Too Technical to Succeed?
How I Let Go of the Technical Mindset to Become a Better AI Product Manager
#beyondAI - Recently, I told a peer, "You're thinking way too technically. You need to let go of the past a bit."
This peer came from a deeply technical background. He was a computer scientist, completely immersed in algorithms, and obsessed with accuracy metrics. He believed that if you could build the best solution from a technical perspective, that was all that mattered. He even spent years researching Artificial Intelligence, dedicated to developing NLP systems that could push boundaries. But now, he was a Product Manager in AI—navigating a space that demanded much more than just technical skills.
Yet, he still approached the challenges with a laser focus on technology. He was always thinking about the smartest algorithms, the highest accuracy, and the most elegant code. We’d be in meetings, and his mind would jump straight to technical solutions, even when we were supposed to be talking about user experience or business needs. I watched him spend hours refining models with his data scientist when a simpler approach could have done the job just as well. Watching him reminded me so much of my own journey.
And in truth? The peer I was advising was me.
When I first transitioned to AI Product Management, I thought my technical expertise was my most valuable asset. And in many ways, it was. But it also became my biggest blind spot. I believed that if I could just architect the best solutions, everything else would naturally fall into place. I had to learn the hard way that being too technical can be just as limiting as not being technical at all.
This post is for all my technical peers out there who are making the leap into Product Management. And for those who have already made that leap, maybe you need to hear this right now too.
"Hi, my name is Jaser, and I was a 'too-technical' PM."
Happy reading 🛋️
What It Means to Be "Too Technical" as an AI Product Manager
As AIPMs, we often wear our technical expertise as a badge of honor. And it’s true—understanding the tech is super important. But here's the thing: if we focus too much on the technical side, we can lose sight of what the user and business really need. Let me share a few lessons I learned along the way:
Pitfall 1: Over-Engineering
At first, I was always striving for technical perfection, aiming to use the most cutting-edge algorithms and fine-tuning models to the smallest details. I wanted every solution to be flawless. But I quickly learned that over-engineering can slow down progress. The business didn't need perfection; it needed solutions that worked well enough to deliver value—and deliver it quickly.
Sometimes, a simpler approach could do the job instead of creating a complex neural network. My urge to use the latest tech often led me to build more than necessary. You can imagine what this meant: it delayed the path to MVP and wasted resources.
Pitfall 2: Losing Sight of the End-User’s Problem
My technical mindset often made me jump to how before I fully understood why. I was always so eager to start building a solution, even before I truly understood the user’s pain points. Over time, I learned that empathy is where effective AIPMs need to begin. You need to put yourself in the user's shoes and really see the problem through their eyes. Only then can you ensure that every solution truly addresses the real needs of the end-user.
Users might ask for a more intuitive search function, and my technical mind would push me toward building a sophisticated NLP model. But sometimes, all that was needed was an enhanced keyword search with some simple filters, which could have been delivered faster and with less effort.
Pitfall 3: Paralysis by Metrics and Data
Data was both my comfort zone and my downfall. I always felt uneasy making decisions unless every single metric was perfectly aligned. Have you ever been stuck in the data spiral, waiting for the perfect answer? You can probably guess what happened—I kept waiting for more data, analyzing, and then overanalyzing. And, as you can imagine, decision-making dragged on, and we ended up missing opportunities. Eventually, I had to face the truth: sometimes, you just need to trust your gut and be willing to test ideas quickly instead of waiting for absolute certainty. This is especially tough for those of us with a technical background because it goes against everything we were taught.
Pitfall 4: Over-focusing on Scalability Too Early
Coming from a technical background, I was obsessed with making sure every solution could scale for millions of users. And here’s the funny part—most of the time, the market wasn't even that big, but my technical mind couldn't let go of this obsession. Scalability is important, sure, but focusing on it too early led me to complicate the architecture for no good reason. I wasted so much precious time when what we really needed was just a simple MVP to validate the idea first and gather real user feedback.
In one case, instead of creating a simple backend that could handle a small user base and iterating from there, I had the wild idea of a distributed microservices architecture that ultimately would have delayed our go-to-market timeline. Thankfully, my more senior developer in the team—who would have made a brilliant Product Manager—steered us in a better direction. Scalability can come later—first, you need to prove that users even want what you're building.
Pitfall 5: Underestimating the Importance of Non-Functional Requirements
At first, I thought that if the core AI model worked well, everything else would just fall into place. But believe me, I even had to learn that non-functional requirements are crucial too, funny but the truth. Things like usability, maintainability, and ease of integration—they're just as important as the core functionality itself. Ignoring these aspects meant we ended up with 'products' that might have been impressive from a technical standpoint, but they were a nightmare to use or integrate. And honestly, can we even call them products if they can't be used? If it can't be used, it's not a real solution. And only real solutions have the potential to grow into successful products. Read more on this idea in the link below.
I worked on an AI-driven tool where I poured all my effort into creating a highly accurate recommendation engine. It felt like a huge win to get those metrics looking perfect. But then came the real challenge—integrating it with our client’s legacy system. Let me tell you, building the model was the easy part. Making it work seamlessly with old, complex infrastructure? That was a completely different beast. It’s something I wish I had thought more about from the beginning, especially in a large enterprise environment.
Why Letting Go (Just a Little, and sometimes a little more) Matters
Now that I've shared some examples of my mistakes, I truly believe they're closely tied to my technical mindset, my education, and my experiences as a data scientist and developer. But you know what? Reflection is what really helps me uncover my blind spots. I have this habit—sometimes it's good, sometimes not so much—of reflecting on almost every aspect of my life. It helps me grow, though I'll admit, it can also lead me to overthink things. And you can google (or chatgpt) to read that overthinking isn't that good. But in this particular case, reflection really helped me understand why I acted the way I did, and it ultimately helped me develop strategies to find a better balance.
As AIPMs, our mission is to deliver value and solve real problems. I think we can all agree on that now.
Technical expertise is definitely an asset as an AIPM, but it's only part of what makes us truly effective. The real impact, I found, came when I stepped back from the technical side and learned to balance it with user insights, business goals, and a willingness to prioritize progress over perfection. Once I understood that there are six key dimensions I need to focus on as an AIPM to even have a chance at success, my mindset shifted—from a purely technical one to what people often call a product mindset.
I've written an entire article about those six dimensions, and you might want to give it a try. It was my key to 'letting go.' It's about deeply understanding the interplay between Data, AI, and IT on one side—the technical trio—and Business, Governance, and People on the other—the strategic trio. If you're only strong on one side, you won't find the balance you need.
The truth is, that being an AI Product Manager means walking a fine line. Being too technical can be just as ineffective as lacking technical depth altogether. Finding that balance was key for me, and maybe it can be for you too.
And I hope you get what I mean by 'letting go.' Sometimes, you have to let go first to make room for something new. Often, that 'something new' turns out to be even better. In this case, it's about letting go of that purely technical mindset—at least for a while—so you can make space for a new, broader mindset to grow.
Where could you benefit from letting go a bit, to create that space for something new?
JBK 🕊️
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