The Moment You Treat Your AI Team Like Devs, Your AI Dies

These days, almost every product has some element of AI in it. Whether it's a recommendation engine, chat assistant, or smart automation — AI is becoming a standard part of modern tech stacks. But here’s the thing: not all Product Owners (POs) or Product Managers (PMs) are equipped to handle both AI and traditional software development effectively.

One of the biggest issues? Not knowing who should do what.

Not All “Smart” Problems Are AI Problems

First, let’s get one thing straight: software development and AI development are fundamentally different.

• Developers (web/app teams) deal with clear logic. If you define a rule, they can build it exactly as intended. Think: “if A, then B.”

• AI engineers, on the other hand, work in probabilities. They train models on data and make educated guesses. Think: “based on the data, there’s an 85% chance this is B.”

Now here’s where things go wrong. Some POs see a task that feels complex or “intelligent” and immediately toss it over to the AI team — even if it's actually a straightforward rules-based task that the dev team should handle.

That’s not just inefficient — it’s a waste of the AI team’s time and energy. Worse, it often results in overcomplicated solutions that don’t perform as well as a basic dev implementation would have.

The Politics of Product Ownership

And then there’s the human side of things.

Sometimes, task delegation doesn’t happen based on logic or team expertise — it happens based on relationships. If a PO is closer to someone on the AI team than the dev team, they might naturally trust them more and push work their way, even when it's not a good fit.

This can hurt the product, the timeline, and the team dynamic. Good people end up working on the wrong problems. Teams get frustrated. And in the end, the company loses.

Why Cross-Team Literacy Matters

You don’t need to be a developer or a machine learning engineer to be a great PO. But you do need to understand the basic differences between the two:

• AI doesn’t always give you 100% correct answers — and that’s okay, as long as the product is designed with that in mind.

• Not every “intelligent” task needs AI. Sometimes a well-written if-statement does the job better.

• Clear task boundaries between dev and AI teams save time, energy, and unnecessary complexity.

Final Thoughts

The line between dev work and AI work is easy to blur — especially when you're juggling feature requests, deadlines, and team dynamics. But a PO who can tell the difference, and delegate accordingly, brings huge value to the table.

So if you’re working in a product role today, ask yourself: am I giving the right task to the right team? And am I basing that decision on tech understanding, not just team chemistry?

Getting that right could make all the difference.