and tries again. New idea, same method. The next version is better. The pitch is cleaner. The deck has more data. And it fails for the same reason the first one did, because the method didn’t change, only the artifact.

    The question that would have helped — and almost never gets asked — is this: based on what has and hasn’t worked before, what does successful adoption actually look like here, and what’s the path to get there?

    That’s not a question about the idea. It’s a question about the method. And it requires looking back at your own history before you move forward.

    We don’t do that. Not consistently. Not structurally. We survive the last failure, close the chapter, and start the next project with conviction instead of context.

    The Pageantry of the Post-Mortem

    When something goes badly enough that it demands acknowledgment, we have rituals for it. The retrospective. The lessons learned document. The post-mortem that produces action items nobody looks at again.

    I’ve sat in those rooms. There’s a specific energy to a post-mortem that’s being performed rather than practiced. Everyone agrees on what went wrong. The right language gets used. The document gets written. And then the next project starts and nobody opens it. Nobody asks: have we seen this before? Nobody maps the current situation against the last one.

    The lesson learned becomes a historical artifact rather than an operational input.

    I’ve watched senior leaders run the same failing initiative multiple times — different team, different framing, different artifact — because the diagnosis from the last attempt never got applied to the next one. The what kept changing. The how never did. The loop ran for years.

    Nobody was malicious. Nobody was incompetent. The lesson was available every time. It just never got encoded into anything that changed the next decision.

    What AI Does That We Don’t

    There’s a specific irony in this pattern emerging at the same moment that AI has become the dominant technology investment across industries.

    AI systems learn from the past. Not as a best practice or a cultural value, but as a structural requirement. A model that hasn’t been trained on historical data isn’t a model. The learning is automatic, systematic, and non-optional. Every inference is shaped by everything that came before it.

    More precisely: AI doesn’t just learn what happened. It learns the conditions under which things happened. A recommendation model doesn’t encode that users liked item X. It encodes that users liked item X when they arrived from a certain channel, at a certain point in their journey, after certain prior interactions. The context is part of the pattern. The how is part of what gets learned.

    We tend to only learn from the what.

    The product team changes the idea but not the method. The senior leader changes the team but not the diagnosis. The organization runs the post-mortem but doesn’t connect it to the next decision. We update the what and treat the how as a fresh problem every time.

    And then we deploy AI — a technology whose entire value proposition is systematic learning from history — without applying that same discipline to the organizational decisions about how to deploy it.

    We ask the model to learn from the past. We don’t ask ourselves the same thing.

    The Question Nobody Asks

    Before AI it was a compelling cloud architecture. Before that a web framework that would change everything. The pattern doesn’t belong to any particular technology. Conviction about the what has always had a way of short-circuiting the discipline of asking about the how.

    The question that would break the pattern isn’t complicated. It’s just uncomfortable.

    What has and hasn’t worked before, in this organization, with this kind of change? Not in general. Not at other companies. Here. With these people. Under these conditions. What did we try? What did we learn? What were the conditions when it worked, and what were the conditions when it didn’t?

    And then: given all of that, what’s the approach that gives this the best chance of actually landing?

    Before the next project kicks off, before the roadmap gets locked, before the pitch deck goes to leadership — pull up the last one. Not to relitigate it. To learn from it. Ask: did we change the how, or just the what?

    If the honest answer is just the what, you already know how this one ends.

  • The Question Is the Creativity

    The Question Is the Creativity

    The conversation around AI tends to focus on outputs. This post is about something upstream from that.

    I was in a room with a small group of engineers and product people. We had a standing rule for these sessions: no constraints. Not on money, not on time, not on what the law currently allowed. Laws change, and if we only invented inside existing rules we’d never get ahead of them. The goal was to ask questions nobody had thought to ask yet.

    One of those sessions started with a conversation between me and a colleague. His father had Parkinson’s. My son had ataxia, a neurological condition affecting coordination and balance that causes involuntary movement. We were talking about tremors, how they made it hard for devices to understand intentional gestures, how the technology kept misreading the signal. Somewhere in that conversation someone said: what if we thought about this the way noise-cancelling headphones work? Not filtering out the gesture, but filtering out the tremor. Separating the signal from the noise at the body level.

    That question became a patent.

    The patent room is an extreme version of something that happens every day in less formal settings. The standard for a patent is legal novelty, something that hasn’t existed before in that form. But the same upstream act, two experiences colliding into a question worth asking, happens whenever someone brings their specific life to a problem and asks it differently than anyone else would have. The question doesn’t have to be patentable to matter. It just has to come from somewhere a model can’t go.

    I’ve thought about that moment a lot since AI became the dominant conversation in technology. Because that question could not have come from a model. Not because the model isn’t capable of sophisticated reasoning. It is. But because the question didn’t come from reasoning. It came from two people’s lives colliding in an unconstrained space.

    That’s where creativity actually lives. Not in the execution. In the question.

    What the Room Was Actually Doing

    Those sessions had a specific energy. You removed the practical objections that normally narrow thinking before it has a chance to go anywhere interesting. No one said “that’s too expensive” or “we can’t do that yet.” You created space for people to bring their actual experience, not their professional expertise, but their lives. Their parents. Their kids. Their frustrations. Their observations from places that had nothing to do with the problem on the table.

    And then you let those things collide.

    We did a session focused on aging in place, how to help elderly people live independently longer, especially when their families were far away. Every person in that room had a version of that worry. A parent in another state. A grandparent who had fallen. The anxiety of not knowing. The ideas that came out of that session didn’t come from market research or competitive analysis. They came from people who were living the problem and had been given permission to imagine solutions without limits.

    That combination of unconstrained thinking plus personal stakes is what produces the questions worth asking. Remove the constraints and you get speculation. Add the personal stakes and you get invention.

