Your team has AI subscriptions. Someone has made a convincing demonstration. The next question is what should become part of everyday work.
Start by naming one workflow, the person who owns it, and the decision a pilot needs to support. That creates a practical boundary for testing whether the change is useful.
Choose a task you can describe
A useful pilot has a recognizable input and a finish that someone can judge. Examples might include preparing a first draft of a test plan, extracting defined fields from a document, or finding relevant internal instructions for a support task. These are possible pilot types, not claims about delivered client work.
Write down what happens today. Who does the work? Where does the information come from? Which steps already need judgment? How often do incomplete or unusual inputs arrive?
Also consider whether a conventional integration, a clearer template, or a process change would address the same need. The pilot should be compared with a credible alternative.
Count the whole task
Time to generate an output is only one part of the work. Include time spent gathering context, checking the result, correcting it, handling exceptions, and returning the result to the system where it belongs.
For engineering, inspect what happens downstream. Did a faster first draft create a longer review? Were the tests relevant? Did the change introduce more rework? Did the team understand the result well enough to maintain it?
DORA’s research on effective AI use describes the tradeoffs between faster generation, verification effort, review, and delivery. That is a reason to measure the workflow your team operates, rather than use generated output as a stand-in for success.
Agree the review and access boundaries
Identify the inputs and tools the pilot may use, and who approves that use. Decide which outputs require review and which actions require explicit approval before they affect another system.
Give uncertainty a destination. If an input is incomplete, a result fails a check, or a provider is unavailable, someone needs to know what happens next. A manual route may be the right initial answer, provided it is visible and owned.
Those operating choices belong in the pilot design. They affect the effort and cost of using it regularly.
Test ordinary and awkward cases
Prepare representative examples before building. Include normal work alongside incomplete inputs, ambiguous requests, unusual formats, and failures. Keep an evaluation set separate from the examples used to improve the pilot where practical.
Record the expected result, actual result, review effort, correction effort, and any escalation. Let the person responsible for the workflow judge whether the output is useful in context.
The AI pilot scorecard provides a starting structure for the baseline, acceptance criteria, and operating questions.
Finish with a decision
The next step should follow the evidence:
- Expand when the pilot meets the agreed criteria and the wider responsibility is understood.
- Revise when a specific gap warrants another bounded iteration.
- Stop when the result does not justify the review effort, cost, or integration work.
Document the result, limitations, owner, and conditions for revisiting the decision. A stopped pilot can still prevent a larger commitment to the wrong workflow.
AI Enablement & Automation is a way to choose that first workflow, define an evaluation, and work through the pilot and handoff with your team.