Data & AI Discovery.

• Use case identification. Facilitated workshops to surface and prioritise data and AI use cases across business domains. Anchored to commercial value, not technical feasibility. A scoring framework your teams can keep running after I leave. Toolkit included.
• Vendor assessment. Independent technology selection across the data and AI stack. Requirements scoping, market scan, shortlist, due diligence, contract guidance. Vendor-neutral counsel built from active connections across the market.

Done well, they reinforce each other. Use cases drive vendor selection. Vendor capabilities shape which use cases are feasible.

Organisations with a long use case backlog and no shared way to prioritise it. Programmes about to procure a marketplace, catalog, governance tool, or AI platform and wary of vendor pitch energy.

Particularly useful when the buying decision is cross-functional: data, IT, business, risk all need to agree, and no one has a defensible methodology to align them.

Use case identification: typically two weeks of stakeholder interviews + working sessions + a scoring workshop. Output: prioritised use case roadmap with commercial cases.

Vendor assessment: three to six weeks depending on category. Output: shortlist memo, scored matrix, recommended next steps. I do not take vendor commissions.

• A prioritised use case backlog tied to commercial value, ready to fund
• A scoring framework your team uses in steady state
• A shortlist of two to three vendors with reasoned trade-offs
• A reference architecture aligned to the shortlist
• An honest read on which vendors will overpromise and underdeliver
• The internal alignment to commit, with a defensible paper trail