Cut through the noise
I separate the use cases that will move your business from the expensive distractions. That includes a confident no on the ideas that won't.
AI strategy for ambitious SMEs
Most AI ideas are not worth building. I help you find the ones that are.
I help ambitious SMEs separate hype from impact: a prioritised, costed plan of what to build now, what to build later, what to ignore, and why. You get a trusted advisor and a hands-on AI engineer in the same person: someone who has shipped agents, RAG pipelines, and production data science himself.
The sentence I hear most
“AI is changing incredibly fast. I want my company to keep up and stay ahead of the competition, but I lack the expertise to steer in the right direction.”
What usually happens next is one of three things: you wait for AI to settle down (it won't), you buy Copilot licences with no use cases behind them, or a few enthusiasts experiment without a framework. All three burn time and money. And none of them answer the real question: what is worth building here?
I've built what I advise on
Years of doing exactly this work: as a data-analytics consultant at PwC across banking, telecom, and public organisations, and hands-on as a data scientist and AI engineer. I built and launched an AI product solo (RAG, GraphRAG, the lot), so I know first-hand what ships and what only demos well. And because I teach at Nyenrode Business University and train teams for a living, I can sit with your engineers and your director in the same hour and make each understand the other.
- 8 years in data & AI
- Former PwC data analytics consultant
- Lecturer at Nyenrode Business University
- Shipped AI products myself
Trusted by
What I do
Make it buildable
Every idea is scored on impact, effort, and whether your data can actually carry it.
Upskill your team
If you have your own developers, I train them to build the top use cases themselves. The training starts with AI-assisted coding.
Technical upskillingThree ways in
Every engagement stands on its own and delivers value by itself. Each one also opens a natural next step.
AI Quickscan
A fast read on the gap between where you stand today and where you want to be. Stakeholder interviews, an AI-readiness scorecard, and a debrief. The low-commitment way to find out where the real opportunities sit.
Most chosen
AI Opportunity Map
We zoom in on three to five priority processes, your own people surface the ideas, and I score every candidate on impact, effort, and data-readiness. You leave with a short list of high-conviction, costed use cases, plus a clear list of what to ignore.
AI Execution Plan
Everything in the Opportunity Map, plus a phased implementation roadmap, a deep data-readiness check on your top use cases, and budget and build/buy guidance. For when you want the full route, not just the map.
And after the plan? The goal is that your company can build without me. Until then I stay on as architect and sparring partner for your developers, and as trusted advisor for your management.
What you leave with
The map is one page. The value is the work behind it: interviews, a session where your own people surface the ideas, and a scoring I can defend line by line. So you innovate with AI on use cases that have already proven themselves.
- Build now
- Build later
- Ignore
- Data not ready yet
The short list
- Rebuild the company website with AI-assisted coding
- AI drafts quotes from incoming requests
- AI reads supplier invoices straight into the ERP
- An AI assistant answers engineers' questions from the manuals
- Customer portal built with AI-assisted coding: orders, invoices, service history
Not now, and why
- Machine-learning forecast of parts demand
- AI chatbot on the website
- AI summaries of meetings and email
In the Opportunity Map
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The map
Every candidate plotted on impact versus effort, flagged for data-readiness.
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The short list
Three to five use cases, each with effort, rough cost, and the data it needs.
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The no's, with reasons
The ideas you can stop discussing, and why.
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The starting plan
What to do first, what comes after, and who needs to be involved.
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The readout
A session with management and your tech lead where I defend every placement.
The Quickscan gives you the scorecard and debrief. The Execution Plan adds the roadmap, the deep data check, and the budget.
How an engagement runs
The same four steps, whether you choose the Quickscan, the Opportunity Map, or the Execution Plan. Only the depth differs.
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Explore
A kickoff workshop and short interviews with the people who know your processes best.
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Select
A use-case session where your own team surfaces the ideas. No imported best practices.
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Prioritise
Every candidate ranked and roughly costed. Enthusiasm alone does not get an idea onto the list.
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Activate
A readout with a concrete starting plan: what to do first, and what comes after.
A few questions clients ask
Isn't this just an expensive brainstorm?
A brainstorm gives you ideas. The Map gives you a prioritised, costed, data-checked plan, plus the no's. One avoided wrong bet pays for the whole engagement.
Why not use ChatGPT, or let our own team experiment?
Generic tools give generic ideas. They know nothing about your workflows or your data. I read your real processes, and I know under the hood what AI can and cannot do today.
What if our data turns out to be a mess?
That is a result, not a failure. It tells you exactly what to fix before you spend money on a use case that would have failed anyway.
We get the plan. Then what?
You own it, and your own people build it. If they need to get up to speed first, I train them. I stay on as sparring partner for as long as that is useful, and no longer.
Which AI opportunity is genuinely worth pursuing?
Book a short introductory call. We'll discuss where you stand, which questions are on the table, and whether one of these three is a sensible next step.
Book a free introductory callNo obligation, practical, and no sales presentation.
