Assess Whether a Business Process Is Ready for AI Assistance

A useful AI pilot begins with a specific decision or task, not a request to “add AI” everywhere. Choose work where a result can be evaluated and where errors have an understood consequence. NIST’s AI Risk Management Framework provides a general risk-management reference; applying it requires attention to the actual use case rather than treating the framework’s name as an approval stamp.

Choose the problem and compare a simpler baseline

Describe the current task, its owner, inputs, output and review effort. If the rules are explicit and stable, a conventional workflow or validation rule may be easier to test and maintain. AI assistance may be worth exploring for tasks such as preparing a draft summary for human review, but usefulness is a hypothesis to measure. Record the current process’s time, error patterns and quality criteria. A pilot should be compared with that baseline rather than only with the first impressive example.

Examine data and boundaries before connection

Identify the records the pilot needs, their quality and the authority to use them. Check missing fields, inconsistent labels and whether a sample represents the situations the system will encounter. Keep credentials and unnecessary personal or confidential information out of test prompts. Review supplier data handling, access, retention and permitted use before transmitting business material. A synthetic or appropriately protected sample can support early evaluation without assuming production data is necessary from the first day.

Evaluate representative and difficult cases

Prepare examples with expected review criteria before changing the model or prompt. Include ambiguous inputs, incomplete information and cases where the correct response is to ask for clarification or decline an action. Measure useful outcomes and the effort required to detect and correct mistakes. A fluent answer can still be inaccurate or unsupported. Keep the evaluation set separate from examples used to refine the approach, and document limitations so later users understand what the pilot has and has not demonstrated.

Assign responsibility and stopping conditions

Name the person who can accept the output and the actions the system may perform without further approval, if any. Begin with a bounded, reversible workflow and a manual alternative. Define conditions for suspension, such as unacceptable errors, unexpected data exposure or an inability to review results in time. Reassess after changes to the model, data or business process. Continue to responsible AI workflows, data privacy and approval workflows. Discuss a measurable pilot through Software Solutions with a clear decision boundary.