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. Employees get three months to settle in. I get a kickoff call. 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 and often on competing directions. I organize the conversation around shared artifacts: journeys, job stories, service blueprints. A shared picture does what thirty slides cannot: enable decisions 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 real 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, not magic
I use AI throughout the process where it is demonstrably faster or better, and nowhere else. Three examples from practice:
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.
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.
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.
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.