Prototype Sprint
What an AI prototype sprint should include
The deliverables that make a two-week AI prototype useful after the sprint ends.
By JirakJ
4 min read
Sprints produce screens but not enough context to decide what should happen next. I would treat that less as an AI opportunity and more as a workflow leak.
I would rather see one honest workflow map than ten polished AI use-case slides. The team does not need a bigger story yet. It needs a smaller decision that can survive contact with real work.
A small field test
Take one recent example of this workflow and replay it from request to finished output. The weak point will usually match the complaint: sprints produce screens but not enough context to decide what should happen next.
Where the human stays
The human work is deciding what good means, what risk is acceptable and when a draft is not good enough. That judgment should be designed into the flow, not left to chance.
What to change first
Include product brief, flows, assumptions, tech notes and next-step backlog. Do that before choosing a platform or adding another automation layer.
What I would keep
Keep the prototype sprint deliverable list. It becomes the reference point when the team forgets why the workflow was changed in the first place.
Monday morning checklist
- • Turn the next meeting into a decision log instead of another broad AI discussion.
- • Write down the artifact that would make the work reviewable: in this case, a prototype sprint deliverable list.
- • Decide who owns the next version if the first version works.
- • Mark the part of the workflow where human judgment must stay visible.
If this sounds familiar
Start with one workflow. FlowMason AI can map it, identify the right intervention, and define whether the next step should be a prototype, agent, documentation pipeline or delivery system.
Request audit fit review