AI ROADMAP
A roadmap built on evidence.
You need a plan for AI: what to build, in what order, with what budget. The trap is a plan built from interviews and industry decks. We build roadmaps by testing: a diagnostic on your actual systems and data produces a ranked map of where AI creates real leverage, what it will take, and what to do first.
THE PROBLEM
Most AI roadmaps age badly.
They get written from stakeholder interviews and maturity models, before anyone has tested whether the data can support the use cases or the models can clear the quality bar. Then the models change, the assumptions drift, and the plan quietly becomes a document. The record is public: MIT research in 2025 found 95 percent of enterprise GenAI pilots delivering no measurable P&L return, and Gartner expects over 40 percent of agentic AI projects to be canceled by the end of 2027. A roadmap is only as good as the evidence underneath it.
HOW WE BUILD ONE
Test first. Then plan.
The roadmap is the output of a diagnostic, and the diagnostic runs on your real systems. We define what working would mean for your highest-stakes use cases, instrument that with evals and red-teaming, and assess your data and workflows against it. What comes back is a ranked map: where AI creates leverage, what each item requires, how ready your data is, and honest effort estimates. If you have nothing deployed yet, the readiness assessment is the same motion for a greenfield.
Every phase after that is quoted from the diagnostic's findings, so the budget conversation happens on evidence about your own company.
FROM PLAN TO PRODUCTION
A roadmap that ships itself.
Each roadmap item runs as a vertical slice of one method: define what working means, instrument it, test against it, fix the root cause, refine against production, and expand what passes. The plan updates as the evals learn from production, so the roadmap stays alive as models and priorities change. Run enough slices and the roadmap becomes the transformation: a company that runs on AI it can prove.
COMMON QUESTIONS
Asked by roadmap owners, answered plainly.
How is this different from a consulting firm's AI roadmap?
Ours is tested. Consulting roadmaps get built from interviews and maturity models; ours is the output of a three-week diagnostic run on your actual systems and data, so every item arrives with evidence, an effort estimate, and a fixed-price quote. The plan and the budget conversation happen on facts about your own company.
The board wants AI results this year. Can a roadmap move that fast?
Yes, because it ships while it plans: the diagnostic takes three weeks and the first vertical slice follows immediately. In BCG's 2026 survey, 61 percent of CEOs said their boards are rushing AI transformation. Evidence is how you keep the speed without joining the failure statistics.
We already have a roadmap. Is this redundant?
Point the diagnostic at it. We test your roadmap's assumptions against your data readiness and your quality bar, retire the items that would never survive production, and attach evidence and quotes to the ones that will. Cheaper to learn that in week three than at milestone three.
Why do so many AI pilots fail?
Nobody defined what working means before building. MIT's 2025 research found 95 percent of enterprise GenAI pilots deliver no measurable P&L return, and the common thread is unmeasured behavior: no standard, no evals, no way to tell progress from motion. Our roadmaps start from the standard.
LET'S TALK
Bring us the hard problem.
We'll bring the team that ships.