A practical model for enterprise AI workflow governance

Start by classifying the steps

Before deciding what to automate, sort the steps in a workflow into three groups: those that must be deterministic, those that may reason, and those that require a named human before anything changes.

Most teams find the first group is larger than expected. State checks, policy comparisons, manifest validation and evidence collection all have correct answers and belong in scripts.

Decide what 'high-risk' means before you need the answer

Approval gates work only when the criteria are written down in advance. Useful thresholds tend to be concrete: anything touching a production scope, anything that mutates a customer-facing system, anything that spends beyond a limit, anything that cannot be reversed by re-running the workflow.

Vague criteria produce two failure modes: gates everywhere, which teams learn to click through without reading, or gates nowhere, which is how the first bad change reaches production.

Separate the approver from the requester

Self-approval is the most common gap in home-grown approval systems, because the person who built the workflow is usually the person testing it. Non-self approval should be configurable per approval type and enforced by the platform, not by convention.

Governance you can evidence

A governance model that cannot be demonstrated is a policy document. Each run should be able to show its trigger source, its effective subject, the authorization decisions that were made, the context it resolved, the tools it called, who approved and what it produced.

If assembling that story requires correlating five systems by timestamp, the model is not yet operational — regardless of what the policy says.