Most AI strategies are lists of ideas. Yours should be a sequence of decisions.
The hard part of AI strategy isn't finding use cases; every workshop produces forty. It's deciding what to fund first, what to defer, and what to refuse, based on what your data, systems, and teams can actually carry.
We build that answer with you: a roadmap where every initiative has an owner, a cost, a payback logic, and a place in line. Because the people who write it also build and run these systems, nothing lands on the roadmap that can't survive production.
FOR LEADERS WHO
need a defensible answer to "what's our AI plan?"
FOR TEAMS WITH
pilots everywhere and production nowhere.
FOR BOARDS THAT
want the investment case before the invoice.
01 What you get
01
The value map.
Where AI creates durable value in your business, by function, workflow, and P&L line, and where it demonstrably doesn't.
02
The sequenced roadmap.
Initiatives in funding order, with dependencies made explicit: what unlocks what, what waits on data work, what ships this quarter.
03
The business case.
Cost, payback logic, and risk for each initiative, written to survive a CFO's questions rather than a steering committee's applause.
04
Executive alignment.
Your leadership team lands on one version of the plan: priorities argued, trade-offs made, ownership assigned before we leave the room.
02 How it runs
Four phases, one decision at the end.
Senior people in the room from day one. No discovery theater.
PHASE 01
Immersion.
We sit with your operators, not just your slides: the workflows, the systems, the economics as they actually are.
PHASE 02
Opportunity scan.
Candidate use cases are surfaced and stress-tested against your data reality, with engineers in the room testing what's actually feasible.
PHASE 03
Prioritize & price.
Each initiative scored on value, feasibility, and risk; business cases written; the sequence argued with your leadership.
PHASE 04
The decision.
Roadmap, funding ask, and ownership on one page, ready to take to the board or straight into a build sprint with us.
03 Questions we answer
Where does AI move our P&L, and where is it just expensive theater?
What should we build first, and what should wait?
Buy, build, or partner: for which pieces?
What will it cost, and when does it pay back?
Is our data ready? And if not, what's the gap?
Who owns this once it's running?
04 Continue the arc