AI Governance for Mid-Market Companies: The Minimum Viable Framework

AI governance for a mid-market company is five concrete controls — action boundaries, human-in-the-loop tiers, audit logging, vendor risk basics, and an incident playbook — not the hundred-page policy documents enterprise governance programs produce. The minimum viable version protects you just as well and actually gets adopted.

Enterprise AI governance frameworks are frequently theater: comprehensive on paper, ignored in practice because nobody at a 400-person company has the headcount to run them. This is the version sized for teams that actually need to ship.

What may an agent do without a human? (Action boundaries)

Write down, per agent, an explicit allowlist of actions it can take autonomously versus actions that require sign-off. "Send an email to an internal address" and "update a status field" are common autonomous-tier actions; "send an email to a customer," "issue a refund," or "modify a production record outside its normal workflow" are common approval-tier actions. The boundary doesn't need to be exhaustive on day one — it needs to exist and be reviewed as the agent's scope grows.

How much human oversight does each action actually need? (Human-in-the-loop tiers)

Not every action needs the same oversight level. A useful three-tier model:

1. Tier 1 — fully autonomous. Low-risk, easily reversible, high-confidence actions (e.g., categorizing an inbound email).
2. Tier 2 — human review before execution. Medium-risk or lower-confidence actions (e.g., drafting a customer-facing response, held for a human to approve or edit before sending).
3. Tier 3 — human executes, agent only recommends. High-risk or high-uncertainty actions (e.g., anything touching money movement or legal exposure).

Most mid-market pilots should start almost entirely in tiers 2–3 and earn their way into tier 1 as track record accumulates — not the reverse.

What should you actually log, and for how long? (Audit logging)

At minimum: every tool call the agent makes, the reasoning that led to it (even a short rationale string), the outcome, and — for tier 2/3 actions — who approved it and when. Retain logs at least as long as your existing record-retention policy requires for the underlying business process (e.g., financial records typically already have a retention requirement — apply the same one here rather than inventing a separate AI-specific schedule).

How do you assess model and vendor risk without a dedicated risk team?

Three questions, asked once per vendor/model and revisited annually: What happens if this vendor changes pricing or deprecates this model? (do you have a fallback path). Where does our data go, and who can see it? (read the vendor's data-handling terms, don't assume). What's the vendor's own security posture? (SOC 2 or equivalent, at minimum, for anything touching sensitive data). This isn't a full vendor-risk-management program — it's the three questions that catch the failure modes that actually happen.

What does an incident playbook need to cover?

A one-page playbook, not a binder: detection (how do you find out something went wrong — monitoring, a customer complaint, an internal report), containment (how do you pause the agent immediately — this only works if you built the kill switch before you needed it), assessment (what did it actually do, using the audit log), and communication (who gets told, internally and externally, and on what timeline). Write this before your first agent goes to production, not after your first incident.

A one-page policy template outline

For a policy document a non-technical exec could actually read: (1) which agents exist and what they're for, one line each; (2) the action-boundary table per agent; (3) the human-in-the-loop tier assignments; (4) where logs live and who can access them; (5) the incident contact and escalation path. Five sections, one page, reviewed quarterly.

Does the EU AI Act apply to us?

For most mid-market companies outside the EU or without EU customers, direct applicability is limited — but if you serve EU customers or operate in the EU, the Act's risk-tiering approach is worth understanding directionally: it classifies AI systems by risk level, with the heaviest obligations falling on "high-risk" categories (things like employment decisions, credit scoring, biometric identification) rather than general business-process automation like the use cases in this guide. Most invoice-exception or order-triage agents don't fall into high-risk categories, but this is exactly the kind of determination worth a real legal read rather than a blog post's guess — treat this section as an orientation, not compliance advice, and check current enforcement dates against your specific use case.

How does this connect back to readiness?

Governance readiness is one of the five pillars in our AI readiness checklist — points 21 through 25 map directly onto the five controls above. If your organization scores low there, it's a fixable gap, not a reason to avoid agentic automation altogether; our AI readiness assessment builds exactly this framework as part of its roadmap deliverable.