AI strategy and use-case portfolio
A funded first wave of AI cases instead of parallel departmental pilots, ranked in one scored portfolio built on a blueprint of enterprise processes. Scope closes after the first discovery round.
Most AI initiatives never leave the pilot stage
Pilots start inside a single department, with its own vendor, its own success criteria and no comparison against other candidates. Industry data records the outcome: 87% of AI projects never move beyond proof of concept (Gartner 2025) and fewer than 25% of organisations report measurable ROI (McKinsey 2025).
A scored portfolio ranks every candidate on business impact, feasibility, data readiness and process standardisation, applying the same criterion across every business area. Once the cases sit on one scale, the argument stops being which pilot deserves budget and becomes which wave you fund first. The list becomes a sequencing decision, with a named owner and a business case behind each entry.
Why AI ambition does not turn into results
The four gaps behind the pilot stall rate, and what closes each one.
Every department picks its own. Each area runs its own pilots with its own vendors and criteria, so nothing is comparable.
Use cases chosen by enthusiasm. Priority goes to what sounds impressive rather than to what pays back.
Process blind spots. 70% of processes lack the data infrastructure to support AI, and nobody checks before committing.
No business case, no owner. Initiatives arrive without a baseline, a target or anyone accountable for them.
Value never measured. Nobody compares the result against what was promised, so the next round repeats the same mistakes.
From a long list to the handful that reach production
What scoring does to a portfolio: it narrows it to what the organization can actually deliver and fund, and makes the leaks between stages visible.
Illustrative of how a portfolio narrows. Your own numbers come out of the scoring workshop.
What you take away
From process discovery to a portfolio your board can sign off. This is not for teams looking to build a single model; it assumes several AI initiatives already running across business areas.
Process map and candidate list
Scored and ranked shortlist
Business case pack
Three-wave roadmap
What working together looks like
Score your existing AI candidates
Six to eight weeks: processes mapped, candidates ranked with your teams, and a roadmap by waves your board can approve.
