Before you buy a platform, choose one workflow.

Use this guide to: plan a bounded AI workflow and measure the accepted result.

Turn an ambition into a test

An instruction to adopt AI is too broad to evaluate. A bounded question is more useful: can a specified workflow reduce the time needed to produce an accepted result, without increasing a defined category of error? Name the person who owns the process and the person who can approve a change.

Choose a task with available inputs, a repeatable output and an observable completion rule. Avoid beginning with the most consequential action in the organization. A pilot should make learning affordable and the results interpretable. That is an operating choice, rather than a promise that any particular task is safe to automate.

Measure the present process

Capture the current time, cost and error handling before introducing the new system. Distinguish active work from waiting time. Record how exceptions are resolved and which cases are sent back. A credible baseline includes difficult inputs, not only the examples that are easy to demonstrate.

Keep an evaluation set separate from the examples used to adjust the workflow. Give both the current process and the candidate process comparable tasks. If reviewers know which system produced an answer, record that limitation rather than treating the assessment as blind.

Define the operating boundary

Write down what information the workflow can access, what it can produce and which actions require approval. A drafting assistant, a recommendation tool and an agent with write access create different operating commitments. Start with the permissions needed for the test and identify who can stop it.

NIST's voluntary AI Risk Management Framework is a useful reference for thinking about AI risk management. Our suggested pilot checklist translates that broad concern into a specific operational question: which failures are acceptable for the experiment, and what prevents the others from reaching a real customer or system?

Agree on the exit criteria

Before the pilot starts, choose the evidence required to continue, revise or stop. Examples include accepted-output quality, review time, exception frequency and total delivery cost. Set the relevant thresholds with the process owner. There is no universal threshold that makes every deployment commercially or operationally sound.

Also define the comparison period and what constitutes enough representative evidence. A handful of impressive outputs is a demonstration. A pilot should reveal the distribution of results, the costs of difficult cases and the work required to keep the process functioning.

Make the next commitment proportional

If the evidence supports expansion, enlarge one dimension at a time: more users, a broader input set or a new permission. Keep a record of changes and repeat the evaluation. If it does not, preserve the finding. Discovering that a proposed workflow requires too much review can be a valuable result before a larger commitment is made.