AI operating model
AI Use Case Prioritisation: How to Choose the AI Projects Worth Funding
Prioritise AI use cases on four tests: value, feasibility, readiness and risk. Score each candidate against a business outcome the board already cares about, check the data and process are ready, weigh the risk, and fund a short list of three to five. Just as important: write down what you will not do. Most failed AI programmes funded too many pilots, not too few.
Read the full briefingIn brief
- Start from business outcomes, not a list of tools. A use case without an outcome is an experiment.
- Score every candidate the same way: value, feasibility, readiness and risk.
- Fund three to five priorities properly rather than twenty pilots thinly.
- Each priority needs an owner, a baseline and a measure before it is funded.
- Re-score quarterly: readiness improves, and the list should move with it.
Why the board should care
Pilots spread too thin
Twenty small pilots with no owner or baseline cost more than five funded priorities and prove less.
The loudest idea wins
Without an agreed method, AI money follows enthusiasm or vendor pitches rather than value.
High-risk use by accident
A use case that looks simple can touch customers, employees or regulated decisions. Scoring risk early avoids surprises.
Full briefing
Read the full briefing
Free for executives. Confirm your work email once and every AIPEX executive briefing opens for 12 months.
- The full written briefing
Next step
Agree the short list in one day
The Executive AI Strategy Day takes your leadership team from a long list to funded priorities, owners and a 90-day plan. £1,499 + VAT.
FAQ
Frequently asked questions
How do you prioritise AI use cases?
Score each candidate on value, feasibility, readiness and risk, against a business outcome the board already cares about, then fund a short list of three to five with an owner, a baseline and a measure.
What is an AI use case prioritisation framework?
A consistent way to compare AI ideas. The AIPEX approach uses four tests (value, feasibility, readiness and risk) and a matrix that separates fund now, fix foundations first, quick wins and stop.
How many AI use cases should we start with?
Three to five, funded properly. Most failed AI programmes spread money across too many pilots with no owner or baseline.
Who should decide which AI use cases to fund?
The leadership team, led by the executive AI owner, with finance signing off the value case and risk reviewing high-risk use. The board approves major investment.
Where do the best AI use cases come from?
From the business outcomes the board already tracks: cost, revenue, service, risk and speed. Ask where those outcomes are held back by manual work, slow decisions or poor information.
This page is general information on prioritising AI use cases. It is not financial or investment advice.