CASE STUDY · ATOS
Making 800+ AI solutions easier to find, and easier to sell.
The challenge
As the AI portfolio grew to 800+ use cases, solutions, demos and case studies, finding the right one for a client conversation got harder. Sellers started with a business problem, not the name of an asset.
Project outcomes
Role and responsibilities
Discovery
Interviews and workshops showed people weren’t searching for an asset by name. They arrived with questions like these.
“Markian’s UX/UI work played a pivotal role in shaping the front end of the Navigator website, which is now one of the most popular platforms within our company.”
Solution overview
Navigator gave sales and consulting teams three complementary ways in, all backed by one scalable information architecture.
Design decisions
What I did. Built navigation, taxonomy and IA around business context: industry, business problem, AI capability, use case and solution.
Why it mattered. Users rarely knew an asset’s name. They started from a client’s problem.
What I did. Improved search, filtering, result organization and content hierarchy so users could narrow hundreds of resources progressively.
Why it mattered. Sellers often had minutes before a conversation, not hours for research.
What I did. Helped design a natural-language assistant that works alongside structured search and navigation, with contextual results users can move between.
Why it mattered. AI should complement structured discovery, not hide the system behind a chatbot.
What I did. Established scalable IA and reusable patterns so new industries, use cases, solutions and capabilities could be added without reorganizing the experience.
Why it mattered. The library kept growing. A fixed structure would have made discovery worse over time.
Reflection
[Two or three sentences: what you learned about designing AI discovery for business users, and what you’d do next.]