Key takeaways
- Choose one business task and define what a useful result looks like.
- Check information quality, access permissions and review responsibilities before a pilot.
- Evaluate the complete workflow, including corrections, cost and operational support.
Choose a specific task rather than a broad ambition
“Use AI in our business” is a direction, but it is not a project brief. Start with a task that people can describe and evaluate. Examples include drafting replies from approved support material, extracting selected fields from incoming documents or helping staff find relevant internal guidance.
Choose a task with a clear owner, repeatable inputs and an understandable outcome. Ask the people doing the work where time is spent, what creates rework and which decisions need judgement. Sometimes a better form, search function or rules-based automation is enough. The first discussion should establish what the business needs before deciding whether AI is the right component.
Understand the process you want to improve
Write down the current steps, including what happens before and after the proposed AI task. For a support reply assistant, the process might include reading the enquiry, finding the relevant policy, drafting a response, checking it and sending it. Improving only the draft may have limited value if finding the correct information remains difficult.
Use a small set of representative examples to establish a starting point. Note the time required, common corrections and how people judge an acceptable result. Include ordinary difficult cases, such as incomplete requests or documents with inconsistent formats. This gives the pilot something concrete to improve upon and prevents a polished demonstration from becoming the only measure of progress.
Check the information the system will use
Identify the documents, records or examples needed for the task. Check whether they are current, readable, consistent and available to the people who will use the system. If two documents give different answers, decide who can resolve that conflict. AI does not remove the need for clear ownership of business information.
Define the access boundaries before connecting real sources. Which users can see which records? What information can be sent to an external service? What should be retained, and for how long? Start a prototype with suitable test material where possible. Move to operational information only after the relevant access, privacy and supplier arrangements have been reviewed.
Agree how you will judge the results
Set evaluation criteria before choosing a preferred demonstration. For document extraction, you might review whether each required field is correctly identified and whether missing information is flagged. For an internal assistant, you might assess whether the response addresses the question, uses approved information and makes its sources available for review.
Build a test set with examples that are separate from those used while developing the solution. Include unclear requests and cases that should be handed to a person. Record the types of failure, not just a single overall score. A system that handles common cases well may still need a different design for exceptions. The review should make those limits visible.
Design the human review and fallback
Decide what the system may suggest, what a person must approve and what it must never do automatically. A first support pilot could prepare a draft for an employee to review, leaving the final response under their control. A document assistant could flag uncertain fields instead of silently completing them.
Make the review practical. Show the original information beside the proposed result, make corrections straightforward and provide a clear route back to the normal process. Name the person responsible for handling failures. NIST’s AI Risk Management Framework provides a useful broader reference for considering the design, use and evaluation of AI systems; it does not replace decisions about your particular workflow.
Run a contained pilot with real evaluation
Limit the pilot by task, user group and information source. Agree its duration, review points and exit criteria. Give participants a short explanation of what the system can do, how to check its output and how to report a problem. Record both successful examples and corrections.
Measure the complete process, including review time, repeated attempts and operational costs. Ask whether the result is useful enough to change how the team works. A good pilot may support expansion, suggest a narrower use case or show that another solution is more suitable. Treat all three as valid outcomes when they help the business make a better decision.
Plan the work needed after the pilot
A successful trial still needs a plan for everyday use. Confirm responsibility for updating source material, managing access, checking quality, supporting users and reviewing costs. Decide how the team will evaluate changes to the model, prompts, integrations or business process before wider release.
- Who owns the business outcome and the information?
- Which results need approval or further review?
- How will issues and corrections be recorded?
- What would trigger a pause or a return to the previous process?
- What evidence is needed before expanding the scope?
A useful first AI project leaves you with a clearer workflow and evidence about where the technology helps. That is a stronger foundation for the next investment than starting with a large list of features.
