Product & AI leadership · The Netherlands
I shape product strategy and lead AI transformation.
I connect strategy, teams and technology to build useful products and put AI to work on real business problems.
I’ve led product portfolios supporting €150M+ in annual revenue across gaming, commerce and media.
Where I work
Most of my work sits at the intersection: product, AI, the organisation and its systems. Select a discipline to see what it covers.
Deciding what is worth building, and building the teams that build it.
- Product strategy
- Portfolio strategy
- Outcome-based roadmaps
- Discovery
Finding where AI creates value, then redesigning the work around it.
- AI readiness
- Opportunity identification
- AI strategy
- Workflow redesign
Commerce, customer data and experimentation at enterprise scale.
- E-commerce
- Personalisation
- CRO & experimentation
- Search
AI products built on enterprise knowledge, with evaluation designed in.
- LLM applications
- Retrieval-augmented generation
- Hybrid search
- Semantic retrieval
Selected scope and outcomes
Selected work
From AI practice to products at scale.
Four examples across practice building, proposed architecture and measurable product outcomes. Each shows the stage the work reached.
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Building an AI Solutions practice
25+ clients served in my WPP role
Leading the development of an AI Solutions practice that connects product strategy, transformation and delivery, starting from organisational problems rather than models.
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Scaling global digital commerce
30% conversion uplift
Led digital product across global commerce, customer data and marketing technology at G-Star RAW, delivering 30% conversion uplift and 30% digital revenue growth through behavioural data and experimentation.
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From engineer to founder: scaling products to millions
5M+ users across three products
Co-founding Ewalk Entertainments and leading product and engineering across three products with more than five million users.
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Designing a trusted enterprise AI answer layer
Turning fragmented content, product data and enterprise systems into one governed intelligence layer for people, search and AI.
Point of view
The hard part of AI transformation isn’t accessing intelligence. It’s redesigning products, workflows and organisations around it.
Too many AI initiatives start at step five.
Value is created across the whole chain. The model is one link, and rarely the one that breaks.
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Why
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Business problem
What is worth solving?
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User need
Who has the problem?
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What it runs on
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Data
What do we know?
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Workflow
How does the work happen?
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AI capability
What can intelligence add?
Too many start here
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How it is used
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Product experience
How will people use it?
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Human oversight
Who stays accountable?
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Whether it worked
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Adoption
Is it actually used?
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Measurable outcome
Did it matter?
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Enterprise AI is a stack, not a model.
Value is decided above the model, in workflows and products. Reliability is decided below it, in data and platforms.
Spanning every layer: Governance, security & evaluation
- Business outcomes Revenue, cost, risk, experience
- Products & experiences Where people meet the capability
- AI-enabled workflows How the work itself changes
- Agents & applications Software that acts within the workflow
- Models · Retrieval · Tools Intelligence, grounded and connected
- Enterprise data & knowledge What the organisation knows
- Platforms & infrastructure Where it runs, and at what cost
Terms that get used interchangeably
Select a term to see where it sits in the stack.
Generates and predicts. Powerful and general, and on its own not a product.
Grounds a model in your own knowledge. Answer quality depends on the data more than on the prompt.
A designed sequence of steps, some of them intelligent. Predictable and auditable.
A model or workflow packaged for a person with a job to do.
Chooses its own next step and calls tools. Useful when the path cannot be scripted, risky when it can.
Several agents coordinating on one task. More capable, and much harder to evaluate.
What makes all of the above repeatable, governed and affordable across an enterprise.
From AI experiments to repeatable capability.
Start with outcomes and workflows, choose the minimum sufficient technology, prove the value, then scale what works.
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Direction
Outcomes, priorities and boundaries.
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Discover
Workflows, decisions and friction.
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Prioritise
Value, feasibility, risk and reuse.
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Design
Target workflow, UX, controls and architecture.
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Prove
Value, quality, adoption and cost.
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Scale
Productise, reuse and continuously improve.
A few things I have learned to insist on.
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Start with the problem, not the technology.
An LLM is not a strategy.
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Discovery before delivery.
The expensive mistake is usually building the wrong thing well.
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AI changes workflows, not just interfaces.
The better question is rarely “where can we add AI?” It is “how should this process work if intelligence becomes cheap?”
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Human expertise is part of the architecture.
In many enterprise systems, human-in-the-loop is a feature, not technical debt.
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Adoption is part of the product.
A proof of concept that nobody adopts creates no value.
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Architecture should preserve options.
Avoid unnecessary model, vendor and platform lock-in.
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Measure outcomes, not output.
Features shipped are not business value.
Career
From writing code to leading product and AI transformation.
Each move expanded the scope — from code to products, businesses, platforms and AI transformation — without losing the technical layers underneath.
UVECO Full-stack Web Developer Full-stack engineering
Parspake Digital Solutions Senior Product Owner & Technical Lead Connecting commercial needs, product and engineering
Ewalk Entertainments Co-founder, Head of Product & Engineering Building products, teams and a business
In-Game Group Head of Product Growth MENA Growth, monetisation and portfolio strategy
G-Star RAW Digital Product Lead Commerce, data, customer experience and platforms
Keesing Media Group Head of Digital Products Multiple digital products and organisational leadership
WPP Head of Product Management and AI Solutions Product capability, portfolio leadership and AI transformation
Education
MSc Business Informatics / Data Science