Insights Data and AI
Your first AI project needs a measurable outcome
A useful first AI project has a specific job to improve, a way to check its output and a business owner who can judge the result. Those decisions give a pilot a purpose before anyone chooses a model or builds an integration.
Consider a customer service team preparing replies. Staff read the request, find the relevant records, draft a response and check it before sending. An AI tool might help prepare that draft, provided it can use the right information and leave uncertain points visible.
The trial should establish whether the complete task becomes easier. A faster draft has little value if the reviewer spends longer finding and correcting its mistakes.
Select work that can be evaluated
Look for a recurring task with enough examples to compare. Drafting service replies, summarising approved documents or extracting fields for verification may be useful candidates, depending on the information involved.
Define what a satisfactory result contains. For a service reply, that might mean the correct customer context, an answer supported by current policy and a clear indication when information is missing.
Also define what the tool must leave alone. During an initial drafting trial, it may have no permission to send messages or change a customer record. That keeps the experiment focused and makes its behaviour easier to assess.
An existing review step is helpful, but it does not make a task safe by itself. The reviewer needs relevant knowledge, access to the source material and time to check the answer properly.
Measure the current task before changing it
Observe how people complete the work now. Include the time spent finding information, checking details and correcting errors, as well as producing the first version.
Choose a primary measure that matters to the process owner. It might be handling time, turnaround time or the proportion of work that needs correction. Keep a quality measure alongside it so that speed cannot conceal a worse result.
Use comparable work for the baseline and the trial. A pilot tested only on short, complete enquiries will give a misleading picture if normal work includes missing records and ambiguous requests.
Record how the team uses any time saved. It may mean shorter queues, more time with customers or room to handle additional demand.
Give the pilot clear limits
Agree which information the tool can read, where it can write and who can approve its use. Confirm that the chosen environment is suitable for the data involved before real records enter the trial.
Include examples with conflicting information, outdated documents and requests outside the agreed scope. Check whether the tool identifies uncertainty or produces an answer that looks complete despite missing evidence.
Keep a record of errors and the corrections they require. If an output reaches the wrong person or changes a record incorrectly, the team needs a clear way to contain the issue and restore the normal process.
NIST's Generative AI Profile provides a broader reference for managing risks across design, use and evaluation. The controls for a particular pilot still need to reflect its actual task and consequences.
Decide what would justify continuing
Set the review date and continuation criteria before the trial begins. A six-week period may suit a narrow task once data access and approvals are ready. A project involving complex integrations may need a different schedule.
At the review, compare the full workflow with the baseline. Include review effort, errors, support demands and the cost of operating the tool. Ask whether the team can sustain the improvement during ordinary work.
The next step may be a wider rollout, a narrower task or a decision to stop. A useful pilot produces evidence for that choice and a clearer understanding of the process it was intended to improve.
Further reading: NIST Generative AI Profile.