Open-ended AI programs drift. A fixed-scope sprint forces clarity: one workflow, explicit acceptance criteria, a working handover, and a decision point. That shape matches how value actually shows up in the research—narrow, measured changes to how work gets done—not platform theater.
Pick one workflow with an owner
Choose a recurring task someone already completes. Name the owner, the inputs, the exceptions, and what “good” looks like. “Explore AI for ops” is not a sprint. “Draft first-response replies for these three ticket types with human send” is.
Write acceptance before you build
Agree on quality, latency, review rules, and what remains manual. Include the failure path: timeouts, low confidence, and disallowed data. If you cannot write the criteria, you do not have a scope.
Ship the handover in the same box
Configuration, eval examples, known limits, and who operates the system belong in the sprint—not as a follow-up myth. A tool only its author can run did not finish.
Decide with evidence
At the end, compare acceptance criteria to reality. Continue, widen, or stop. Platforms and shared infrastructure earn their place from repeated needs across finished workflows—not from the kickoff slide.