The key idea
Governance makes it explicit who may use which tools and data, who approves outputs and what happens when something goes wrong.
Our method
Classify use cases by consequence, document approved inputs and assign a responsible owner. Set review rules for claims, customer communication and publishing. Establish a reporting path for errors and keep the operating guide understandable to the people using it.
How to assess progress
Review whether the rules are followed in actual work. This operational framework is not legal advice or a compliance certification; specific obligations need appropriate professional review.
Decision
Decide who can approve each consequence of an AI-assisted workflow: using a data source, accepting a factual claim, publishing material or contacting a customer. Assign responsibilities to actual roles in the team. A broad statement that someone should review everything does not explain who acts when work is urgent.
Exercise
Walk through three scenarios with the people who operate the process: an inaccurate draft caught before publication, a mistaken customer message already sent and a source file exposed to the wrong audience. Ask each participant what they would stop, preserve, correct and report. Record unclear ownership and conflicting instructions as operating gaps.
Deliverable
Produce a one-page responsibility map with permitted uses, data boundaries, approval points, incident contact and recovery actions. Attach a short use-case register naming the owner and last review date for each active workflow. Make the guide accessible from the places where colleagues prepare and approve work.
Pitfall
Policies that ignore normal working pressure tend to be bypassed. Test whether a colleague can find the correct route in a few minutes and whether an alternate owner exists during absence. Simplify redundant reviews while keeping responsibility for consequential actions clear and documented.
Demos
Four fictional documents support answerable, unknown and conflicting-information test cases.
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