AI Strategy for Companies That Don't Have a Chief AI Officer
“Enterprise AI strategy” pulls about 720 searches a month, and almost everything written for that term assumes a dedicated AI team, a data science department, and a multi-year roadmap. If you’re running a business in Denver, Boulder, or Golden with no such department, here’s the version of AI strategy that actually applies to you.
The Wrong Starting Question
Most AI strategy content starts with “what’s your AI vision.” That’s backwards for a small or mid-size business. The right starting question is much narrower: what’s the most expensive, repetitive, checkable problem in your business right now?
Checkable matters more than it sounds like it should. If you can’t tell whether an AI system did the task correctly, you shouldn’t hand it that task yet, no matter how impressive the demo looked.
The Filter I Actually Use
When I’m deciding whether a task is a good first AI project, I run it through three questions:
- Is the outcome checkable? A call that gets answered, a lead that gets captured, a page that loads faster: yes. “Improve customer sentiment”: not checkable enough to start with.
- Is it expensive enough to matter? If fixing it saves ten minutes a month, it’s not worth the setup cost. If it’s costing real missed revenue (a missed call, a slow website losing search rank, hours of manual research), it’s worth it.
- Is the failure mode survivable? Start with tasks where a wrong output is embarrassing, not catastrophic. Save the high-stakes, low-margin-for-error tasks for after you’ve built trust in how the system behaves.
A task that clears all three is a real first project. A task that fails even one of them is a distraction dressed up as strategy.
Skip the Roadmap. Build the First Thing.
Big-company AI strategy documents love multi-year roadmaps. For a small business, that’s mostly theater: the AI landscape moves too fast for a 2026 roadmap to still be accurate by mid-2027, and a roadmap doesn’t produce any actual value sitting in a slide deck.
What I’d do instead: pick the single project that clears the three-question filter above, build it, measure whether it actually worked, and let that result inform the next one. That’s not the absence of strategy. It’s a strategy built on evidence instead of speculation.
Where Compliance Background Changes the Calculus
If your business touches sensitive data (health information, financial records, anything regulated), the three-question filter gets a fourth question: what does this system see, and does that access need to be there? I carry a HIPAA/compliance background from prior healthcare software work, and the habit that comes from it is scoping data access before scoping capability. A system that’s less capable but only sees what it needs beats a more capable one with broad, unexamined access every time.
The Takeaway
You don’t need a Chief AI Officer or a multi-year roadmap to have a real AI strategy. You need one good first project, checkable, expensive enough to matter, survivable if it’s wrong, built and measured before you commit to a second one. Evidence beats speculation, every time, at this scale.