Redesigning Microsoft's product recommendation experience — evolving a fragmented quiz into a unified, Copilot-assisted decision system across Windows and Surface ecosystems.
The Problem
Windows and Surface each had their own "Help Me Choose" flow — separate interaction models, separate recommendation logic, separate teams maintaining them. One used rigid decision trees; the other, filter-based profiling. Neither produced confident users.
The deeper issue wasn't information — users had access to specs and comparisons. The issue was confidence. Users couldn't tell if they were making the right decision, and the experience didn't help them feel like they were.
Duplicated internal effort across Windows and Surface teams
Inconsistent interaction models made the system feel unreliable
Cognitive load front-loaded before users understood the trade-offs
AI guidance felt opaque — users couldn't tell why they got a recommendation
No reassurance layer — confident users got the same experience as confused ones
Impossible to scale to a unified system without a shared foundation
My Approach
Rather than starting with UI, I focused on understanding how people make technical decisions under uncertainty. The reframe: from "help users compare products" to "help users feel confident moving forward." That shift changed almost every subsequent design decision.
I worked with the data team to map the taxonomy and logic powering recommendations — activities, preferences, environments, product attributes. Understanding this system was prerequisite to designing a UI that reflected it honestly.
Rather than designing isolated flows, we built a structured taxonomy that could map user intent consistently across products, surfaces, and future recommendation experiences. The design and data architecture were designed together.
Every interaction was designed so the AI supported user judgment rather than replaced it. Recommendations remained editable, explainable, and transparent throughout.
Key Decisions
Interaction Model
We moved away from rigid step-by-step forms toward a more adaptive, conversational structure. Users needed the experience to feel supportive — not technical. Each question was designed to feel like a natural next step, not a requirement.
Architecture
Instead of designing isolated recommendation flows, we created a scalable taxonomy system that mapped user intent consistently. This meant making design and data architecture decisions jointly — unusual but essential.
AI Role
We designed AI to surface reasoning, not just results. Every recommendation was explainable — users could see why they were being guided toward an option and change their inputs if it didn't feel right.
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Outcome
The experience was still in concept and validation stages, but usability testing and stakeholder reviews showed consistent, directional signal.
Decision Flow
Users moved through decisions more naturally without requiring step-by-step guidance — the conversational structure reduced perceived complexity.
Cognitive Load
The single adaptive scroll reduced cognitive load compared to traditional multi-step flows — users reported feeling less overwhelmed.
AI Trust
Transparent reasoning helped users trust recommendations — they understood why they were being guided, not just where.
Internal Alignment
The shared taxonomy created stronger cross-team alignment and established a scalable foundation for future AI-assisted experiences.
Reflection
"Start with the taxonomy before the first screen. The data architecture is the design."
The most valuable work on this project happened before any UI was designed — mapping the underlying recommendation logic and building a shared vocabulary with the data team. In hindsight I'd push even harder for that structural work upfront, earlier in the project.
I'd also advocate for more diverse user testing earlier — specifically users across the full spectrum of technical familiarity, not just the middle. The experience behaved very differently at the extremes.
Want to see the full picture?
There's a lot more to this project — detailed flows, research synthesis, interaction specs, and design system components. If you'd like to see how it all comes together, I'd love to walk you through it.
See the full processUsually reply within 24 hours · manalishah19691@gmail.com