Know exactly where AI pays off before you spend on it
A defensible plan for where AI creates value in your business — and where it doesn't.
All AI & Intelligent SystemsBoards demand an AI story; vendors pitch a hundred pilots. Without a value-ranked roadmap, enterprises scatter budget across demos that never reach production — and conclude, wrongly, that AI doesn't work for them.
- A value-ranked AI portfolio instead of a pilot graveyard
- Budget concentrated on the 2–3 use cases that clear the bar
- A governance posture ready for regulator and board scrutiny
What the work actually involves
AI opportunity audit
Workshop-driven scan of your workflows, ranked by value, feasibility, and data readiness.
Data readiness assessment
What your data can support today, and the shortest path to what it can't.
Build/buy/wait decisions
Per use case: custom build, product purchase, or deliberate waiting — with the reasoning written down.
Governance & risk framework
Usage policies, review gates, and accountability lines your compliance team can operate.
Phased investment roadmap
A sequenced 12–24 month plan where each phase funds confidence in the next.
Model and vendor selection
Which models, hosted where, under what commercial and data terms — assessed against your residency constraints rather than a benchmark leaderboard.
Capability and team planning
Who operates this after the first workflow ships: what to hire, what to keep with a partner, and the skills that decide whether the second use case is cheaper than the first.
What you are handed.
Yours to keep, and written so another team could pick them up.
A ranked use-case portfolio
Every candidate scored on value, feasibility and data readiness, with the ones we advise against and why.
Data readiness assessment
What your data supports today, what it does not, and the shortest route between the two.
Build / buy / wait decisions
One line per use case with the reasoning attached, so the decision survives the person who made it.
A phased investment plan
12–24 months sequenced so each phase funds confidence in the next, costed enough to take to a board.
The engagement
We do not publish prices — scope drives them. Everything else, here.
- Starts with
- A scoping call, then workshops with the teams whose work would change
- Typical duration
- 4–8 weeks
- Who you get
- A principal architect and an AI engineer, with a data engineer for the readiness work
- Commercial model
- Fixed fee against defined artefacts
What the answer depends on.
Two sets of conditions. Read both against your own situation — most organisations recognise themselves in one column within a sentence or two.
This is the right call when
- Your board has asked for an AI plan and you want one that survives scrutiny.
- You have run pilots that never reached production and want to know why.
- You need to know where *not* to spend before you commit a budget.
A different approach fits better when
- You already know the use case and want it built — start at Custom AI Development instead.
- You want validation for a decision already taken.
- You need it in a fortnight. Scoring a portfolio takes four weeks minimum.
Questions about AI strategy & roadmap
Where this comes up most.
The regulatory context and the systems already in the building change the build. Each sector page says how.
If this is close to what you need but not quite it, that gap is usually the useful part of a first call — discuss this capability.