    The cochlear implant came from a researcher whose child was deaf. He wasn’t analyzing the hearing aid market. He was watching his daughter navigate a world that hadn’t been designed for her and asking whether it had to be that way. The sticky note came from a choir singer who had been frustrated by a bookmark falling out of his hymnal for years before he connected that frustration to an adhesive a colleague had invented that nobody wanted. Neither of those connections was the product of a logical process. They were the product of a life that had been paying attention to a problem long enough to recognize an answer when it appeared from an unexpected direction.

    Personal stakes aren’t the only path to the right question. Plenty of inventors have no direct connection to the problem they’re solving. But stakes create a kind of attention that’s hard to replicate any other way. You notice differently when you care.

    The question came first. The execution came after.

    What AI Actually Does

    AI is extraordinarily good at execution. Given a well-formed question, it can explore the solution space faster and more thoroughly than any human team. It can find patterns across domains that no individual would have the bandwidth to survey. It can synthesize, generate, iterate, and refine at a scale that would have seemed impossible five years ago.

    I use it this way every day. And the back-and-forth of working with a model, the “that’s close but not quite it,” the steering toward something you can sense but not yet fully articulate, has its own creative quality. It reminds me of those patent sessions. The iterative energy of a room where ideas are being shaped in real time, where one person’s response moves another person’s thinking, where the answer emerges through the conversation rather than arriving fully formed.

    But there’s a difference. In that room, every person brought something the others didn’t have. Their specific experience, their specific frustration, their specific version of the problem. The model brings what it was trained on. Which is vast, more than any person in any room could hold, but it is still bounded by what humans have already thought, written, and recorded. It can recombine brilliantly. It cannot bring something from outside its training the way a person brings something from outside their expertise.

    The Constraint That Isn’t a Constraint

    We told those rooms: imagine no constraints. And it worked, because removing the practical filters let thinking go somewhere it couldn’t go otherwise.

    But you could give an AI the same instruction. “Imagine no constraints on money, time, or law. Solve this problem.” And it would produce output. Sophisticated output. Options and combinations and lateral connections across domains it has been trained on. It might even produce something that looks creative.

    What it can’t do is bring something from outside its training into that unconstrained space. The model has no constraints to remove because it has no constraints in the first place, and also no life experience pressing against those constraints, no accumulated frustration waiting for permission to become a question. The removal of constraints only matters if something was being constrained. In a human, what gets constrained is everything they’ve lived and observed and felt and noticed. Remove the filter and that floods in.

    The model has no equivalent. It can go wide within what it knows. It cannot go to the place where my colleague’s father’s tremor and my son’s tremor became the same problem.

    The Most Important Skill

    There’s a version of the AI conversation that focuses on prompting. How to write better prompts. How to get better output. How to work with the tools more effectively. That’s useful, and I’m not dismissing it.

    But I think it understates where the leverage actually is.

    The prompt is downstream of the question. And the question is downstream of the specific collision: your son’s tremor meeting your colleague’s father’s tremor, a choir singer’s bookmark meeting a chemist’s unwanted adhesive, a researcher’s deaf daughter meeting a question about whether the world had to be that way. That’s not experience in the abstract. It’s experience that has been building pressure against a specific problem long enough to recognize an answer when it comes from an unexpected direction.

    That capacity lives in you, not in the tools.

    What the Model Will Never Have

    The model has no son with ataxia. It has no colleague’s father with Parkinson’s. It has no parent far away whose silence worries it. It has no memory of a room where someone said “what if we” and everything shifted. It has no frustration that has been accumulating for years, waiting for the right collision.

    It has no stakes. It notices nothing differently because it cares about nothing. It brings no life to the question because it has no life to bring.

    Those aren’t limitations that will be engineered away. They’re not gaps in the training data or constraints on the context window. They’re the definition of what the tool is.

    And they’re the definition of what you are.

    The question comes from you. The creativity is yours. The execution, extraordinary, useful, genuinely remarkable, is something you can share.

    Don’t confuse the two.

  • The Same Rocket, A Different Ship

    The Same Rocket, A Different Ship

    AI investment is accelerating faster than any technology wave before it. The pattern underneath it is older than most of us realize.

    There is something deeply human about looking up.

    We’ve always done it. We looked at the stars and decided we belonged there. We looked at the horizon and built ships to cross it. We looked at problems that seemed impossible and found ways to make them less so. That instinct, the refusal to accept limits as permanent, is responsible for most of what we’ve built.

    It’s also responsible for a pattern that keeps repeating. And we never seem to learn it.

    Every generation gets a technology that feels like the one. The thing that will finally close the gap between what we can do and what we’ve always wanted to do. Computers. Then software. Then the internet. Then big data. Each one arrived with genuine capability and genuine promise. Each one attracted enormous investment. Each one produced real gains, new industries, new efficiencies, new possibilities that didn’t exist before. And each one, eventually, ran into the distance between what the demo promised and what the real world required.

    That gap never closed the way we expected. So we did what we always do.

    We threw more fuel at it.

    (more…)
  • The Leveler: The Use Cases Nobody Planned For

    The Leveler: The Use Cases Nobody Planned For

    I sat at a table in the gym at my son’s school. Around the room there was a dog groomer, a police detective, someone from the state park maintenance crew, and an archaeologist. We were there for career day. My topic was AI.

    When each group of kids came over, I played them a song. I told them my son made it. He had the idea, shaped the lyrics, and used AI tools to bring it to life. It’s on Spotify and Apple Music. The instruments, the vocals, the production were all generated by AI. But the idea, the story, the words were his.

    Some of the middle school kids had already used ChatGPT. A few had used it for homework. One wanted a training plan for a video game he was trying to get better at. One girl said she used it as someone to talk to.

    That last one stopped me. The adults in the room were thinking about AI in terms of what it might take from these kids. That girl was using it for something none of the adults had thought to offer her.

    (more…)
  • Defining Success Criteria: Do You Know Where You Are Going?

    Defining Success Criteria: Do You Know Where You Are Going?

    It was the tenth call.

    I had been on the first one. A customer data project, matching records across systems to connect outcomes to the right source. It seemed straightforward enough that I handed it off and moved on. What followed was eight more calls between my team and the customer, each one ending with a tweak to the logic, each tweak fixing something and revealing something else.

