A good AI roadmap sprint produces decisions: what to build, what to avoid, architecture options, risk controls, and a 90-day execution plan.
Week 1: diagnostic and workflow mapping
Identify bottlenecks, map data sources, and shortlist use cases with owners and ROI hypotheses.
The first week is about listening, not recommending. You need to understand where the real pain is, not where leadership thinks the pain is.
Week 2: architecture and plan
Define stack options, governance requirements, evaluation plan, and a phased delivery roadmap.
The output should be specific enough that an engineering team can start building on Monday. If it requires "further discovery" before anyone can act, the sprint didn't do its job.
Related reading:
- What should be included in an AI diagnostic or roadmap?
- Do I need an AI strategy before building anything?
Frequently asked questions
Do we need stakeholders in workshops?
Yes. The sprint fails without process owners and decision-makers in the room.
What is the output artifact?
A concise blueprint: use case shortlist, architecture, risk controls, and a 90-day delivery plan.