My method
A process built for
real results.
Most AI programmes fail not because the technology is wrong, but because the process is. I designed this method to remove the most common failure modes — technology-first thinking, capability gaps, and missing business cases.
The AI Operating System is what I build. Illuminate → Align → Activate → Accelerate is how I build it.
01
Weeks 1–2
Illuminate
I start by getting genuinely clear on where you are. A rigorous audit of your digital landscape, customer experience, technology stack, data, and competitive position — plus structured interviews with your leadership team. The output is an honest, evidence-based picture of what is actually possible, not a list of assumptions.
Key deliverables
- AI readiness assessment report
- Current-state process map
- Data infrastructure audit
- Stakeholder interview synthesis
- Identified opportunity areas
02
Weeks 3–5
Align
Strategy only works when the whole organisation points the same way. I translate the diagnosis into a prioritised roadmap — sequenced by business impact and feasibility, costed, and tied to your P&L — then work it through leadership, marketing, sales, and technology until the agreement is real rather than polite.
Key deliverables
- Prioritised AI use case register
- Phased implementation roadmap
- Board-ready business case
- Success metrics framework
- Resource and capability plan
03
Weeks 6–16
Activate
Strategy becomes working capability. I start deliberately narrow — a single high-value use case with defined success criteria — then scope it, oversee the build, select technology partners where needed, and run the training and change work that makes it stick. I work alongside your team rather than above it, so the capability stays when the engagement ends.
Key deliverables
- Scoped first use case with success criteria
- Technology partner selection (where needed)
- Implementation oversight and QA
- Staff training and change management
- Results and learnings report
04
Ongoing
Accelerate
Good strategy evolves. I establish the measurement frameworks, governance, feedback loops, and quarterly review cadence that let you keep extending the work across teams and use cases — so the value compounds long after the initial engagement, and AI becomes a standing capability rather than a completed project.
Key deliverables
- Scaled implementation programme
- AI governance framework
- Internal capability building plan
- Ongoing performance measurement
- Quarterly strategic review
My principles
How I think, not just
how I work.
Evidence before investment
I never recommend a tool, approach, or vendor before understanding your specific situation. Every recommendation is grounded in what I have observed in your business — not in what is fashionable, and not in what I have a commercial relationship with.
Business outcomes, not technology outputs
My engagement metrics are always tied to your P&L: time saved, revenue generated, costs reduced, errors eliminated. I measure what matters to your business, not what makes AI look impressive.
Capability over dependency
My goal is to build your organisation's ability to think about and manage AI — not to create a reliance on an external consultant. Every engagement includes knowledge transfer and capability building as a core component.
Honest counsel
If AI is not the right solution for a particular problem, I will say so. If your data is not ready, I will tell you before you spend the budget. The trust I build with clients is worth more to me than any single engagement fee.
Engagement models
The right structure for
your situation.
Workshops, project sprints, and advisory retainers — each designed for a different stage of the journey.
Not sure where to start?
Take the AI readiness checklist
15 minutes. 35 questions. A clear picture of where your business stands — and where to focus first.
Let’s find your light.
Whether you are at the start of your AI programme or deep into it, a single conversation can change its trajectory.