Approach

I'm a UX researcher with seven years of experience working across AI/ML platforms, enterprise software, and complex system design. I'm drawn to the early-stage, unwieldy questions: like how human users and AI systems can work together meaningfully and how interfaces can make black-box processes more legible.

My practice is mixed methods with a qualitative lean: interviews, surveys, usability testing, and concept validation, but always with an interest in being face-to-face with people. I've worked across system admins, data scientists, service desk agents, and executives at companies from tiny startups to large enterprises, and I've led research on everything from prompting behaviors to trust boundaries to onboarding flows to security and governance frameworks.

I believe research should be rigorous without being rigid, especially when the systems we're designing for are still figuring out what they're supposed to do.

Projects

AI Agent Studio (ServiceNow, 2025-ongoing)

At ServiceNow, I lead UX research for AI Agent Studio, conducting end-to-end research across onboarding, prompting, versioning, and governance workflows to shape how medium to large enterprises build, test, and govern Agentic AI with confidence.

Case Summarization (ServiceNow, 2023-2024)

I conducted foundational research (interviews and surveys) on how service desk agents summarize support cases with AI, with insights that directly shaped the case summarization feature in Now Assist.

Task Intelligence (ServiceNow, 2022-2023)

I led UX research across the agent and admin experiences for Task Intelligence (ServiceNow's AI capability for automated task creation, triage, and routing) with insights that informed improvements to explainability, confidence score displays, and usability for both roles.

Document Intelligence (ElementAI)

Owned UX research for Document Intelligence (AI tool that automatically extracts structured data from documents like receipts and PDFs). I uncovered trust and explainability gaps around confidence scores that directly informed design changes improving review accuracy, user control, and adoption.

Toolkit/Data Labeller (Element AI)

I conducted user interviews and literature reviews on collaborative internal data labeling tools, focusing on how teams navigate ambiguous inputs and disagreement during annotation.

Publications

Investigating Interaction Friction in Generative AI: Improving User Experience and Decision-Making (SIGCHI, 2025)

A published academic paper co-authored with HEC Montréal researchers examining how introducing deliberate "interaction friction" into generative AI chatbot interfaces affects user trust, agency, cognitive load, and decision-making — finding that a moderate level of friction (chatbot plus summary) outperformed both frictionless and high-friction designs on perceived usefulness and sense of agency.

Writing

UX Matters (2025)

A six-part series for UXmatters exploring how UX research must evolve for the era of generative AI, covering research methods, interview techniques, human-centered design principles, and what it means to build AI experiences that genuinely serve people.

Data Science Alliance (2024)

A co-authored industry white paper examining 18 months of generative AI advancements since ChatGPT covering real-world applications, ethical considerations, responsible AI principles, and the role of UX as a bridge to human-centered AI development.

Speaking Engagements

ServiceNow Knowledge, Las Vegas (2025)

“Putting AI Agents to Work: A Framework for Designing AI Agent Personas”
A talk introducing a persona-based framework for designing AI Agents: treating them as intentional workers with defined goals, constraints, and success metrics rather than reactive tools, with criteria for identifying where Agentic AI adds value over standard automation.

NOW AI Conference, Santa Clara (2025)

“The Role of UXR in Building the Future of Feedback Mechanisms”
A co-presented talk with a Senior Linguist from ServiceNow's internal data annotation team exploring how UX research can bridge the gap between annotation workflows and real user needs, examining where traditional annotation misses crucial context and demonstrating research methods for capturing richer, more nuanced feedback that leads to better-trained, more human-centered AI models.

Learners UX Conference, San Diego (2024)

“Generative AI & UX Research in the Enterprise”
Talk & roundtable exploring how UX research must adapt for enterprise generative AI: where the stakes of shipping early, imperfect technology are far higher than in consumer contexts and user expectations for consistency and reliability leave little room for error.

UXC, Montreal (2023)

“From Jackpots to AI Algorithms: The Role of Human-Centered Design in Navigating Unpredictability”
A co-presented talk with Di Le (AI ethicist and design strategist) drawing parallels between generative AI and slot machines to argue for "controlled friction" as a UX design principlem empowering users to navigate AI's uncertainty rather than obscuring it behind seamless, streamlined experiences.