    The problem wasn’t the code. It wasn’t the team. It was that nobody had defined success criteria before the work started. Nobody had asked: what does done actually look like?

    By the time my team pulled me back in, it was already swirling. The customer’s manager had joined too. I suspect that because the issue still wasn’t resolved, he felt the need to get involved. We had a piece of logic built by people who were no longer on the team, results that were almost always right, and a team that had been grinding on this for weeks.

    I asked a simple question: What do you actually want?

    That was it. Every previous conversation had been about what the code wasn’t doing right, assuming the approach was sound and just needed adjustment. Nobody had stopped to ask whether the approach itself was the right one. The answer to that simpler question was: when this happens, here is what we expect to see. And the solution that followed was far simpler than what we had been building toward. A defined set of conditions, clearly mapped to outcomes. The complexity we had been wrestling with wasn’t a feature of the problem. It was a feature of never having properly defined the problem.

    We left that meeting with a clear destination. We should have had that conversation on call one.

    (more…)
  • Enterprise AI Implementation: You Were Promised Everything. Here’s What It Took.

    Enterprise AI Implementation: You Were Promised Everything. Here’s What It Took.

    It was, by all appearances, a standard enterprise AI implementation.

    The summaries looked clean.

    At the top of the screen was a concise paragraph capturing a customer interaction: what was requested, what was explained, and what follow-up was required. Action items were listed neatly below. It was the kind of output you could screenshot for a slide deck. Efficient. Polished. Convincing.

    The premise was simple. If employees spent less time documenting interactions, they could spend more time serving customers. Efficiency would increase. Costs would decrease. The model worked in the demo. It summarized transcripts fluently and quickly. The business case felt straightforward.

    It moved forward.

    The strain didn’t appear in the demo. It appeared in real use.

    (more…)
  • The Illusion of Intelligence: How We’re Still Missing the Promise of AI

    The Illusion of Intelligence: How We’re Still Missing the Promise of AI

    When I started my first role as a product manager, my portfolio included solutions built with AI. I leveraged my product experience and joined forces with a team of data scientists as we sought to tackle complex problems with data.

    It was a great time to be in the space. Everyone wanted to work with AI, and the promise of an AI-driven future was highlighted at every quarterly meeting and town hall. There were piles of data and just enough maturity in the tools and teams to start developing and deploying powerful algorithms at scale.

    But progress was much slower than most people anticipated.

    We stood in front of the piles of data without a shovel, unable to make efficient use of the resource because it was in the wrong format or inside an inaccessible system. Sometimes, we’d discover too late that our intuition about the relevance of the data in a particular pile was wrong, and the data wasn’t useful to solve a particular problem.

    To overcome those challenges, we’d start by looking for more data by checking other piles or generating additional data by adding telemetry to our systems to close the gaps in coverage.

    If we couldn’t assemble what we needed to solve that particular problem, we’d try to reshape the problem, or we’d move on to the next problem to solve. But if we happened to find what we needed, we ran into our next hurdle: armchair data scientists—people who watched a demo, skimmed an article, and came away convinced they knew how to build the model better than the experts trained to do it.

    When I said that everyone wanted to work with AI, I meant everyone. In some cases, developers would head to Coursera to learn about AI and how to create algorithms. Others went back to school to get an advanced degree in machine learning or statistics. As a proponent of continuing education, I applauded these efforts to level up their knowledge and skills, and, for the most part, these individuals became curious allies, trying to learn about the process from the inside.

    But the armchair data scientists, often in leadership or decision-making positions, were more disruptive. They would watch a video or read an article, then send it to the team with a brief note, stating only, “We should do this.” There would be no context, no knowledge of how the technology worked, or if what they found addressed a problem or challenge we were facing.

    For the most part, we could deflect these suggestions through thoughtful responses, explaining that we were already doing what they suggested, or why it wasn’t relevant to the actual problem we were solving.

    The more draining interactions were from leaders who wanted to prescribe how a model should be built, sometimes implicitly but often explicitly. They wanted the algorithm to reflect a vision of what they felt a system should do based on their intuition, even if their vision wasn’t technically possible—or even relevant—to the problem at hand. They would prescribe what data should be used, what data should be excluded, or how a model should be trained.

    They tried to influence how predictions were interpreted. They challenged results that didn’t feel intuitive, even when the outcomes were reproducible, measurable, and backed by data. This sometimes created another round of training a separate model driven by feelings, followed by a side-by-side comparison that consistently showed the data science approach performed better than one based on intuition and feelings.

    I’ve sat with some of those same executives to review the results of a model, only to be met with their disappointment, especially when they felt there should be a logical, straightforward solution to a problem that was so complex that it couldn’t be solved or even attempted without AI.

    They would question why the predictions weren’t 100% accurate. Even when I pointed out that we were predicting the future from past data, and even if the model was right 60% of the time, and the previous human-driven process was right only 10% of the time, their questions focused only on achieving the impossible 100%. They’d leave value on the table chasing perfection when they could improve a process now and hope to improve it over time. Or they’d go off on tangents and hyperfocus on edge cases for which there was no solution and often no data to even attempt to use to train an algorithm.

    If the performance was impressive, they’d attempt to move the targets by setting an arbitrarily higher bar, or they’d switch from the measurable metric to a different metric or an abstraction like “trust” without providing direction on what that meant or how to measure it. Trust in what? Accuracy? Fairness? Transparency? Nobody could say. When asked for clarification, the response was the classic Justice Potter Stewart response, “I know it when I see it.”

    In the end, it always seemed like AI was a disappointment. Unless it could solve 100% of a problem 100% of the time, no matter how complex or how poorly humans performed before, leaders would keep chasing a unicorn, while ignoring the perfectly capable, faster horse already in the stable.

    Over and over, the pattern was the same: impossible expectations, misunderstanding of the tools, and a tendency to chase magical thinking over measurable progress.

