Developing an AI Strategy: Use Cases, Rules, and Review

Developing an AI strategy means choosing where AI should help, setting the rules for its use, and deciding what evidence would justify expanding it. Start with one bounded work problem and a reviewed pilot, then make a deliberate decision to continue, revise, or stop.

A useful strategy gives the team a shared starting point. Tools like fenbs keep the approved pilot decisions, tasks, and project context available to each connected assistant, so a change of chat does not erase the plan.

What are the first steps?

1. Describe one recurring problem and how the team handles it today. Name a person responsible for improving that work.
2. Choose a small use case with an observable result. Specify the work AI may draft or analyze and what remains outside the pilot.
3. Agree on approved inputs, human review, and stop conditions before using real work. Involve the people who will do the checks.
4. Run the pilot against a documented baseline. Measure the whole task, including preparation, corrections, and review.
5. Have the accountable person decide whether to expand, revise, or stop. Save the evidence and reasons behind that decision.

Say a small distributor wants clearer product descriptions. A bounded pilot might draft descriptions from approved manufacturer specifications for one product family. Automatically publishing those drafts or letting the assistant invent missing specifications stays outside the scope.

How do you choose a useful first use case?

Look for work that is frequent enough to evaluate, constrained enough to explain, and easy for a knowledgeable person to check. A bottleneck the team already understands is a better starting point than a broad goal such as becoming an AI business. Ask the people doing the work where incomplete information or repetitive preparation costs them effort.

Compare the proposed approach with simpler fixes. If a clearer intake form removes the problem, you may not need AI for that task. Include the current process as an option so the pilot has to demonstrate a useful improvement rather than winning by default.

For a broader view of the choices before implementation, see AI strategy for your team. This pilot plan focuses on turning a selected opportunity into a bounded test.

Which rules need agreement before the pilot?

Write down what information the tool may receive, which actions require approval, and who can change the scope. Check the selected vendor's current terms and controls directly before choosing it. Do not assume a personal account and a company-approved setup have the same data arrangements.

The NIST AI Risk Management Framework↗ (opens in a new tab) offers a voluntary reference for evaluating AI risks. Use it to inform the pilot boundaries; adopting a framework does not itself make the work safe or compliant.

Turn a general value into an instruction someone can follow. For the distributor, accuracy could mean that every product claim must match an approved specification. A useful stop condition is equally concrete: if a source conflicts with another approved source, hold the draft for the product specialist.

Keep these human-approved instructions in Decisions and rules, with their scope and reason. In fenbs, rules appear first in the context an assistant is asked to read; someone must still check that the work follows them.

Who needs to participate in the evaluation?

Include the person doing the task, the person reviewing it, and the person authorized to approve changes. Bring in privacy, security, legal, or other specialists when the data or consequences require their expertise. Give each participant a specific question to answer rather than a vague request for feedback.

The GAO AI accountability framework↗ (opens in a new tab) highlights governance, data, performance, and monitoring. Those areas help you divide responsibility: an owner sets the goal, an input reviewer checks sources, a work reviewer checks results, and someone watches what happens after adoption.

Ask whether the pilot simply transfers effort to someone else. A fast first draft is not a team improvement if the specialist spends longer correcting it. Let reviewers report repeated failures without having to argue that the whole strategy is wrong.

What should the pilot measure?

Choose measures before seeing the result. Compare completion time, correction effort, factual errors, and usefulness to the people receiving the work. Keep the sample representative: include awkward source material and unusual requests, not just examples selected because they are easy.

Keep the test material and evaluation notes in an approved location. Record what was checked, who checked it, and what remains uncertain. Distinguish an accepted draft from a result that was never reviewed; otherwise unfinished checks can disappear into a positive summary.

Set the decision rule in advance. The distributor might require that all published claims are verified and that total preparation plus review effort is acceptable to the team. These are hypothetical criteria to adapt, not evidence that a particular tool will meet them.

The guide to using AI at work offers more everyday starting points. Keep this evaluation specific to your chosen task instead of treating another team's success as proof for your own.

How to do this with fenbs

1. Create a free account and add a project for the pilot. One-person use is free, with no credit card; colleagues share work through a team kanban board.
2. Add the pilot tasks and their plans. Include the baseline check, input review, output review, and the final evaluation.
3. Record the choices people approve in Decisions and rules. Give each rule a clear project scope and keep unanswered approval questions open.
4. Connect a supported AI app from Integrations. Ask it to read the project context, then save Where we left off with completed checks, unresolved questions, and the next action.
5. Record the human decision to expand, revise, or stop. Add lessons from the pilot so future work can use what the team learned.

fenbs keeps the project record and decisions; it does not run model evaluations or enforce your organization's data controls. Use specialist systems when you need those capabilities, and keep restricted evaluation material in its approved home.

What changes when the pilot becomes routine work?

Approve expansion as another decision, with new boundaries and a named owner. Check whether more users, different inputs, or less review would change the risk. A successful narrow pilot does not justify automatic use across unrelated tasks.

Keep the useful context in an AI brain and revisit it when the workflow changes. The AI governance hub covers the review practices that help a strategy survive ordinary team turnover and new tools.