How I unified OneTrust's fragmented component ecosystem, embedded Pendo analytics into the design process, and led the organization's first AI-driven prototyping initiative — positioning the platform for the next generation of enterprise UX.
OneTrust is one of the world's leading trust intelligence platforms, serving over 14,000 customers globally. When I joined as Senior Principal UX Design Lead in June 2024, the product suite had grown rapidly — but the design infrastructure hadn't kept pace. Components were inconsistent across product areas, design tokens were undocumented, and every new feature sprint introduced friction between design and engineering.
The stakes were high: a fragmented design system doesn't just create visual inconsistency. It compounds across every team, slowing velocity, eroding brand trust, and increasing the cost of every single product decision.
"Without a single source of truth, you don't have a design system — you have a collection of opinions. My first mandate was to transform that collection into infrastructure."
Before prescribing solutions, I needed a complete picture of the system as it existed. I led a full component audit across all active product suites — cataloguing every UI element, documenting inconsistencies, and cross-referencing with Pendo behavior data to understand which components were most frequently interacted with and most frequently causing friction.
This Pendo-powered prioritization was a deliberate strategic move: rather than letting subjective stakeholder preference drive the roadmap, I anchored every design system decision to real user interaction data.
The goal was never just to make things look consistent. The goal was to build a system that teams could govern, scale, and trust. This required a structural approach — one that codified decisions as tokens, established clear ownership, and connected the design system directly to the engineering workflow via Storybook.
Beyond the design system, I was charged with positioning OneTrust at the forefront of AI-driven product design. This meant more than experimenting with tools — it meant building a repeatable, scalable AI prototyping framework that the wider design organization could adopt.
I pioneered the integration of AI at both the rapid prototyping phase and the final production rollout. Using Figma Make and LLM-Guided UI approaches, I created workflows that dramatically reduced the time from concept to testable prototype — allowing earlier validation loops before engineering resources are committed.
The real value of AI in prototyping isn't speed for its own sake — it's the ability to test more hypotheses before any engineering investment is made. We can now validate 3–4 interaction patterns in the time it used to take to build one.
The results of the design system overhaul and AI initiative are measured not just in design metrics, but in the organizational change they enabled. A governed, data-informed design system changes how teams make decisions — and that impact compounds over time.
If I were starting this engagement over, I'd invest even more heavily in stakeholder alignment before the audit phase began. Getting every product squad to agree on what a "component" means — before you start cataloguing them — saves significant rework downstream. We course-corrected, but earlier alignment would have bought us more time for the governance model.
What I'd absolutely do again: anchoring every prioritization decision to Pendo data from day one. In complex organizations with many competing stakeholder voices, objective interaction data is the most effective tool for cutting through opinion and building cross-functional consensus around design investments.