    Here We Go Again

    Fast forward a few years, and we’re back at it. Only this time, the technology looks smarter. LLMs have reignited AI’s promise with a seductive twist: they speak fluently. They write. They reason. They respond. And for many, that’s been enough to assume that LLMs also understand.

    But just like before, we’ve let the illusion get ahead of the reality.

    While LLMs make it easier than ever to demo something impressive, they haven’t made it easier to deliver something useful. Underneath the conversational surface, the same problems persist: inaccessible data, unclear problems, and unrealistic expectations. In fact, the expectations are even worse now, because the technology feels like it’s already “there.”

    I’ve seen teams leap into building generative AI “solutions” without a clear understanding of what problems they’re solving. I’ve seen leadership get swept up in generative demos and approve massive budgets to chase abstract goals like “productivity” or “creativity” without metrics, definitions, or infrastructure.

    The same pattern is playing out again. Impossible expectations, except this time they’re even higher. A misunderstanding of the tools, especially when it comes to differentiating the hype from the reality. And the same magical thinking chasing a hypothetical problem rather than focusing on a real problem with measurable outcomes.

    Two years in, we’re starting to see the same disappointment creep in again, too. The unrealized expectations, longer timelines, and lack of returns on the investments.

    What Useful AI Actually Looks Like

    Useful AI doesn’t always look like magic. In fact, the most valuable AI systems I’ve seen rarely impress anyone in a demo. They don’t write poetry, simulate conversation, or generate pitch decks with a prompt. They just quietly make things better—faster, cheaper, more consistent, more scalable.

    They are, by most standards, boring.

    A model that flags billing anomalies in a healthcare system might save millions. A classifier that routes customer service tickets to the right team might shave minutes off every support interaction. An optimization algorithm that suggests more efficient delivery routes could reduce fuel costs, improve ETAs, and shrink carbon footprints. None of these use generative AI. None of them are headline-worthy. But all of them create real value.

    And unlike a chatbot that sometimes gives the wrong answer with great confidence, these systems are narrow by design. Purpose-built. Measured. Tightly integrated into workflows and optimized over time. They don’t need to sound human. They just need to work.

    We often overlook this kind of AI because it’s not exciting to watch. It doesn’t feel like the future. But that’s exactly the point: the best AI doesn’t draw attention to itself. It dissolves into the process, making things work better than they did before.

    The Opportunity Cost of the Hype

    The hype around LLMs has sparked a renewed interest in AI, but it’s also warped our sense of what progress looks like. Instead of focusing on impact, we’ve become obsessed with spectacle.

    Executives see a demo of a chatbot that answers questions with a human-like cadence and see the realization of the vision that they’ve had for AI all along. Suddenly, every team is greenlit to build a “copilot.”

    LLMs make it easy to show something impressive. A few prompts, a fancy UI, and you’ve got a prototype that feels like innovation. But most of these tools don’t stand up to basic scrutiny. They hallucinate. They break when connected to real systems. They introduce ambiguity into workflows that once relied on clarity. They create new risks—ethical, operational, and technical—that teams are often unprepared to manage.

    We’re pouring talent, time, and money into building AI wrappers around problems we haven’t defined. Meanwhile, the infrastructure work that would actually make AI useful—cleaning data, improving feedback loops, building explainable systems—is neglected.

    This is the code of chasing the hype. Years of expensive effort with little or no realization of value, certainly to the scale that was promised. Real problems that didn’t require generative AI but could have been solved and added real value were ignored or neglected. Two years in, it turns out we’ve been sprinting on a treadmill. We’ve spent the energy, but we’re still in the same place.

    To be clear, there’s nothing wrong with experimentation. But exploration without a clear problem or success metric isn’t innovation—it’s expensive theater. It gives the illusion of progress while distracting from work that actually moves the needle.

    And we should know the difference because we’ve been here before. We let unrealistic expectations undermine the progress of last-generation AI. Now, we’re doing it faster. We’re skipping the discipline that made the old models work and replacing it with a dopamine hit from a prompt that feels smart.

    A Better Mindset: Value Over Novelty

    If the last two waves of AI taught us anything, it’s this: the technology is only as good as the problems we point it at and the people we trust to solve them.

    Too often, we let novelty set the direction. We ask, “What can we build with this?” instead of “What’s worth solving?” But even when we pick the right problems, we don’t always empower the right people to do the work.

    Instead, we’re seeing a return of the same behavior that stalled progress last time: leaders prescribing not just what to solve, but how to solve it. Dictating which data to use. Demanding specific architectures. Redefining outcomes midstream based on intuition instead of evidence. In some cases, they’re building solutions backwards from a flashy demo instead of forward from a real need.

    This isn’t strategy—it’s armchair data science all over again.

    And it’s especially risky now, because LLMs make it even easier to look smart without being right. It’s one thing to brainstorm ideas. It’s another to second-guess trained experts who understand the constraints, trade-offs, and mechanics of building something that works.

    A better mindset means more than just optimizing for usefulness. It means creating space for people with real expertise—data scientists, engineers, researchers, designers—to lead the “how.”

    It means:

    • Letting evidence drive decisions, not gut instinct or LinkedIn hype.
    • Empowering teams to solve, not just execute.
    • Recognizing that success isn’t always intuitive—and being okay with that.

    Adopting this mindset doesn’t mean ignoring new tech. It means respecting the disciplines that make that tech useful. It means pairing vision with humility, ambition with trust.

    Because there’s nothing wrong with being impressed by what’s possible.

    But if we’re serious about delivering real value with AI, we have to get out of our own way—and let the experts do their jobs.

  • The Dulling of Innovation

    The Dulling of Innovation

    For a few years, I was on a patent team. Our job was to drive innovation and empower employees to come up with new ideas and shepherd them through the process to see if we could turn those ideas into patents.

    I loved that job for many reasons. It leveraged an innovation framework I had already started with a few colleagues—work that earned us a handful of patents. It fed my curiosity, love for technology, and joy of being surrounded by smart people. Most of all, I loved watching someone light up as they became an inventor.

