Each engagement starts differently, but the pattern is consistent: clarify what matters, design how AI strengthens the work, and build the capability to keep going.
I work with expertise-heavy organisations — mostly mid-market and enterprise, in sectors where bad AI decisions create operational, legal, or reputational risk.
They're not asking whether to use AI. They're asking how to deploy it so it strengthens the expertise, trust, and accountability their value depends on.
Healthcare, scientific services, water, environmental. AI has to earn its place alongside professional accountability and regulatory rigour.
Organisations whose premium lies in professional expertise and domain knowledge, not just process efficiency. The goal is amplifying what clients already pay for.
Businesses whose value depends on how they curate, connect, interpret, and deliver — events, media, professional services. AI should sharpen the offer, not flatten it.
From Shadow AI to Safe Use
Situation
People were already using AI tools — they just needed a sanctioned way to do it. Leadership had limited visibility over what was happening.
What I did
Diagnosed current AI use through stakeholder interviews, built a prioritisation framework, and created a sanctioned path for scaling what was already working.
What changed
Leadership gained visibility over AI experimentation. A lightweight governance framework now channels informal use into safe, visible practice.
What it made possible
The framework is now being extended across additional departments, with internal ownership of the rollout. Two further country operations are now evaluating the same approach for their own contexts.
From Drafts to Decisions
Situation
Field engineers produce safety-critical reports under strict regulatory frameworks. Documentation was slow; AI could help, but professional accountability could not be compromised.
What I did
Delivered an AI literacy programme across the organisation and designed workflow automation that cuts documentation time while keeping professional sign-off intact.
What changed
Engineers gained confidence using AI within clear boundaries. The work created a path to shorter documentation cycles without weakening professional sign-off.
What it made possible
Subsequent work extended into additional bounded workflows — each scoped and delivered as its own piece rather than as open-ended consultancy.
From Momentum to Strategy
Situation
A science-led organisation with strong AI momentum across multiple countries — but no coherent strategy connecting that energy to what makes the business valuable.
What I did
Worked with the executive team to map AI activity against the organisation's core strengths — expertise, rigour, and client trust — and built a prioritisation framework for scaling what mattered.
What changed
Leadership moved from reacting to AI enthusiasm to directing it. The work gave leadership a clearer basis for connecting AI adoption to the things the business is trusted for.
What it made possible
Initial executive work opened discussions about extending the approach beyond the original region.
From Licences to Value
Situation
AI tools had been rolled out company-wide, but leadership wanted clearer results. Usage was broad; impact was unclear.
What I did
Ran executive sessions focused on turning experimentation into real workflows, real decisions, and measurable value — not just licence adoption.
What changed
Teams shifted from generic AI use to specific, high-value applications tied to the information products the business is known for.
What it made possible
The work was delivered as a series of distinct executive sessions across regions, each scoped as its own piece.
From Skills to Practice
Situation
The design team wanted practical AI skills for research, ideation, and prototyping — but needed structured guidance, not just tools.
What I did
Built structured prompts, embedded ethical watch-outs into the design workflow, and ran a real design challenge to test the approach under working conditions.
What changed
The team moved from curiosity to competence. They ran follow-up sessions internally without further support.
From Theory to Practice
Situation
A cross-community Fellowship needed AI sessions for leaders whose currency is accountability, relationships, and public trust — not technical fluency.
What I did
Designed and delivered scenario-based sessions at the Oxford residency, grounded in the real decisions these leaders actually face.
What changed
Fellows left with a practical framework for evaluating AI decisions in their own contexts — civic leadership, peacebuilding, public accountability.
If you're past "should we do AI?" and thinking about how to actually make this work — I'd be happy to have a conversation.
Start a conversation— David