Rupa Chaturvedi

Head of Merchant & Ads Design at Uber, Adjunct Lecturer at Stanford University

I’m a UX Design Leader and AI/GenAI specialist who blends 0→1 invention with platform-scale delivery. I build high-performing global teams, architect systems and information frameworks, and turn ambiguous problems into durable products that move KPIs.Highlights:- 0→1 to v2 at scale: From concept to launch to systematized patterns—GenAI assistants, video discovery, conversational shopping, AR/3D visualization.- Platform UX and IA: Unifying fragmented surfaces into responsive, multi‑platform systems; shaping primitives for workflows, analytics, and monetization.- Design org building: Scaling diverse, distributed teams; codifying operating cadences; partnering with research and content; aligning roadmaps to outcomes.- AI design enablement: Equipping UX orgs to design AI responsibly—expanding influence beyond UI via model evaluation, systems thinking, AI‑first mental models, and AI workflows.Selected outcomes:- Built a 14-person global design org across 3 continents; shipped 80+ features in under 5 months; consolidated a multi‑platform system; meaningful KPI impact.- Incubated 0→1 GenAI initiatives (consumer LLM assistant, Merchant Copilot, AI creative tools, video discovery)—adopted by SMBs and enterprises globally.- Scaled a conversational shopping product from 0→1 to 1M WAUs in under 6 months; 20+ patents granted/published; launched AR “View in Your Room/AR View” with press coverage.- Taught and enabled 1,500+ professionals via a human-centered AI institute; consistent 5/5 ratings; partners include Reforge, Stanford, Maven.Today, I focus on platform UX, AI-assisted experiences, and design org development—bringing product strategy, systems thinking, and responsible AI to enterprise and SMB workflows.

UX Design Manager

Over 4000 people signed up for my talk on Designing for Trust in AI Agents yesterday.

As the industry continues to dig deeper on what AI agents can do, the question of whether users will actually trust them to do it seems to be top of mind for many of us product builders.

I've been experiencing this firsthand. I've tested Claude Cowork, Cora, Manus, and a handful of other agents over the past few months. They're fast. They're thorough. And I keep finding myself unable to use what they produce.

The pattern I keep landing on: we've designed agents to optimize for speed to output instead of speed to understanding. The industry celebrates execution time. Users care about whether they can actually do something with the result. Those metrics have almost nothing to do with each other.

I wrote up the core ideas from the session in this week's newsletter. It goes deeper on where the trust gap actually lives and why the usual mechanisms (transparency, guardrails, reversibility) don't solve the underlying problem.

If you'd like to watch a replay of the talk, comment AIUX and I will send you the recording link.

Design & AI Leader | Stanford Lecturer | AI enablement for UX orgs | ex-Uber, Google, Amazon

This is the AI UX Brief - my weekly newsletter on designing intelligent systems, leadership in the AI era, and navigating the messy reality of AIUX. F

Open article: The Speed Paradox: Why AI Agents Feel Impressive But Unusable by Rupa Chaturvedi • 7 min read

The Speed Paradox: Why AI Agents Feel Impressive But Unusable

Anne T. Griffin

AI Product Leader and Product Futures | I help teams move from ambiguity to shipped AI products and real competitive advantage | Teaching AI for Pearson on O’Reilly and writing about AI in Forbes

I help companies pinpoint where AI can create defensible advantage and help them move faster than the market, not just keep pace. I focus on shipping AI products that deepen retention, drive account expansion, and become meaningfully hard for competitors to replicate. 

I work with founders and product leaders who need someone who can operate with high agency: someone who can walk into ambiguity, talk to customers, understand data and technical constraints, and turn that into a product direction the company can actually ship. 

What this actually looks like in practice: 
- Identify the specific workflows, pains, or decisions where AI can create real leverage
- Validate through lightweight prototypes before major engineering investment
- Ship capabilities that meaningfully create value for customers

How I approach the work:
- Research the market, competitive landscape, and emerging alternatives to understand where the space is moving
- Apply futures practices to identify where AI can create durable, long-term advantage
- Analyze the company’s value chain to pinpoint where AI can amplify strengths or remove friction
- Work directly across product, engineering, data, design, and GTM so alignment happens during the build, not after
- Reduce cognitive load for founders and teams instead of adding to it

I have 14+ years of experience building products in 0 to 1 startups and in Fortune 500 giants in multiple industries, including AI and blockchain. I've also served as interim Head of AI / Data Science operations for a scaling startup. I've worked across B2C, B2B2C, and B2B SaaS. Translating strategy and technical topics into a message people can understand is one of my super powers, and that has given me the opportunities to guest lectures at Columbia and West Point.

I also teach Jumpstart Your AI Career for Pearson on O'Reilly. I love exploring the realities and possibilities of emerging tech in my writing as a Forbes Contributor on the topics of AI and Strategy. My insights on AI and tech have been highlighted in Rolling Stone and The Atlantic. 

My product blog: https://productmindlab.substack.com/
My Talks: https://www.annetgriffin.com/talks-lectures

Senior Product Manager

- Lead product vision and strategy for a AI/ML product for a cross-functional team to rebuild customer trust and mitigate risk of churn
- Increased ARR by $480,000 by leading a team to build self-serve features on the core product experience, removing the dependency to onboard with the customer success team
- Owned the roadmap and prioritized key initiatives to increase revenue and enable new generative AI product
- Established user-centric practices with continuous discovery user research, implementing with ideal customer personas, using insights to de-risk and prioritize new features and key investments sharing insights
- Developed best practices for cross-functional collaboration with marketing, sales, engineering, design, and customer success
- Mentored a junior, first-time product manager team member, accelerating their growth to a promotion

Anne T Griffin

Update: I have enough people to talk to now. Thank you to everyone who reached out or tagged people. 🙏🏽

——

Looking to speak to two types of people for one of next Forbes pieces:

- Business owners using Clawdbot or Claude code in their business 

- Leaders in companies who use Clawdbot and Claude Code extensively outside of work but are not allowed to in their day job

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