    I worked with an engineer who had an idea based on his deep knowledge of a specific system. Together, we expanded on that idea and turned it into an innovative solution to a broader problem. The look on his face when his idea was approved for patent filing was one of the greatest moments of my career. For years after, he would stop me in the hallway just to say hello and introduce me as the person who helped him get a patent.

    Much of the success I saw on that team came from people who deeply understood a problem, were curious to ask why, and believed there had to be a better way. That success was amplified when more than one inventor was involved, when overlapping experiences and diverse perspectives combined into something truly original.

    When I moved into product management, the same patterns held true. The most successful ideas still came from a clear understanding of the problem, deep knowledge of the system, and the willingness to explore different perspectives.

    Innovation used to be a web. It was messy, organic, and interconnected. The spark came from deep context and unexpected collisions.

    But that process is starting to change.

    Same High, Lower Ceiling

    In this new age of large language models (LLMs), companies are looking for shortcuts for growth and innovation and see LLMs as the cheat code.

    Teams are tasked with mining customer comments to synthesize feedback and generate feature ideas and roadmaps. If the ideas seem reasonable, they are executed without further analysis. Speed is the goal. Output is the metric.

    Regardless of size or maturity, every company can access the tools and capabilities once reserved for tech giants. Generative AI lowers the barrier to entry. It also levels the playing field, democratizing innovation.

    But what if it also levels the results?

    When everyone uses the same models, is trained on the same data, and is prompted in similar ways, the ideas start to converge. It’s innovation by template. You might move faster, but so is everyone else, and in the same direction.

    Even when applied to your unique domain, the outputs often look the same. Which means the ideas are starting to look the same, too.

    AI lifts companies that lacked innovation muscle, but in doing so, it risks pulling down those that had built it. The average improves, but the outliers vanish. The floor rises, but the ceiling falls.

    We’re still getting the high. But it doesn’t feel like it used to.

    The Dopamine of Speed

    The danger is that we’re not going to see it happening. Worse, we’re blindly moving forward without considering the long-term implications. We’re so fixated on speed that it’s easy to convince ourselves that we’re moving fast and innovating.

    We confuse motion for momentum, and output for originality. The teams and companies that move the fastest will be rewarded. Natural selection will leave the slower ones behind. Speed will be the new sign of innovation. But just because something ships fast doesn’t mean it moves us forward.

    The dopamine hit that comes from release after release is addictive, and we’ll need more and more to feel the same level of speed and growth. We’ll rely increasingly on these tools to get our fix until it stops working altogether. Meanwhile, the incremental reliance on these tools dulls effectiveness and erodes impact, and our ability to be creative and innovate will atrophy.

    By the time we realize the quality of our ideas has flattened, we’ll be too dependent on the process to do anything differently.

    The Dealers Own the Supply

    And those algorithms? They’re owned by a handful of companies. These companies decide how the models behave, what data they’re trained on, and what comes out of them.

    They also own the data. And it’s only a matter of time before they start mining it for intellectual property—filing patents faster than anyone else can, or arguing that anything derived from their models is theirs by default.

    Beyond intellectual property and market control, this concentration of power raises more profound ethical and societal questions. When innovation is funneled through a few gatekeepers, it risks reinforcing existing inequalities and biases embedded in the training data and business models. The diversity of ideas and creators narrows, and communities without direct access to these technologies may be left behind, exacerbating the digital divide and limiting who benefits from AI-driven innovation.

    The more we rely on these models, the more we feed them. Every prompt, interaction, and insight becomes part of a flywheel that strengthens the model and the company behind it, making it more powerful. It’s a feedback loop: we give them our best thinking, and they return a usable version to everyone else.

    LLMs don’t think from first principles—they remix from secondhand insight. And when we stop thinking from scratch, we start building from scraps.

    Because the answers sound confident, they feel finished. That confidence masks conformity, and we mistake it for consensus.

    Innovation becomes a productized service. Creative edge gets compressed into a monthly subscription. What once gave your company a competitive advantage is now available to anyone who can write a halfway decent prompt.

    Make no mistake, these aren’t neutral platforms. They shape how we think, guide what we explore, and, as they become more embedded in our workflows, influence decisions, strategies, and even what we consider possible.

    We used to control the process. Now we’re just users. The same companies selling us the shortcut are quietly collecting the toll.

    When the supply is centralized, so is the power. And if we keep chasing the high, we’ll find ourselves dependent on a dealer who decides what we get and when we get it.

    Rewiring for Real Innovation

    This isn’t a call to reject the tools. Generative AI isn’t going away, and used well, it can make us faster, better, and more creative. But the key is how we use it—and what we choose to preserve along the way.

    Here’s where we start:

    1. Protect the Messy Middle

    Innovation doesn’t happen at the point of output. It happens in the friction. The spark lives in debate, dead ends, and rabbit holes. We must protect the messy, nonlinear process that makes true insight possible.

    Use AI to accelerate parts of the journey, not to skip it entirely.

    2. Think from First Principles

    Don’t just prompt. Reframe. Instead of asking, “What’s the answer?” ask, “What’s the real question?” LLMs are great at synthesis, but breakthroughs come from original framing.

    Start with what you know. Ask “why” more than “how.” And resist the urge to outsource the thinking.

    3. Don’t Confuse Confidence for Quality

    A confident response isn’t necessarily a correct one. Learn to interrogate the output. Ask where it came from, what it’s assuming, and what it might be missing.

    Treat every generated answer like a draft, not a destination.

    4. Diversify Your Inputs

    The model’s perspective is based on what it’s been trained on, which is mostly what’s already popular, published, and safe. If you want a fresh idea, don’t ask the same question everyone else is asking in the same way.

    Talk to people. Explore unlikely connections. Bring in perspectives that aren’t in the data.

    5. Make Thinking Visible

    The danger of speed is that it hides process. Write out your assumptions. Diagram your logic. Invite others into the middle of your thinking instead of just sharing polished outputs.

    We need to normalize visible, imperfect thought again. That’s where the new stuff lives.

