The Smallest Useful AI Control
A lot of AI governance work starts too high above the business. The policy gets updated, the acceptable-use note is circulated, someone adds a training module, and the vendor questionnaire grows by a few pages. None of that is wrong. It just may not answer the question a risk lead will ask once AI is inside real work.
Where did AI touch the workflow, and what proof do we have?
That is the question I would build around because it is specific enough to test. It moves the conversation away from AI as a broad category and toward the business process that now depends on it.
The smallest useful artifact is one row: one AI use case, one workflow, one owner, and one evidence trail. It sounds small, but that is the point. A large framework can stay abstract for months. A row has to name the work and make the dependency visible.
It has to say whether AI is being used for a client email, a complaint summary, a legal first pass, a credit memo, a vendor review, a call summary, a board note, or a marketing claim. Once the workflow is named, people can stop arguing about AI in general and start reviewing the actual dependency.
The row I would use is simple enough to put in front of a COO and specific enough for compliance to test.
FieldQuestion it answersWorkflowWhere does AI touch the work?OwnerWho owns the final judgment?Data classWhat information can enter?Approved pathWhich tool, model, workspace, or vendor route is allowed?EvidenceWhat log, approval, sample, or review artifact is kept?FallbackWhat happens if access changes?
That is enough to begin. It is not enough to finish the whole program, but it is enough to show whether the firm has an operating view of AI usage or just a list of approved tools.
Why this works better than another policy paragraph: a policy can say "human in the loop." The row has to name the human, the point where judgment enters, and the proof that remains after review. A policy can say "use approved tools." The row has to name the route approved for this workflow. A policy can say "do not enter sensitive data." The row has to name the data class the workflow actually handles.
That is where privacy becomes practical. Public research, customer records, legal advice, health data, source code, pricing files, and regulated records should not be treated as one generic prompt-risk bucket. One workflow may be fine in public AI. Another may need private cloud controls. A third may need local processing or an isolated offline route. The row gives the team language for those differences.
The fallback field tells you when AI is becoming operational. The easiest field to skip is fallback, and it is also the field I would check first. Ask one question: "What happens if this AI path changes tomorrow?"
If the honest answer is "nothing," the use case may still be experimental. If the answer is "manual review doubles," "client turnaround slows," "the SLA breaks," or "a manager has to approve an exception," the workflow is already becoming dependent on AI.
That dependency is not bad by itself. It just needs to be visible before the process becomes too useful to unwind. This matters even more when AI sits inside vendor products. A vendor can change the model, region, retention rule, logging path, or feature behavior after the original approval. The control row should connect that vendor answer back to the workflow that depends on it.
Otherwise the vendor file can look complete while the real business dependency sits somewhere else.
A practical first pass: I would not ask for every AI use case in the firm. I would ask for the 10 workflows people already rely on. The real work, not the shiny demos. The places where someone would feel pain if the AI path disappeared next week.
Then I would fill the row for each one. The first pass will probably be imperfect, and that is fine. The goal is not to produce a beautiful register on day one. The goal is to reveal which workflows need ownership, evidence, and fallback thinking before they scale.
Once those 10 rows exist, leadership can have a better conversation. They can see which workflows are still experimental, which ones are becoming operational, and which ones need better evidence before they are allowed to grow. The discussion becomes less about whether the firm is "using AI" and more about where AI has become part of the operating model.
That distinction matters. Adoption sounds like a technology story. Dependency is an operating story, and operating stories need owners, evidence, and fallback plans.
Where this helps in practice: the row gives each stakeholder a different job. The workflow owner explains how the work actually happens. Compliance checks whether the evidence is reviewable. IT checks whether the approved route is real. Legal checks whether the data class creates a different obligation. Operations checks whether the fallback would work on a busy day, not just in a policy workshop.
It also helps leadership see the difference between adoption and dependency. A team may be experimenting with AI in one workflow and quietly relying on it in another. Those should not be governed the same way. The row makes that distinction visible because the fallback field forces people to say what breaks if the AI path changes.
I would keep the register small at first. Ten rows is enough to learn where the firm has real exposure. After that, the team can decide which rows need stronger controls, which rows can stay lightweight, and which rows should be paused until ownership or evidence is clearer. That is a much better first month than trying to design the perfect AI governance framework before anyone has mapped the work.
P.S. If I were starting next week, I would begin with this request: "Show me the 10 workflows people already rely on, and tell me what breaks if the AI path changes tomorrow."