Before buying AI tools, write the operating model

AI adoption usually becomes messy before it becomes strategic. One team buys a writing tool, another tests an internal assistant, a third uploads sensitive spreadsheets into a model, and no one can answer which use cases are approved.

An operating model prevents that drift. It does not need to be heavy. It needs to define ownership, review, risk classification, and the minimum evidence required before a workflow becomes normal business process.

The NIST AI Risk Management Framework is a useful reference because it treats AI risk as something organizations map, measure, manage, and govern over time NIST AI RMF.

Use this when

Use this when more than one team is experimenting with AI and leadership needs a shared way to approve, support, and retire use cases.

It is especially useful for operations, enablement, and technology leaders who need practical coordination before a full governance program exists.

Skip it when

Do not build a company-wide operating model for one narrow experiment. If a small team is testing a low-risk workflow with public data, a project checklist may be enough.

Do not use this as a substitute for legal, security, procurement, or compliance review when the workflow affects regulated data, employees, customers, medical decisions, legal advice, financial eligibility, or safety-critical work.

What to do

1. Name a single accountable owner for AI adoption.
2. Maintain a visible inventory of active and proposed AI workflows.
3. Classify each workflow by data sensitivity, business impact, reversibility, and human review.
4. Define which workflows can launch locally and which need security, legal, or executive review.
5. Require evidence before scaling: examples, failure cases, reviewer notes, and operating limits.
6. Revisit approved workflows on a schedule because tools, policies, and risks change.

Watch the boring risks

The biggest early risk is not model quality. It is unclear responsibility. If no one owns the workflow, no one owns the data exposure, incorrect outputs, access control, or vendor review.

Before approving a workflow, check whether the tool stores prompts, uses customer data for training, exposes logs to administrators, supports access controls, and allows data deletion or export.

Other ways to handle it

For a very small company, use a lightweight approval checklist and a shared spreadsheet.

For a regulated organization, treat this operating model as intake only. The actual approval path should connect to established risk, privacy, legal, procurement, and security processes.

Try this next

Create a one-page AI workflow inventory with these fields: workflow name, owner, input data, output audience, tool, risk level, review status, and next review date.