AI Is Not Your Product Strategy

I was in an executive team meeting not long ago where everyone in the room wanted AI on the roadmap.

Nobody could tell me why.

Not in any useful way. There was a lot of talk about staying competitive, a lot of references to what other companies were shipping, and at least one mention of what a board member had asked about in the last quarterly review. But when I pushed on the actual question, what problem does our user have that AI is the best possible way to solve, the room got quiet.

That meeting is not unusual. I have been in versions of it more times than I can count.

And it is exactly how you end up with an AI feature instead of an AI strategy.

The pressure is real. The clarity usually is not.

I want to be straight about where I am coming from here. I am not skeptical of AI. I have spent over a decade working at the intersection of AI and product. I hold a patent in AI powered personalization. I have built AI roadmaps for board audiences and launched AI features inside regulated industries.

So when I say most teams are building AI for the wrong reasons, I am not saying it from the sidelines.

The pressure to put AI on the roadmap is real and it is coming from everywhere at once. Boards are asking about it. Competitors are announcing it. Sales teams are fielding questions about it from prospects. That pressure is not going away.

But pressure is not a strategy. And shipping something because the pressure to ship is stronger than your clarity about what to ship is one of the most expensive mistakes a product organization can make. I have watched it happen. The features get built. The launch goes out. The adoption numbers come back flat. And then everyone tries to figure out why the thing did not land the way they expected.

Usually the answer is that nobody asked the right question at the start.

The question that actually matters

Before any AI initiative gets on a roadmap, I want to know one thing.

What problem does your user have that AI is uniquely positioned to solve better than anything else you could build?

Not whether AI can technically be applied to the problem. It can be applied to almost anything if you try hard enough. The question is whether it is the right tool. And that answer is not always yes.

AI earns its place when a problem involves pattern recognition at a scale no person could do by hand. When personalization at that level would be impossible to deliver any other way. When the product genuinely gets better the more people use it, because there is a real feedback loop between what the system learns and what the user gets.

If your use case does not have any of that, you might be adding complexity to a problem that already had a simpler solution. Simpler solutions ship faster, break less often, and are a lot easier to explain to the person who has to use the thing every day.

Three questions before anything gets built

When a team brings me an AI initiative, I ask three things before it goes anywhere near the roadmap.

01 - What does the user actually get?

Not what the feature technically does. What changes for the person using it. A faster version of something they already had is not a differentiator anymore. The bar needs to be higher than that.

02 - How does this get better over time?

An AI product that does not improve with use is not really an AI product. It is software with a model attached for the press release. The data flywheel has to be part of the thinking from day one, not something you figure out after launch when adoption stalls.

03 - What happens when it is wrong?

Because it will be wrong sometimes. That is not a bug you engineer away. It is something you design around from day one. How does the user know whether to trust an output? What happens when they catch a mistake? Does the system learn from that correction? These questions belong in the spec, not the post-launch retro.

If a team cannot answer all three before the work starts, the initiative is not ready to be built. It is ready to be defined.

Features ship. Strategy compounds.

A feature can be copied in a quarter. Sometimes faster.

A strategy built around a real data advantage and a user outcome that genuinely improves over time takes years to replicate, if it ever gets replicated at all. That compounding is where sustainable differentiation lives.

Most of what I see on roadmaps right now is features. Some of them are good and genuinely useful to the people using them. But they are not strategies, and it is worth being clear eyed about the difference before the work begins.

A real AI strategy means having a point of view on where the market is going. It means knowing where you actually have an edge and building toward something that gets harder to catch up to over time. It means saying no to some things, and holding a position even when a competitor ships something you did not.

And underneath all of it is the same question I have been asking about every product decision for twenty years.

What does this actually do for the person using it?

If you can answer that clearly, and AI genuinely is the best way to deliver it, you have the start of a real strategy.

If you cannot answer it yet, you have a feature. Get clear on the question first. The roadmap can wait.