Making AI trustworthy by design, not by review
AI features were launching across many product areas, each moving fast but interpreting responsibility, trust, and user experience differently. The risk was fragmented AI behavior, inconsistent trust signals, and legal or reputational exposure.
If this failed: Speed would amplify risk, forcing reactive controls that slowed teams and undermined confidence in AI across the platform.
Teams lacked shared decision frameworks. Without them, speed created risk instead of advantage.
AI should be trustworthy by design, not by review. If we embedded clarity into how teams decided, we could scale AI safely without slowing teams down.
Defined enterprise AI experience principles and decision frameworks. Partnered deeply with product, legal, and engineering leadership. Focused governance on enablement over control. Said no to one-off exceptions that weakened consistency.
We empower customers to choose when and how to use AI, with clear identification, guidance, and responsible deployment support.
We design AI to reduce complexity and reflect diverse users, testing broadly to create equitable, accessible, and human-advancing experiences.
We clearly explain how AI works, its limits, and intended use, providing documentation and model clarity that enables informed choices.
We uphold trust through governance, oversight, and continuous improvement, partnering with experts to ensure responsible and accountable AI operations.
AI should match the user's moment — help them think, help them execute, or act on their behalf.
When the goal is unclear or decisions require reasoning. AI clarifies, guides, and explains.
When the task is known and structured. AI streamlines execution inside the workflow.
When autonomy is appropriate and permissioned. AI proactively triggers workflows — within guardrails.
High-stakes actions are always hybrid. AI explains. Humans confirm.
AI experience principles and evaluation criteria. Reusable governance frameworks adopted across 20+ teams. Clear guidance for conversational vs non-conversational AI. Decision trees and checklists that made complex decisions actionable.
Demo videos of platform experiences shaped by my team, crafted by business units to serve their unique customer needs.
Accelerated AI shipping velocity with fewer late-stage escalations. Improved consistency and trust across AI experiences. Reduced ambiguity for teams working in high-risk areas.
I would work much earlier and more closely with engineering using rapid prototyping to see true model output sooner, bringing data science and ML partners in at the right moments. This would be supported by executive-backed roadshows and an AI Design Ambassador network to spread these process shifts faster across teams.
Weekly rapid prototypes with real model outputs instead of mocked states. Live design reviews with engineering partners, with ML and data science present when model behavior shaped experience decisions. Early executive walkthroughs to align on tradeoffs before teams scaled.
This case demonstrates my ability to turn AI risk into an actionable strategy that teams actually use—not theoretical principles, but practical frameworks that accelerate shipping while increasing trust.
