AI Product Strategy Isn’t New But Most Are Getting It Wrong

There’s a misconception going around: that AI product strategy is some entirely new frontier.

But the truth?

Product leaders have been mastering AI-driven product thinking for over two decades.

From Google’s search engine to Amazon’s recommendation engine, AI has been quietly powering the products we use every day. What we’re seeing now with generative AI is just a new chapter in a much longer story.

So why are so many companies fumbling the playbook now?

Let’s take a step back.

AI Has Been Shaping Product Strategy for 20+ Years

Think about it:

• Google Search uses complex ranking algorithms to serve the most relevant results — powered by narrow AI.
• Amazon built a multi-billion-dollar recommendation engine that influences 35% of their revenue.
• Netflix fine-tuned personalization to reduce churn, saving them over $1 billion annually.
• Smart speakers respond to voice, understand intent, and offer real-time interactions.

All of these are examples of narrow AI — systems designed to solve very specific problems, incredibly well. They don’t generalize. A chess engine can’t drive your car. A speech recognition system won’t curate your playlists.

But here’s the key:
These successes weren’t about flashy demos. They were about laser focus on real user needs.

And they’re proof that strategic focus, not general intelligence, is what delivers business value.

The Strategic Lesson of Narrow AI

What made these AI implementations successful wasn’t just the tech.

It was a product mindset:

Solve a specific, well-defined user problem. Solve it brilliantly. Measure the impact.

That’s it. No magic. No AI hype. Just solid product thinking applied to a powerful tool.

So why are we now seeing product strategies that seem to forget this?

4 Common Traps in Today’s AI Product Strategy

Despite all the examples we have to learn from, many companies are falling into familiar traps:

1. AI for the Buzz, Not the Value

“We added AI!” Great. But… why? What problem did it solve? If the answer is unclear, users won’t care — and neither will your bottom line.

2. Tech With No User Need

Many teams start with the model and try to “find” a use case. It’s backward. Real value starts with understanding the user — not the algorithm.

3. Copycat Syndrome

“If they built an AI assistant, we need one too.” But if your assistant does the exact same thing as theirs, what makes you different?

4. Cool Demos Over Real Solutions

We’ve all seen the viral AI demo that does something futuristic and jaw-dropping. But does it solve a problem people have every day? Is it reliable at scale?

Often, the answer is no.

Why This Is Happening

It’s not because product teams suddenly forgot how to build.

It’s because they’re trying to force-fit old playbooks to a completely new canvas.

AI doesn’t just change what’s possible — it changes how users expect to interact with your product.

• The interface evolves: prompts replace buttons.
• The experience changes: users expect adaptability.
• The metrics shift: value comes from outcomes, not clicks.

AI product strategy isn’t just product strategy with a twist.

It’s an entirely new flavor of product thinking. And getting it right requires unlearning some of the habits that worked in the past.

So, What Now?

The companies that succeed with AI will do what great product teams have always done:

• Start with user needs.
• Build solutions, not spectacles.
• Test relentlessly.
• Measure impact, not impressions.

And above all:

Remember that the “AI” part only matters if the “product” part is rock solid.

If this topic sparks curiosity, I broke down these ideas in more depth in my latest article: The 4 AI Strategy Traps — and How to Avoid Them

Because AI might be changing everything.

But the fundamentals of good product strategy?
They still hold the power.

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