AI Strategy & Business Development

An AI strategy is not a list of tools. It is a set of decisions about where intelligence — human and machine — creates value in your organisation, what you are willing to depend on, and how you will know it worked. My role is to make those decisions well-informed, defensible and executable.

Why this matters now

The first wave of AI adoption was driven by fear of missing out and produced a lot of pilots that never reached production. The second wave is driven by economics and regulation: budgets are being scrutinised, the AI Act creates obligations, and sovereignty is on the agenda. Organisations now need to choose deliberately — which use cases, which dependencies, which capabilities to own — and to explain those choices to boards, auditors and employees.

For research institutions and innovation-driven companies, the same shift shows up in funding: programmes increasingly demand demonstrable impact, interdisciplinary consortia and responsible-AI practice.

How I approach it

I combine business analysis with the technical depth to check whether a use case is actually feasible at your data quality, your budget and your constraints. Use cases are ranked by value, feasibility and risk, then turned into business cases that survive scrutiny. Build-buy-partner decisions are made with total cost, lock-in and sovereignty on the table.

Experience from government-funded research projects lets me harmonise the technical and the social: shaping proposals, structuring consortia, translating between engineers, researchers, management and funders.

Where it typically starts

Often with a one-day executive workshop and a short follow-up analysis, producing a strategy memo and a prioritised portfolio. From there, advisory support on a monthly basis, or a concrete build on the highest-value use case.