Awkward Silences

This week we're revisiting one of Awkward Silences' most memorable episodes.
Much of the conversation around AI in UX involves how it will shape and change researchers' work. This episodes's guest, Jess Holbrook, who leads UX research for Microsoft AI, thinks a lot about how this technology can be improved with design and UX best practices. Jess has spent over a decade working on the UX of AI at companies like Meta and Google. He's also helped develop foundational principles for the ethical use of LLMs.
Jess joined Erin and Ben to reframe the AI conversation, focusing on how UXRs can and should adapt, what working on these systems has taught him about the UX discipline, and his philosophy for building teams that are ready to take on the innovative challenges of tomorrow.
Jess's recommendations: Matt Webb, Maggie Appleton, and the Pessimists Archive Newsletter.
Highlights
07:15 Human-Centered AI Evolution
17:53 Evolving AI Forecast Strategies
24:04 Diverse AI Team Building
33:27 Navigating Responsible AI Challenges
37:01 Qualitative Analysis with AI Tools
40:15 Optimism and Concerns About AI
About Jess
Senior Director of Research specializing in human-centered AI. Early stage investor in AI, AI hardware ecosystems, climate, and anything that seems like the world would be better with it in it. Currently leading UX research for Microsoft AI. Previously led the Generative AI team and supported all UX functions for the Responsible AI and data transparency teams at Meta. I also served on the Board of Forging Youth Resilience, a nonprofit that empowers young people to build physical and mental strength for life by providing access to a national network of community-based fitness programs and mentorship.
Resources
The AI in UX Research Report
20+ Tools for Every Research Phase
Using AI in UX: A Day-in-the-Life

Erin May sits down with Alec Levin, CEO and Co-Founder of Learners, to explore how researchers can navigate the rapid changes reshaping their field. Alec shares his vision for how AI and automation are creating unprecedented opportunities for research teams willing to step up and lead.
Alec argues that while AI can handle routine tasks like usability testing and surveys, researchers have a chance to focus on higher-value discovery work that includes prototyping and coding. He emphasizes that this is a critical moment for researchers to define their own future rather than letting executives decide their role. The conversation covers practical examples of AI-powered research workflows, the importance of cross-functional collaboration, and why researchers need to be more proactive in pitching their vision internally.
Highlights
03:40 Balance sheeting thinking for change
06:15 Connecting researchers through community
13:11 Discovery research importance 
15:02 Vision needed for researcher evolution
24:08 Automated research workflows emerging
31:16 Human elements AI can't replace
Resources
AI for User Research 101 Workshop
What is the New AI in Research Risk Cascade?
Assessing the ROI of AI in UX Research Webinar
AI Context Engineering for Research Course

This week we're revisiting one of Awkward Silences' most memorable episodes.
This episode welcomed Ruby Pryor, founder of Rex, a consulting firm specializing in UX research and strategic design.
This episode gets into what it means to demonstrate the "business value" of one's work, specifically how researchers can and should quantify their impact in terms that are tangible to the company. Ruby shared ways to score early wins, build momentum, and overcome communication frictions to find shared value.
Episode Highlights
03:14 - UX researcher impact: insights, optimization, prioritization, strategy
09:14 - Understanding organizational structures and strategy development collaboration
20:22 - Challenge of quantifying impact and strategic level
21:33 - Measuring strategy impact: challenges and indicators
30:04 - Prioritizing investments based on strong market indicators
38:51 - Stakeholders prioritize growth, revenue, and cost reduction
About Our Guest
Ruby Pryor is the founder of Rex, a service design and UX research consulting firm. Her previous roles include UX research at Grab, strategic design at Boston Consulting Group and management consulting at Nous Group. She has taught courses on increasing the impact of UX to learners from 5 continents and has spoken about design and UX at conferences in Asia and Europe.
Resources on UX Research Impact
How to Track the Impact of Your UX Research
Leading an Impactful User Research Team
Use Stakeholder Interviews to Drive Impact

Erin May sits down with Sam Ladner, Senior Principal Researcher of Strategy at Workday, to explore the evolving role of AI in qualitative research. Sam brings a refreshingly balanced perspective on where AI can genuinely help researchers and where it fundamentally cannot replace human insight.
Sam explains how AI has transformed labor-intensive tasks like transcription and closed coding, freeing researchers to focus on the deeper work of sense-making and understanding outliers. She emphasizes that while AI excels at mathematical correlation hunting, qualitative research is about unriddling complex human experiences that require thinking, feeling, and imagination. The conversation covers practical applications like using MAXQDA for AI-assisted coding, the importance of explaining every outlier in qualitative work, and why emotional storytelling must remain exclusively human territory.
Highlights
03:09 Why machines can't replace human sense-making
08:47 Human moments AI cannot understand
12:00 Explaining outliers in qualitative coding
16:27 Building effective coding systems for AI
21:33 Getting AI to do closed coding properly
29:26 When to use AI for low stakes research
37:23 AI cannot substitute for qualitative research
About Sam
Sam Ladner (she/her) is a sociologist who helps teams innovate, design, and learn. She is the author of Practical Ethnography: A Guide to Doing Ethnography in The Private Sector and Mixed Methods: A Short Guide to Applied Mixed Methods Research. She has worked on dozens of advanced software projects at Microsoft, Amazon, and most recently Workday, where she worked as a Senior Principal Researcher studying the future of work. She is now an independent researcher and consultant, writing her third book, tentatively titled Practical Foresight: Strategic Foresight in Applied Settings. She received her PhD in sociology from York University and lives in Toronto, Ontario, Canada.
Resources
Practical Ethnography: A Guide to Doing Ethnography in The Private Sector
Mixed Methods: A Short Guide to Applied Mixed Methods Research
Qual vs Quant vs Mixed-Methods Field Guide
Qualitative Sample Size Calculator

This week we're revisiting one of Awkward Silences' most memorable episodes.
This episode welcomed George Whitfield, an expert in applying AI to the analysis of qualitative data. George discussed the intricate challenges of leveraging language models to interpret expansive open-ended data (like interview transcripts), and emphasized the importance of context and not just keyword or topic identification.
Episode Highlights
03:48 - Innovating consumer insights using AI
12:21 - Importance of human involvement in AI tools
20:04 - Enhance discussion sections with  AI tools
26:50 - AI-inspired insights provide inspiration, not guidance
34:12 - Interpretation beyond analyzing transcripts
36:46 - Applying engineering rigor to the process of building a business
About Our Guest
George Whitfield is an Entrepreneur-in-Residence at the Martin Trust Center for MIT Entrepreneurship, Lecturer at the MIT Sloan School of Management, and Software Engineer at Google. George holds 4 patents and has 3 degrees from MIT including a Bachelors in Electrical Engineering and Computer Science and a Masters and Ph.D. in Materials Science and Engineering.
Resources on Qual Data and AI
Our AI in UX Report shares findings from a survey of over 1,000 researchers
What does it mean to "code" qualitative data? This breakdown explains it all.
Interested in trying an AI tool for your analysis? Here are 20 worth considering.