AI Product Design for Healthcare and Enterprise Teams
AI product design is the work of deciding where AI belongs in your product, designing how it works for the people who use it, and staying with it until it ships.
Adding AI is easy. Most teams can wire a model into a product in an afternoon. The hard part is everything around the model: which problem it should solve, what the person on the other end sees, when a human stays in control, and how you know it is working. That is the job, and it is the work I do with product teams through Pineapple Blueprint.
What AI product design actually covers
I break the work into three parts, and each one stands on its own.
- Map where AI belongs. Find where AI actually relieves a bottleneck in the workflow, not where it is easiest to bolt on.
- Design how it works. Shape the interaction so people can see why the AI made a recommendation, check its sources, and step in when the stakes call for it.
- Guide it until it ships. Stay through the build cycles, hold the design standards as you scale, and measure adoption rather than output.
Why AI features stall after launch
Most AI features exist because the model could do it, not because the user needed it there. Nobody owns the decision of where AI belongs, so features ship, usage stays flat, and the team is left guessing why.
The pattern I see most often is a trust problem dressed up as a technology problem. People will not act on AI they cannot check. When the product shows its reasoning and keeps a person in control at the moments that matter, adoption follows. Good AI product design builds that in from the first screen instead of patching it in after launch.
What this looked like at Walmart and Shopify
Before founding Pineapple Blueprint, I led experience design at Walmart and Shopify, and before that at Illumina and Intuit. Those outcomes belong to those roles, and they shaped how I approach AI product design today.
At Walmart, I led design for W+ ecommerce benefits and for LocalFinds. On W+, the work was retention and engagement: making membership benefits feel valuable and part of people's routines. LocalFinds was a new effort to bring local merchants into Walmart ecommerce and delivery, and I unified 14 product teams around one consistent experience. I worked on the prompt engineering behind AI-generated merchant branding, logos, and product descriptions in seller onboarding. That AI shipped live: setup efficiency improved 27% and store setup got 10 times faster. Across the work, adoption rose 19% and retention rose 27%.
At Shopify, I led design for merchant-facing Online Store surfaces. We built a theme customization framework around three kinds of merchants (no-code, code curious, and advanced coder), so each one got the right level of control. Discovery and engagement rose 58% in A/B testing, and 62% of merchants launched faster.
Over that stretch, shops using themes grew from 2.5 million to 3.54 million, and the time to customize or add new features dropped from 6 weeks to 2. AI-generated product descriptions helped merchants launch with the right content and layout, cutting placeholder content from 8% to 1%.
Both stories point at the same lesson: design for the real person and their real skill level, and AI becomes something people use instead of something they work around. More detail is on Outcomes.
AI product design in healthcare
My most recent work has been in healthcare and commerce. In healthcare, that means patient experience, self scheduling, and clinical workflow design. Those results publish on this site once they are measured, the same as every other claim here.
Healthcare raises the bar. Decisions carry real consequences, workflows are regulated, and clinicians have no patience for tools that waste their time. That is why I design friction in on purpose at the decision points that matter, and remove it everywhere else. I wrote about that approach in Friction Is a Feature.
How an engagement runs
Every engagement starts with the map. A two-week diagnostic gives you a ranked list of AI opportunities and a plan for the first one. Adoption System Design then designs and proves that experience over four to ten weeks. Teams shipping AI continuously keep me on retainer for ongoing strategy and design as the landscape shifts.
Frequently asked questions
What does an AI product designer do?
An AI product designer decides where AI belongs in a product, designs how people interact with it, and guides the team until it ships. The focus is on whether people adopt it and trust it, not on whether the model can technically do the task.
How is AI product design different from regular product design?
Regular product design assumes the software behaves the same way every time. AI does not. AI product design adds the work of showing reasoning and sources, deciding where a person stays in control, and designing for the moments when the model is uncertain or wrong.
How long does an AI product design engagement take?
Most start with a two-week diagnostic that maps where AI should pay off first. Designing and proving the experience usually takes four to ten weeks. Teams that ship AI continuously keep ongoing design leadership on retainer.
Do you work with healthcare companies?
Yes. My most recent work has been in healthcare and commerce. In healthcare, that includes patient experience, self scheduling, and clinical workflow design.