How do we turn board-level AI ambition into a buildable plan?
Executive AI discovery & product translation
Sold asAI Opportunity & Feasibility Sprint · Fixed scope · typically 2–4 weeks
Executive AI discovery turns an ambition into a plan a CFO can fund and an engineer can start on Monday. We run the leadership sessions, map where the value sits, test feasibility against your data and systems, settle build versus buy, and write the roadmap, the investment case and the governance conditions. Programmes rarely stall because the technology is hard. They stall because nobody turned the ambition into something specific, sequenced and fundable, and the cheapest place to find that out is before the budget is committed.
The problem this solves
A board asks for an AI strategy. What comes back is either a slide deck with no engineering in it, or an engineering plan with no business case in it. Both stall, for the same reason: the person writing them could only see one half of the problem.
Discovery closes that gap. It produces a plan that a CFO can fund and an engineer can start on Monday, because the same person wrote both halves and can defend the trade-offs between them.
How the work runs
It is deliberately short. The aim is a decision, not a documentation exercise.
Leadership sessions
What the business is actually trying to change, commercially rather than technically. Where the pressure is coming from, and what success would look like to the people funding it.
Workflow analysis
How the work is done today, including the spreadsheets and manual steps nobody documents. This is usually where the real opportunities are hiding.
Data and systems feasibility
What data exists, what state it is in, who is allowed to see it, and which systems would have to be touched. Most AI plans die here, and it is cheaper to find out early.
Opportunity map
Candidate use cases scored on value, feasibility and risk, so the sequence is defensible rather than whichever idea had the loudest sponsor.
Governance review
What has to be true for legal, security, and risk to sign off, established before anything is built rather than discovered at the end.
Why it comes first
Almost every expensive AI failure traces back to skipping this. A team builds the use case that was easiest to describe, rather than the one that was most valuable, and finds out at rollout that the data was not accessible or the process owner was never consulted.
Discovery is the cheapest step in the programme and the one that determines whether the rest of the money is well spent.
What you get
- Opportunity map with value, feasibility and risk scoring
- Current-state workflow analysis
- Technical architecture for the recommended direction
- Sequenced delivery roadmap
- Risk and governance notes
- Build-versus-buy recommendation
Start here if
- Leadership has committed to AI but nobody has converted it into a plan
- Several teams are running disconnected experiments
- A business case is needed before budget is released
- Previous AI work stalled and no one is certain why
Frequently asked questions
How long does discovery take?
It is scoped as a short, focused engagement rather than an open-ended consulting phase: long enough to interview the people who own the work and assess the data honestly, short enough that it does not become the project. The output is a decision and a roadmap, not a research programme.
Do you need access to our data to do this?
It helps a lot. Feasibility claims made without seeing the data are guesses. Where access is impossible in the timeframe, the assessment states its assumptions so they can be tested before build.
Related
Have the ambition, not the plan?
Bring the board deck and the org chart. We will tell you which use case to build first and what it will actually take.
Last reviewed · 1AYM