    6. Incentivize Depth

    If we reward speed, we get speed. If we reward outputs, we get more of them. But if we want real innovation, we need to measure the stuff that doesn’t show up in dashboards: insight, originality, and depth of understanding.

    Push your teams to spend time with the problem, not just the solution.

    Staying Sharp

    We didn’t set out to flatten innovation. We set out to go faster, to do more, to meet the moment. But in chasing speed and scale, we risk trading depth for derivatives, and originality for automation.

    Large language models can be incredible tools. They can accelerate discovery, surface connections, and amplify creative potential. But only if we treat them as collaborators, not crutches.

    The danger isn’t in using these models. The danger is in forgetting how to think without them.

    We have to resist the pull toward sameness. We have to do the slower, messier work of understanding real problems, cultivating creative tension, and building teams that collide in productive ways. We have to reward originality over velocity, and insight over output.

    Otherwise, the future of innovation won’t be bold or brilliant.

    It’ll just be fast.

    And dull.

  • AI First, Second Thoughts

    AI First, Second Thoughts

    Over the past few weeks, several companies have made headlines by declaring an “AI First” strategy.

    Shopify CEO Tobi Lütke told employees that before asking for additional headcount or resources, they must prove the work can’t be done by AI.

    Duolingo’s CEO, Luis von Ahn, laid out a similar vision, phasing out contractors for tasks AI can handle and using AI to rapidly accelerate content creation.

    Both companies also stated that AI proficiency will now play a role in hiring decisions and performance reviews.

    On the surface, this all sounds reasonable. If generative AI can truly replicate—or even amplify—human effort, then why wouldn’t companies want to lean in? Compared to the cost of hiring, onboarding, and supporting a new employee, AI looks like a faster, cheaper alternative that’s available now.

    But is it really that simple?

    First, there was AI Last

    Before we talk about “AI First,” it’s worth rewinding to what came before.

    I’ve long been an advocate of what I’d call an “AI Last” approach, so the “AI First” mindset is a shift for me.

    Historically, I’ve found that teams often jump too quickly to AI as the sole solution, due to significant pressure from the top to “do more AI.” It showed a lack of understanding of what AI is, how it works, its limitations, and its cost. The mindset of sprinkling magical AI pixie dust over a problem and having it solved is naive and dangerous, often distracting teams from a much more practical solution.

    Here’s why I always pushed for exhausting the basics before reaching for AI:

    Cost

    • High development and maintenance costs: AI solutions aren’t cheap. They require time, talent, and significant financial investment.
    • Data preparation overhead: Training useful models requires large volumes of clean, labeled data—something most teams don’t have readily available.
    • Infrastructure needs: Maintaining reliable AI systems often means investing in robust MLOps infrastructure and tooling.

    Complexity

    • Simple solutions often work: Business logic, heuristics, or even minor process changes can solve the problem faster and more predictably.
    • Harder to maintain and debug: AI models are opaque by nature—unlike rule-based systems, it’s hard to explain why they behave the way they do.
    • Performance is uncertain: AI models can fail in edge cases, degrade over time, or simply underperform outside of their training environment.
    • Latency and scalability issues: Large models—especially when accessed through APIs—can introduce unacceptable delays or infrastructure costs.

    Risk

    • Low explainability: In regulated or mission-critical settings, black-box AI systems are a liability.
    • Ethical and legal exposure: AI can introduce or amplify bias, violate user privacy, or produce harmful or offensive outputs.
    • Chasing hype over value: Too often, teams build AI solutions to satisfy leadership or investor expectations, not because it’s the best tool for the job.

    What Changed?

    So why the shift from AI Last to AI First?

    The shift happened not just because of what generative AI made possible, but how effortless it made everything look.

    Generative AI feels easy.

    Unlike traditional AI, which required data pipelines, modeling, and MLOps, generative AI tools like ChatGPT or GitHub Copilot give you answers in seconds with nothing more than a prompt. The barrier to entry feels low, and the results look surprisingly good (at first).

    This surface-level ease masks the hidden costs, risks, and technical debt that still lurk underneath. But the illusion of simplicity is powerful.

    Generalization expands possibilities.

    LLMs can generalize across many domains, which lowers the barrier to trying AI in new areas. That’s a significant shift from traditional AI, which typically had narrow, custom-built models.

    AI for everyone.

    Anyone—from marketers to developers—can now interact directly with AI. This democratization of AI access represents a significant shift, accelerating adoption, even in cases where the use case is unclear.

    Speed became the new selling point.

    Prototyping with LLMs is fast. Really fast. You can build a working demo in hours, not weeks. For many teams, that 80% solution is “good enough” to ship, validate, or at least justify further investment.

    That speed creates pressure to bypass traditional diligence, especially in high-urgency or low-margin environments.

    The ROI pressure is real.

    Companies have made massive investments in AI, whether in cloud compute, partnerships, talent, or infrastructure. Boards and executives want to see returns. “AI First” becomes less of a strategy and more of a mandate to justify spend.

    It’s worth mentioning that this pressure sometimes focuses on using AI, not using it well.

    People are expensive. AI is not (on the surface).

    Hiring is slow, expensive, and full of risk. In contrast, AI appears to offer infinite scale, zero ramp-up time, and no HR overhead. For budget-conscious leaders, the math seems obvious.

    The hype machine keeps humming.

    Executives don’t want to be left behind. Generative AI is being sold as the answer to nearly every business challenge, often without nuance or grounding in reality. Just like with traditional AI, teams are once again being told to “add AI” without understanding if it’s needed, feasible, or valuable.

    It feels like a shortcut.

    There’s another reason “AI First” is so appealing: it feels like a shortcut.

    It promises to bypass the friction, delay, and uncertainty of hiring. Teams can ship faster, cut costs, and show progress—at least on the surface. In high-pressure environments, that shortcut is incredibly tempting.

    But like most shortcuts, this one comes with consequences.

    Over-reliance on AI can erode institutional knowledge, create brittle systems, and introduce long-term costs that aren’t immediately obvious. Models drift. Prompts break. Outputs change. Context disappears. Without careful oversight, today’s efficiency gains can become tomorrow’s tech debt.

