Portfolio & Use Cases
Covers how ideas for using AI are collected, ranked, tested and either scaled, stopped or retired. Treating uses of AI as a portfolio means effort goes to the uses most likely to pay off, weak ideas are stopped early and cheaply, and what works in one place is reused elsewhere.
Idea intake open to all staff
Anyone in the organisation can propose a use of AI through a simple, well-known route, and every proposal is acknowledged and considered.
Agreed prioritisation criteria
Proposed uses of AI are ranked against published criteria covering value, feasibility, risk, cost and strategic fit, applied the same way to every proposal.
Pipeline register of proposed uses
A single register records every proposed and in-flight use of AI with its status, owner and decision history, and hands each entry to the AI inventory when it goes live.
Stage gates from idea to retirement
Each use of AI passes through defined stages (explore, pilot, scale, operate, retire), with entry and exit criteria checked at each gate.
Proportionate business case
Each use of AI has a business case scaled to its size and risk that states expected benefits, full costs and assumptions, and is approved before significant commitment.
Safe experimentation
Staff can try AI quickly within defined guardrails covering approved environments, data limits, time limits and exit criteria.
Pilot to production decisions
Every pilot ends in a recorded decision to scale, extend or stop, made against criteria and evidence agreed before the pilot started.
Reuse across the organisation
Successful patterns, components and lessons from uses of AI are recorded so others can find them, and are reused before anything new is built.
Retiring uses of AI
Uses of AI that no longer earn their place are withdrawn through a planned process that covers users, dependent processes and disposal or retention of models and data.