What use should be allowed by default?
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AI Governance Starter
How do you encourage people to use AI without creating a governance mess?
A lightweight AI governance framework focused on practical behaviour, responsibility and human oversight.
DOES THIS SOUND FAMILIAR?
Start with what is actually happening.
- Users do not know what they can share with AI tools.
- Different teams interpret policy differently.
- Fear blocks experimentation in some areas while experimentation runs ahead of controls in others.
- Nobody clearly owns AI-enabled processes after a pilot succeeds.
WHAT WE NEED TO ANSWER
Good work starts with better questions.
What requires additional review?
Where is human oversight mandatory?
Who owns the outcome of an AI-enabled process?
How do rules remain understandable enough to influence behaviour?
WHAT WE DEFINE
The scope follows the problem.
I do not force every engagement through the same checklist. These are the areas that may matter; the agreed scope determines how deep we go.
Acceptable-use principles
Data and sensitivity considerations
Risk categories
Human-in-the-loop expectations
Ownership and escalation
Verification responsibilities
Experimentation boundaries
User guidance and communication
WHAT YOU GET
Something you can use after the conversation ends.
The output should help you make a decision, change a practice or move the work forward. It should not exist simply to prove that work happened.
AI Use Principles
A small set of clear principles users can remember and apply.
Risk Categories
A practical distinction between lower- and higher-risk use.
Human Oversight Guidance
Where review, approval or judgement must remain explicit.
Ownership Model
Who is responsible for AI-enabled outputs and processes.
User Checklist
A practical aid for everyday decisions.
HOW IT WORKS
Enough structure to make the work clear. Not so much process that the process becomes the work.
Context
We start with a short conversation about what is happening, what matters and what has already been tried.
Review
I analyse the relevant parts of the solution or operating model at the depth agreed for the engagement.
Prioritise
I organise observations around impact, risk and usefulness — not around how many issues I can find.
Walkthrough
We go through the findings together, challenge assumptions and discuss trade-offs.
Next step
You decide what happens next: implement internally, continue together or stop because the focused engagement was enough.
YOUR TIME COMMITMENT
I keep the client-side time demand deliberately light. A focused assessment usually needs a kickoff conversation, access or materials, and a final review session. We agree the exact involvement before we start.
A GOOD FIT WHEN
This is likely to help if...
- AI usage is expanding and informal rules are no longer enough.
- You want governance that enables sensible experimentation instead of stopping it.
- You need policy translated into user behaviour.
IT MAY NOT BE THE RIGHT FIT IF
- You need formal legal advice or regulatory certification.
- You need a full enterprise risk-management programme across all AI systems.
If the problem is real but the format is wrong, that is useful to know early. I would rather redirect the conversation than force the challenge into the wrong service.
AI GOVERNANCE STARTER
How do you encourage people to use AI without creating a governance mess?
Bring me the problem, not a polished brief. A few sentences about what is happening and what you would like to change are enough to start.
Tell me what's happeningPREFER TO WRITE?
Send me a message.
You do not need a polished brief. Whether the topic is Power BI, Fabric, AI, team training, mentoring, coaching or simply a challenge that needs untangling, leave a few details and we can start from there.
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