    Moving fast is easy. Moving well is harder. “AI First” can be a strategy—but only when it’s paired with rigor, intent, and a willingness to say no.

    What’s a Better Way?

    “AI First” isn’t inherently wrong, but without guardrails, it becomes a race to the bottom. A better approach doesn’t reject AI. It reframes the question.

    Yes, start with AI. But don’t stop there. Ask:

    • Is AI the right tool for the problem?
    • Is this solution resilient, or just fast?
    • Are we building something sustainable—or something that looks good in a demo?

    A better way is one that’s AI-aware, not AI-blind. That means being clear-eyed about what AI is good at, where it breaks down, and what it costs over time.

    Here are five principles I’ve seen work in practice:

    Start With the Problem, Not the Technology

    Don’t start by asking “how can we use AI?” Start by asking, “What’s the problem we’re trying to solve?”

    • What does success look like?
    • What are the constraints?
    • What’s already working—or broken?

    AI might still be the right answer. But if you haven’t clearly defined the problem, everything else is just expensive guesswork.

    Weigh the Tradeoffs, Not Just the Speed

    Yes, AI gets you something fast. But is it the right thing?

    • What happens when the model changes?
    • What’s the fallback if the prompt fails?
    • Who’s accountable when it goes off the rails?

    “AI First” works when speed is balanced by responsibility. If you’re not measuring long-term cost, you’re not doing ROI—you’re doing wishful thinking.

    Build for Resilience, Not Just Velocity

    Shortcuts save time today and create chaos tomorrow.

    • Document assumptions.
    • Build fallback paths.
    • Monitor for drift.
    • Don’t “set it and forget it.”

    Treat every AI-powered system like it’s going to break, because eventually, it will. The teams that succeed are the ones who planned for it.

    Design Human-AI Collaboration, Not Substitution

    Over-automating can backfire. When people feel like they’re just babysitting machines—or worse, being replaced by them—you lose the very thing AI was supposed to support: human creativity, intuition, and care.

    The best systems aren’t human-only or AI-only. They’re collaborative.

    • AI drafts, people refine.
    • AI scales, humans supervise.
    • AI suggests, humans decide.

    This isn’t about replacing judgment, it’s about amplifying it. “AI First” should make your people better, not make them optional.

    Measure What Actually Matters

    A lot of AI initiatives look productive because we’re measuring the wrong things.

    More output ≠ better outcomes.

    And if everyone is using the same AI tools in the same way, we risk a monoculture of solutions—outputs that look the same, sound the same, and think the same.

    Real creativity and insight don’t come from the center. They come from the edges, from the teams that challenge assumptions and break patterns. Over-reliance on AI can mute those voices, replacing originality with uniformity.

    Human memory is inefficient and unreliable in comparison to machine memory. But it’s this very unpredictability that’s the source of our creativity. It makes connections we’d never consciously think of making, smashing together atoms that our conscious minds keep separate. Digital databases cannot yet replicate the kind of serendipity that enables the unconscious human mind to make novel patterns and see powerful new analogies of the kind that lead to our most creative breakthroughs. The more we outsource our memories to Google, the less we are nourishing the wonderfully accidental creativity of our consciousness.

    Ian Leslie, Curious: The Desire to Know and Why Your Future Depends on It

    If we let AI dictate the shape of our work, we may all end up building the same thing—just faster.

    More speed ≠ more value.

    Instead of counting tasks, measure trust. Instead of tracking volume, track quality. Focus on the things your customers and teams actually feel.

    The Real “AI First” Advantage

    The companies that win with AI won’t be the ones who move the fastest.

    They’ll be the ones who move the smartest. They’ll be the ones who know when to use AI, when to skip it, and when to slow down.

    Because in the long run, discipline beats urgency. Clarity beats novelty. And thoughtfulness scales better than any model.

    The real power of AI isn’t in what it can do.

    It’s in what we choose to do with it.

  • Product Management is Dead

    Product Management is Dead

    My social media feeds have been inundated lately with bold assertions and proclamations about the future of product management.

    • Do we still need product managers?
    • Is AI going to replace product teams?
    • Has product…died?

    The claims tend to follow a predictable pattern:

    • AI writes user stories and PRDs.
    • AI generates user personas.
    • AI summarizes feedback and explores pain points.
    • AI prioritizes roadmaps.

    It makes for a compelling headline, often pushed by companies or consultants selling tools or services that claim to automate these tasks. These headlines grab attention, spark debate, and tap into the anxiety many product managers feel as AI reshapes their role.

    But this isn’t a funeral. It’s a reckoning. The old, process-heavy, adaptability-light version of product won’t survive. But that’s not the end of product. It’s the beginning of something better. Beneath the clickbait is a valid call to evolve: product management isn’t dying, it’s transforming.

    What Product Really Is

    Before we talk about what’s changing, let’s be clear about what product is.

    Product management isn’t a set of tasks. It’s a discipline of focus, alignment, and judgment.

    It’s about understanding problems deeply, prioritizing effectively, and creating the conditions for great teams to build the right things.

    AI can assist with this work, but it can’t own it. And if you think product is just a list of tasks?

    You’re already doing it wrong.

    Why People Want Product Dead

    Product is often seen as a bottleneck. It’s seen as the layer that slows down builders with meetings, documents, and decisions that feel like bureaucracy. In fast-moving, engineering-led organizations, product often looks like something that should be automated rather than a discipline rooted in insight, prioritization, and alignment.

    AI has only amplified that impulse. With tools that can instantly generate specs, synthesize feedback, and mock up features, product starts to look like a collection of tasks rather than a strategic function. And if it’s just tasks, why not let the machines do it?

    That thinking is tempting, especially to companies chasing speed and efficiency. But it’s also shortsighted. Still, the “product is dead” narrative keeps getting airtime because companies want it to be true, even if it misses the bigger picture.

