My philosophy is that a successful user research program is built on a consistent cadence of inquiries that directly align with business and product objectives. This page covers how I do that in practice—and how I use AI as a co-researcher to strengthen rigor.
Research principles
This approach requires us to:
- Ground research in strategic goals – Ensure every study connects to key business priorities and product decisions, so insights directly inform strategy and drive measurable impact.
- Employ a variety of research methods – Draw from usability tests, interviews, surveys, web analytics, participatory design, and other approaches to answer different types of questions.
- Adapt our research to different scopes – Scale our methods appropriately, whether we're examining a specific feature, an entire application, or broader user needs and market opportunities.
- Blend various data sources – Combine attitudinal insights (what people say) with behavioral data (what people do) to build a complete, nuanced understanding of user experience.
- Actively involve cross-functional partners – Engage team members from product, design, customer experience, engineering, and other disciplines at every stage of the process to ensure shared understanding and commitment to action.
- Translate insights into action – Deliver research findings in clear, actionable formats that empower teams to make confident decisions and track how those insights influence product outcomes.
Augmented Rigor: The Co-Researcher Model
To enhance the depth and objectivity of my work, I treat Artificial Intelligence (i.e., generative AI tools) as a tireless "co-researcher" rather than a simple automation tool. My goal is not to work faster, but to leverage AI to challenge my own assumptions and ensure the highest quality of insights.
- Collaborative Design: During the planning phase, I use AI to stress-test my research instruments. I draft my interview guides independently, then task the AI with generating a parallel version based on the same objectives. This gap analysis surfaces edge cases and creative angles I might have otherwise overlooked.
- Comparative Analysis: For qualitative data, I conduct my own thematic analysis and then prompt the AI to perform its own independent analysis of the raw transcripts.
- Validation of Discrepancies: When the AI identifies a theme that differs from my own, I treat it as a critical inflection point. I return to the raw data for a secondary audit to validate the logic. This "triangulation" ensures that every finding in my final report has been vetted twice, minimizing researcher bias and ensuring no stone is left unturned.
I've begun codifying this approach in an open AI UX Research Toolkit, that stress-test research plans, interview guides, and synthesis at the moments where bias is most likely to creep in.