When to use this
The AI ambition is vague
The client wants an AI roadmap, but nobody can say which workflows are worth changing first. Workshops give you ideas, not enough evidence to commit.
Use cases look good from too far away
Pilots fail when the use case was chosen from the outside. You need evidence from the people doing the work before you recommend what to build.
Budget needs a ranked roadmap
The client cannot fund everything. You need to rank opportunities by pain, readiness, impact, and effort so the roadmap feels defensible.
How it works
Define the AI opportunity lens
Set up interviews around workflows, pain points, data availability, adoption barriers, and value drivers.
Capture real work from every function
Reach the people closest to the work. Understand what they do, where friction sits, and what would actually change if AI helped.
Score use cases against evidence
Prioritize opportunities by pain, frequency, readiness, feasibility, and impact - not by who shouted loudest in the workshop.
Export the AI roadmap inputs
Get the ranked use cases, evidence trails, readiness gaps, and recommendations your team needs to build the roadmap.
Impact
Use cases ranked
AI opportunities scored by real pain, readiness, feasibility, and impact
Bad pilots avoided
Ideas pressure-tested before budget is spent building the wrong thing
Roadmap evidence
Every recommendation traceable to stakeholder input and workflow reality
Ready to fund
A clearer path from AI ambition to the first projects worth backing
