A leadership team has invested in AI.
Some employees are producing remarkable results.
Others have tried the same tools and quietly returned to their old ways of working.
Leadership begins asking familiar questions.
Do we need better AI training?
Should we standardize prompts?
Do we need different tools?
Would stricter policies improve consistency?
These are all reasonable questions.
They just aren’t where I’d begin.
At this point, I probably wouldn’t have recommendations.
I’d have questions.
Quite a few of them.
I’d want to understand what the people getting exceptional results are doing differently.
Not just which AI tools they’re using.
How do they think?
How do they approach problems?
How do they decide when AI is helpful — and when it isn’t?
Because I’ve noticed something.
The biggest difference is rarely the software.
It’s usually the judgment of the person using it.
Suppose several employees are consistently producing outstanding work with AI.
Now another question becomes interesting.
Can their way of working be understood?
Not copied mechanically, but understood deeply enough that others can learn from it.
Perhaps their prompts aren’t the important part.
Perhaps what matters is the sequence of questions they ask before writing a prompt.
Perhaps they understand the business unusually well.
Perhaps they’re exceptional at recognizing weak outputs and refining them.
Perhaps they naturally combine experience, judgment, and AI in ways they don’t even realize.
Those are the things I’d want to understand.
Because much of that can be taught.
At the same time, I’d become curious about the people who aren’t embracing AI.
Are they struggling with the tools themselves?
Or are they uncertain about when to trust them?
Do they worry AI will replace their work?
Have they been given permission to experiment — or do they feel pressure to get everything right immediately?
Sometimes what appears to be resistance is actually uncertainty.
And uncertainty deserves understanding before correction.
Only after developing a clearer picture would I begin suggesting experiments.
Perhaps some of your highest performers become mentors.
Perhaps AI workflows are documented — not as rigid procedures, but as examples of thoughtful decision-making.
Perhaps teams begin sharing not only successful outcomes, but how they arrived there.
Perhaps AI training shifts away from learning features toward developing better judgment.
The goal isn’t simply to make everyone use AI the same way.
It’s to help the organization become better at learning from its own successes.
Every organization is unique.
The situation you’ve just read isn’t a template.
It’s an example of how I approach complexity.
My goal isn’t to arrive with predetermined answers.
It’s to help your organization better understand itself before deciding what comes next.