Useful shortcuts are happening, but leadership cannot clearly see where.
Offer 3 / AI Amnesty Review
Your team is probably already using AI. The question is whether you trust how it is happening.
For businesses using ChatGPT or similar tools in awkward, unofficial, or fragile ways that want a no-blame cleanup plan without killing the productivity gains.
Unofficial usage
Prompt habits
Source data exposure
Review gaps
Output dependency
Keep / fix / stop
Risk
The problem
What awkward AI adoption usually looks like
People are pasting real work into AI tools without much structure or review.
Prompt habits, copied outputs, and hidden dependencies are piling up.
The workflow works just enough that nobody wants to touch it until something breaks.
You want cleanup and visibility, not a panic ban or trend-chasing AI theater.
The mechanism
Preserve the upside. Remove the weird fragility.
Map the AI workflows your team is already using, identify the biggest practical risks, and give clear keep / fix / stop guidance so useful adoption survives without relying on brittle habits.
Find live AI usage.
Sort keep / fix / stop.
Reduce risky dependencies.
Set the next operating rules.
What gets reviewed
Inspect the actual usage path, not the AI policy fantasy version.
Deliverables
What you get back
Where AI is actually in play.
Practical triage.
Fit
Good fit
- Your team is already using AI in real work.
- You want to keep the useful parts without ignoring risk.
- You would rather clean things up now than discover the problem later.
Not fit
Wrong expectation
- You want legal or regulatory counsel disguised as workflow consulting.
- Nobody is actually using AI yet.
- You only want hype, not cleanup or process discipline.