The word «governance» lands badly in mid-sized companies, and the reaction is rational: images of committees, approval chains, policy binders — a body built for ten thousand employees, bolted onto three hundred. The result of the allergy is predictable and worse: no governance at all, which means AI arrives through individual initiative, unrecorded, with confidential documents pasted into whatever tool each employee preferred that week. The failure mode is not the committee. The failure mode is the vacuum. And a vacuum can be filled with something much lighter than the corporate binder.
Strip the enterprise theater away, and AI governance answers four questions. What may go where: which categories of data and documents are permitted in which tools — the one rule that prevents the expensive class of accidents. Where AI acts alone versus advises: which decisions may be produced by a model with a human acceptor, and which require a human making the judgment with AI as input — the line between assistance and delegation. Who owns each use: every live use case has a name attached, someone who knows what it does, what it costs and what it gets wrong. What gets reviewed, when: a cadence — light, but existing — at which the use list, the rules and the incidents are glanced at before they become archaeology.
Each answer should fit on a page, not in a binder. The data rule is a short list of categories, in the language employees actually use — «client contracts», «personal data», «board materials» — not the language of the security standard. The alone-versus-advises line is drawn for the company's real decisions: pricing approvals, client communications, hiring, financial reporting. The owner is a person, not a committee — often the same person who owns the AI strategy, because governance detached from strategy calcifies into ritual. The review is a short recurring meeting with three items: what new uses appeared, what went wrong, what changed in the tools themselves. That is the whole apparatus. It can be built in days, and it costs almost nothing to run.
There is a practical argument beyond the cost saving: heavy governance fails in the mid-market even when installed, because rules nobody can remember get routed around, and the shadow usage the binder was meant to prevent simply moves further into shadow. A short list of rules that every employee actually knows outperforms a comprehensive policy that the same employees have never read — the same adoption law that governs why AI tools end up idle applies to the rules about those tools. And the register of use cases earns its keep in the second way: it is the map an owner reads to ask where value is appearing and where it is not, which makes governance and opportunity assessment the same document seen from two directions.
Governance in a mid-sized company is not a binder. It is a short list of rules everyone knows, an owner's name on every use, and a calendar entry. Everything heavier belongs to a company that isn't yours yet.
Building the light-governance set — data categories, the delegation line, the use register, the review cadence — is standard work in the AI transformation practice, usually done alongside the first serious use cases rather than after the first serious incident, which is the alternative schedule.
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