'AI Everything' Is Not a Strategy

I sat in a strategy meeting last month where a leadership team spent 90 minutes debating where to "add AI." Not what problem AI would solve. Not why customers would care. Just where to put it.

By the end, every single box on their roadmap had an AI label slapped on it. AI search. AI recommendations. AI reporting. AI auto-complete. It looked less like strategy and more like they'd found a stamp.

The kicker? Their product still took 40 seconds to load a basic dataset. But sure, let's add AI.

The Cargo Cult of Innovation

Here's what's actually happening: leadership went to a conference, came back terrified of disruption, and decided that AI is the moat. So they greenlighted every AI proposal. Not based on merit. Based on fear.

This is cargo cult product development. You build the ceremonial runway hoping the planes come back. But the planes were never about the runway.

I watched a team spend four months building an "AI-powered insights engine" that was essentially k-means clustering with a GPT wrapper. The problem it was supposed to solve? Users had too much noise in their reports. The actual solution? A filter toggle. Cost $0. Took a day to ship.

But the AI version? That got announced. That got into the investor deck. That justified the headcount.

Nobody asked if it worked better. Because admitting that a toggle was the better solution would've meant admitting the strategy was emotional, not rational.

The Real Pattern

Every company I've seen that successfully used AI did one thing first: they found a specific, painful problem that AI actually solved better than alternatives.

Slack's search got smarter because people have mountains of messages and the old search was objectively terrible. That's not "we have AI now, let's search." That's "search is broken, and here's a specific way AI fixes it."

The companies failing? They're working backward. They have AI, so they're searching for problems it might solve. They're creating features nobody asked for, dressed up in neural networks.

The Cost of Vaporware Strategy

Here's what happens next:

You announce AI features. You allocate engineering time. You ship something that's technically an LLM but doesn't actually move the needle. Your customers don't use it. You quietly sunset it in the next release. Your engineering team is demoralized because they spent months on something that mattered to nobody.

I watched a CRM add "AI email drafting" because everyone else did. They spent two sprints on it. Three months later, 0.3% of users had tried it. They were paying $40k/month for API calls. The math was broken.

But admitting that meant admitting the strategy was wrong. So they just let it bleed.

The Uncomfortable Question

Before you add AI to anything, ask yourself: could I solve this faster with a human, a database query, or basic statistics?

If the answer is yes, do that instead.

I'm not anti-AI. I'm anti-theater. And right now, most "AI strategies" are pure theater.

The companies that are actually winning with AI aren't the ones with the most AI announcements. They're the ones that solved specific problems where AI was the only reasonable tool. They're the ones that measured whether it worked. They're the ones that killed it if it didn't.

What Actual Strategy Looks Like

Real AI strategy starts with a customer problem. Not with "how do we use AI?" but with "where are our users stuck, and could AI materially improve their situation?"

Then you validate. You measure. You compare against the non-AI baseline.

If AI wins, you ship. If it doesn't, you shelve it. No ego. No conference deck. Just results.

Most 2026 roadmaps aren't strategy. They're panic dressed up as innovation. And your customers can tell.

Stop adding AI to everything. Start solving real problems with the right tool—whether that's AI or a database index or a delete button.

That's how you actually win.