Summary — tl;dr
- GE Healthcare: global medical technology, 55K employees, 4MM installed devices, $20B revenue, serving healthcare providers & institutions
- In 2019 I stood up a UX team that launched the first AI platform at GE Healthcare in partnership with Data Science, Product, and Engineering
- I aligned product group and AI platform leadership on the value of UX & brand consistency for AI across products — foreshadowing Microsoft’s 2023 Copilot strategy
- I led, produced designs, and conducted research for a global cross-product UX effort that created the first AI design standards at GE Healthcare
- Our flagship AI solutions were based on these standards — our process won a DMI Value Award in 2020, and we patented a number of the designs
Overview & Situation
2 months to v1 · 6 months to v2 adoption.
Opportunities & Obstacles
To start, I drove a shared understanding with AI platform & product team leadership that a unified UX for AI across products would create more value for GE and our users.
The platform execs initially said design was up to the implementing product teams — the AI platform was just a service. However, after meeting with the leadership of all 23 product teams, I had broad commitment to adopt standards.
I used that product team commitment to gain agreement from the platform execs — and agreement that the platform org had the best cross-portfolio visibility for driving design governance.
A unified AI experience across GE products would create more value for the company — and far better outcomes for the healthcare providers who depend on it.
Discovery Research
To be effective in aligning teams and creating interaction standards, we needed to know how healthcare providers perceived the value of AI, how they wanted to engage with it, and how they currently used it.
My team led the first AI research at GE, with global healthcare providers in India, Europe, and the US across multiple healthcare institutions.
We learned that receptivity to AI, as well as visual & notification preferences, varied by region, hospital policy, and provider workload. We distilled findings into a simplified core workflow and a set of archetypes.
Based on this research I created the first draft of visual design, editorial, and interaction guidelines for use in an upcoming workshop.
Stakeholder Alignment & Scope
I led a 2-day workshop with worldwide Product and UX leaders, sharing our research, reviewing draft guidelines, and ideating on how to broaden and improve them.
We built a unified roadmap of AI features across product teams, prioritized next steps for design and research, then aligned on dates and expectations.
We published the first version of the AI standards on eds.gehealthcare.com, obtained broader company feedback, and began executing against the agreed plan.
Refine & Expand Guidance
Based on the agreed roadmap, my UX team and I led multiple design and research activities: a company-wide AI icon challenge, an AI visual design working group, and user research with radiologists.
Some of our design challenges were: showing AI results without obscuring the diagnostic image; representing AI globally without using the letters “A” & “I”; and aligning AI severity with hospital notification preferences.
Based on workshops, design ideation, and deeper research with radiologists, we broadened and refined our visual standards, notification guidance, and editorial approach.
Partnership, Launch, & Awards
