80% of product orgs say they have an AI strategy. More than half admit it's informal. What's going on?

Hey community 👋

airfocus recently surveyed 500 product leaders across the US and the UK to understand how product teams are actually doing with AI: how it’s being used, where it's falling short, and what stands between AI adoption and true AI maturity. 

One finding stopped us in our tracks: 

• 71% say they would be less productive if AI was removed from their workflows tomorrow.
• 40% cite trust in AI outputs as their #1 blocker to scaling AI.

Product teams still don't trust AI, yet they already depend on it. How did we get here?

Product teams are past the "Should we use AI?" question. AI is already woven into how work gets done: analytics, user research, feedback synthesis. The harder question is: “Are we actually getting smarter with it?”

The survey shows a sharp tension around product teams’ strategic approach to AI.

• 80% of respondents say that their product organization has a clearly defined AI strategy.
• 57% also agreed that their AI strategy is only informal.

That's the crux of the maturity gap. Many teams have tools, experiments, and internal champions, but not yet a shared answer to the harder strategic questions: Where should AI make the biggest impact? Who owns its outputs? How do you know if it's improving decisions or just speeding up the same ones?

The pattern seems clear: Adoption is fast, but effectiveness is stalling. 

I’d love to hear where your team sits on this. 👇

• Has your org moved past informal AI use, or are you still working through what "strategic" actually means?
• What's your biggest blocker right now: trust, data quality, skills, or something else?
• Any wins (or hard-won lessons) from trying to scale AI across a product team?

We published the full findings in our new e-book, “The AI maturity gap: Are product teams keeping up?” I’ll share the link for those who are interested, but I’m mostly curious what's resonating (or not) from your own experience.

– Emma