Discovery built for an organisation of 13 functions
The process began with a rapid AI maturity assessment — not to produce a score, but to understand how ready each function actually was to adopt AI, and what "adoption" would realistically mean for them. This shaped how we approached each part of the organisation: meeting people where they were, rather than applying a uniform methodology to uneven ground. We then ran systematic AI opportunity discovery workshops with each of the 13 functions. The workshops were designed to surface possibilities without creating false expectations — helping teams identify what AI could plausibly do for them, translate those possibilities into concrete use cases, and understand what it would take to pursue them. Across the organisation, this produced 125 identified AI-driven use cases. To make sense of that volume, we developed a custom prioritisation model that evaluated each use case against business value, feasibility, employee engagement, and strategic alignment. The model was built to be transparent — so that decisions about what to pursue first could be explained and challenged, not just accepted. The result was a ranked, realistic roadmap for AI development across the organisation.
125 use cases identified across 13 functions. The prioritisation model turned that into a manageable, sequenced roadmap — and gave the organisation a replicable tool for evaluating future opportunities as they emerge.
From concepts to proof-of-concepts
The initiative was designed to produce learning, not just plans. Rather than stopping at the roadmap, we moved quickly into experimentation — helping teams validate their highest-priority use cases in the real context of their work. The approach was deliberate: short cycles, real feedback, clear success metrics. Teams moved from concept to experiment to pilot without getting stuck in lengthy planning phases. Experiments that didn't work taught something. Experiments that did work advanced to piloting with defined measures of success, making the case for continued investment concrete rather than theoretical. By the end of the engagement, 15 proof-of-concepts had been iterated and demonstrated across multiple business functions. Each one represented a validated idea — something the organisation had tested against reality and decided was worth building on. The engagement also produced a replicable methodology for running future AI initiatives, so the process can continue inside the organisation without external facilitation.
15 demonstrated proof-of-concepts. The organisation now has both a prioritised roadmap and the internal methodology to keep moving — without needing to start the discovery process from scratch for every new initiative.
Next projects.
(2016-25©)
