Approach
Enterprise UX is not a sprint towards a pretty screen. It is finding order in organizations with legacy, politics and experts who have done their work their own way for twenty years. This is how I approach that.
1. Build context in days, not months
I do not start at the screen but at the landscape. Which tools sit open on a desk, which data actually exists, who decides what. What I learn along the way I write down, so it stays after I leave.
2. Align stakeholders on substance
Large programs rarely fail on the design. They fail on internal politics. I organize the conversation around journeys, job stories, service blueprints. Something people can point at. After that the decision lands on substance instead of on the loudest voice.
3. Research where the work actually happens
In-depth interviews, shadowing, observation. With expert users the truth lives in the daily practice: the Excel next to the official system, the workaround everyone considers normal. Every design choice stays traceable to a quote or observation.
4. Validate with working prototypes
I do not deliver reports that end up in a drawer, but prototypes that settle the discussion. From wireframe to working, AI-built interaction: expert users only give valuable feedback when they hold something real.
5. Express value in risk and results
UX value in enterprise environments is often invisible: the malfunction that does not escalate, the sprint that does not go the wrong way. I make it measurable with baselines, OKRs and the language of the business: risk reduction.
Methodical foundation
No fixed method or framework: every instrument has its moment. This is the core of my toolbox and when I reach for it.
- IBM Enterprise Design Thinking (certified)
- for alignment in large programs: Hills, Playbacks and Sponsor Users to point everyone at the same outcome.
- Jobs-to-be-Done, job stories & story mapping
- to detach requirements from opinions: what is the user really trying to get done?
- Service design & journey mapping
- when the problem runs across teams and systems and nobody sees the whole chain.
- UX metrics (SUS, effectiveness, efficiency)
- to make improvement demonstrable, especially for tools people are required to use.
- Lean user research
- when validation has to happen fast, without a months-long research program.
AI in my practice: concrete, no hype
I use AI throughout the process where it is demonstrably faster or better, and nowhere else. Three examples from practice:
Knowledge that compounds
Per project I maintain a structured, AI-maintained knowledge wiki: every interview, decision and insight stays findable and connected. At the end, teams do not inherit loose files but a searchable project memory.
Synthesis that stays traceable
I turn interviews, feedback sessions and documentation into insights, personas and requirements with AI. The rule: every claim traceable to a source or quote. AI speeds up the synthesis, the evidence stays with the users.
Working prototypes in days
With AI tooling I build interactive prototypes with realistic data and interaction. At Eneco, traders could respond to a working version in the first weeks instead of to static sketches.
A complex system and experts who have to work with it every day?
I help organisations build tools that help people work smarter. From research to working design.
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