AI is not a feature strategy

A chatbot here.
A recommendation engine there.
AI-generated content.
A predictive model.
A copilot added to an existing product.

It looks like progress. But adding AI to products is not the same as having an AI strategy.

Features answer “what?”

An AI feature strategy usually starts with a technology:

“Where can we add AI to our product?”

This can produce useful features, but it often leads to AI for AI’s sake.

Competitors launch AI assistants, so you launch one too. A new model becomes available, so you find somewhere to use it. Customers start asking about AI, so AI appears on the roadmap.

The result can be a portfolio full of AI-enabled products without a clear reason why those products should exist.

An actual AI strategy starts somewhere else:

“Where can AI create meaningful business and customer value?”

That question changes everything.

AI should influence the portfolio

AI can do more than enhance existing products.

It can change what products you build, which products you retire, how you package your services, how you price them, and even which customers you target.

For example, an AI capability might make an existing product significantly cheaper to operate. That could change its pricing model.

Another AI capability might automate a process that previously required a separate product. That could make part of the existing portfolio obsolete.

And sometimes AI creates an entirely new customer problem worth solving, leading to a product that didn’t exist before.

This is where AI becomes a portfolio strategy question, rather than a feature question.

The AI feature trap

The danger is not adding AI.

The danger is measuring progress by the number of AI features shipped.

A product with ten AI features is not necessarily more valuable than a product with one.

The important questions are:

• Does the feature solve a real customer problem?
• Does it create measurable value?
• Does it improve the product’s competitive position?
• Does it support the company’s strategic direction?
• Is the cost and risk justified by the value?
• Does it change the economics of the product?

If the answer to these questions is unclear, adding AI may simply be adding complexity.

From “AI everywhere” to “AI where it matters”

A mature AI strategy doesn’t try to put AI into everything.

It decides where AI matters most.

That means evaluating AI opportunities across the portfolio, not just at the individual feature level.

Some opportunities will deserve investment.

Some will remain experiments.

Some will be rejected.

And some existing products may need to be redesigned or retired because AI has fundamentally changed the market around them.

That’s the difference between adding AI to a product and using AI to shape a product portfolio.

AI is not a feature strategy.

It is a strategic capability that can change what your company builds, how it competes, and where it creates value.