The Hidden Intelligence in Scattered AI Use
Your team is all over the place with AI. They’re scattered across curiosity, caution, avoidance, and quiet experimentation.
This variation feels messy, but it contains real strategic signal.
Building AI capability isn't like rolling out a new CRM or similar business technologies. AI is a fundamentally different animal - multipurpose tools that anyone can access, capabilities evolving at a dizzying rate, use cases that blur personal and professional, and a moving landscape of what's even possible. Each person is on their own AI journey that intersects with your organizational strategy but isn't contained by it.
This can’t be controlled the way other technology initiatives have been. Some organizations still try - I heard from someone just last week whose company (remarkably) has a blanket "no AI use" policy. Others mandate "only use Copilot" despite low adoption and user frustration. Both approaches miss the opportunity.
The question isn't how to control AI use. It's how to learn from what's already happening. That's AI on purpose.
The Questions That Matter
This isn’t about assessing ‘AI maturity’ against some external benchmark. You're trying to understand your actual terrain - what's working, what's not, and why.
Where is value already appearing? Who’s using AI in a way that genuinely helps them do better work? Not the people who talk about AI enthusiastically, but the ones who’ve quietly integrated it into their flow. What tasks have changed, and why do those work when others don’t?
What's creating friction? Where are people trying AI and hitting walls? What are the actual sticking points - the tool itself, not knowing what to ask, results that don't fit their needs, workflow disruption? The specifics matter here.
What patterns exist in who's succeeding? Look across people using AI effectively. Do they share certain work characteristics or roles? Are they generally early adopters, or is something about AI specifically clicking for them? What does this tell you about where AI fits naturally in your organization versus where you're forcing it?
What are people avoiding and why? When people avoid AI, they have reasons - quality concerns, worries about their role, past bad experiences with new tools, or simply not seeing how it fits their work. What are those reasons telling you about implementation challenges you'll need to address?
What's happening in the shadows? Who's using personal AI tools instead of company-provided options? For what tasks? What gap does this reveal between what's 'available' and what's actually useful to your people?
What the Patterns Tell You
The answers won't give you clean adoption curves from vendor presentations. They'll be messy, context-specific, sometimes contradictory. That's exactly what makes them valuable.
You might discover your most effective AI users aren't the technically sophisticated people you expected. You might find certain types of work lend themselves to AI assistance while others don't, regardless of individual enthusiasm. You might realize your approved tools are failing not because people resist change, but because they genuinely don't work as well as alternatives for specific use cases.
This intelligence shows you where to focus your energy. It reveals which early wins to amplify and which approaches aren't worth forcing. It tells you who your natural change agents are and what obstacles you'll need to address.
Where This Takes You
Understanding where your team actually is with AI gives you the foundation to build capability that works with your organization’s reality.
The scattered AI use you’re seeing isn’t something to clear away before your ‘real’ strategy can begin. It’s the raw material for a strategy that fits your people, your work, and your context.
