A useful feasibility sprint ends with a decision. The goal is to find out whether a particular application of AI can improve a workflow enough to justify the next investment. A polished demonstration alone cannot answer that question.
Start with the current process
Choose one role and one repeated task. Walk through an actual example with the person who does the work. Note the inputs they need, the decisions they make, the time spent and the corrections that happen later. Existing tools may already solve part of the problem; the prototype should fit into that context.
Define the output and its reviewer
“Summarise our data” leaves too much open. A more testable brief might be: draft an inspection note from an approved photograph and a voice observation, link it to the correct asset, and require an inspector to approve it. Specify the required fields, the evidence behind them and which steps remain human decisions.
Agree the evidence before the demonstration
Set aside representative examples that are not used while tuning the prompts. Include ordinary work and difficult cases. Compare completion time, missed details, incorrect assertions and time spent correcting the output. The team should decide in advance which failures prevent a rollout.
Use the current process as the baseline. An output that takes two minutes to generate and ten minutes to verify may be less useful than the existing approach, even when the demonstration appears impressive.
Keep integrations proportional
A sprint usually needs enough integration to test the core assumption, not an entire enterprise deployment. Use an authorised export or a narrowly scoped connection where appropriate. Separately document the identity, access, retention and system integration work required for a production release, so the prototype does not hide that cost.
Leave with a decision record
- The task and baseline the team evaluated.
- Representative results, including failures and limitations.
- The data and integration requirements for the next phase.
- A proposed scope, milestones and ownership.
- A recommendation to build, revise the use case or stop.
This makes feasibility work useful even when the answer is to defer AI. It gives the business and engineering team a shared explanation of what was learned, and what would need to change before trying again.