    Speed Over Strategy: Engineering-Led Cultures Prefer Shipping

    In many engineering-led cultures, especially in AI, there’s a deep bias toward building, shipping fast, testing fast, and iterating fast. AI has collapsed the cost of experimentation. And with today’s AI tools, it’s never been easier to vibe code (i.e., rapidly stitch together working demos using AI and low-code tools) your way to a working prototype. You can spin up UIs, connect APIs, and generate sample data in hours instead of weeks. It looks and feels like progress.

    But without intention, you’re not building products, you’re building distractions. You’re producing, not progressing. You’re generating output, not outcomes.

    And that’s the trap: it feels like you’re moving faster, but without a clear understanding of the customer, the problem, and the strategy, you’re either moving in circles or heading in the wrong direction entirely.

    Task-Based Thinking: Why Product Looks Replaceable

    The appeal is obvious: automate the “middle layer,” and suddenly, your team is leaner, faster, and cheaper. Product work is reframed as a series of repeatable tasks: write a story, generate a persona, summarize feedback, and stack rank a backlog. It’s presented as something mechanical, like configuring an assembly line, rather than requiring focus, intention, and insight.

    But this framing is dangerously incomplete. These aren’t just tasks; they’re judgment calls. They ensure teams solve the right problems in the right way at the right time. Discovery without direction is noise. Strategy without prioritization is chaos. Specifications without insight are just empty documentation.

    AI can assist with product work, but reducing it to a checklist makes it easier to sell a tool but harder to build anything meaningful.

    A Convenient Story: The Simplified Narrative That Sells

    It’s a narrative that promises clarity: eliminate the middle layer, remove the blockers, and let machines and makers do what they do best. This strategy plays perfectly in a world obsessed with efficiency and in organizations that already see product management as overhead.

    But the truth is messier.

    Good product managers don’t just write tickets or relay requests. They bring cohesion to chaos. They align teams around a shared understanding of the customer, the problem, and the goal. They ask the hard questions that AI can’t answer on its own.

    Should we build this? Why now? What matters most?

    AI can produce content, but not conviction. It can analyze feedback, but not frame a vision. And it can’t resolve the tensions between user needs, business goals, and technical constraints — at least not without someone to interpret, prioritize, and lead.

    The “product is dead” story works because it feels simple. But building good products was never simple. Removing the people who deal with complexity doesn’t make it go away. It just makes it your customer’s problem.

    The Companies Who Will Regret This

    Here’s my prediction:

    • The companies that cut product first will move fastest at first.
    • Their roadmaps will fill up. Their launches will accelerate. Their demos will look impressive.

    But then, slowly and quietly, things will start to break.

    • Customer engagement will slip.
    • Retention will fall.
    • New features will feel disconnected from real needs.
    • Teams will build for what’s easy, not for what’s valuable.

    The companies that sold them those shiny new tools, the ones that promised to replace product? They’ll be long gone, moving on to the next buyer or looking for the next hype cycle to exploit.

    Meanwhile, the companies that doubled down on the real craft of product, who invested in judgment, customer obsession, and asking why before what, will still be standing (and thriving) while others fade. They’ll have products that resonate and that evolve with their customers.

    Because tools don’t create strategy.

    People do.

    Old Product Might Be Dead — And That’s a Good Thing

    Now, here’s where I’ll agree with the AI evangelists: old product needed to change.

    The days of PMs acting as backlog managers, Jira ticket writers, or meeting schedulers? Yeah, that should die.

    PMs who only handed off requirements to engineering? Gone.

    PMs who never talked to customers? Dead.

    Let’s be honest: many organizations misdefined the PM role. They hired process managers and called it product. They built layers of communication, not layers of clarity. They were managing workflows, not products. They were pushing tickets, not pushing strategy. Those roles are vulnerable not because of AI, but because they weren’t doing product in the first place.

    The version of product that survives this shift and is worth fighting for is sharper, faster, and more essential than ever. It’s not about being an intermediary between engineering and design. It’s about creating clarity, focus, and vision where there was once noise and confusion.

    It looks like:

    • Problem curation over solution obsession: It’s not about finding the quickest fix or building what’s easiest. It’s about understanding what problem we’re solving, for whom, and why it matters.
    • Judgment over process: AI can help automate the steps, but it can’t tell you if you’re solving the right problem or if the timing is right. Good product management is still a series of thoughtful decisions, not just steps in a flowchart.
    • Context over control: Dictating requirements from above doesn’t work anymore. Context, shared understanding, and alignment are what drive teams to collaborate effectively, not command and control.
    • Collaboration over command: PMs are the glue that brings engineering, design, and business together. But that means being a partner and enabler, not a dictator. Collaboration is the new currency in product development.
    • Customer truth over corporate theater: Building the right product requires honest feedback, real conversations with customers, and deep empathy. It’s not about making the product look good on paper; it’s about making it work for the people who use it.

    The old way of doing product is over. But this isn’t about mourning its loss. It’s about embracing a new, more purposeful approach. The role of product management is evolving, and in many ways, that’s a huge opportunity to do better, build better, and have a bigger impact.

    The King Is Dead. Long Live the King.

    The “product is dead” narrative is loud right now because it’s easy. It’s easier to believe we can automate judgment than it is to build it. Easier to replace complexity than to wrestle with it. Easier to promise speed than to commit to substance.

    But the companies that endure — the ones that create real value, not just hype-fueled demos — will be the ones that lean into the harder, more human work.

    They’ll treat product not as a process to optimize, but as a practice to sharpen.

    They’ll embrace AI as a powerful tool — not a replacement for the thinking, intuition, and collaboration that make great products possible.

    They’ll stop treating product like a middle layer to cut, and start recognizing it as a critical function to elevate.

    Because here’s the truth: the best product teams won’t just survive this shift. They’ll lead it.

    They’ll be faster because they’re clearer. Smarter because they’re humbler. Stronger because they’re more aligned.

    Product isn’t dead. Bad product is dead. Shallow product is dead. Performative product is dead.

    The age of product isn’t over. The age of better product is just beginning.

    Long live product — not as it was, but as it needs to be.