Awkward Silences

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.

Erin May sits down with Aneta Kmiecik, founder of Be Your Own Design Team, to tackle one of the biggest questions facing designers today: how to position AI skills in portfolios and resumes. With 15 years in creative industries and a community of 200,000 followers, Aneta brings unique insights into navigating career strategy during this AI transformation.
Aneta reveals her Past Present Future framework for career positioning and explains why most companies aren't explicitly requiring AI skills yet, but smart candidates should still showcase them strategically. She discusses the three designer archetypes emerging at AI native companies and shares practical advice on standing out when AI can generate portfolios and case studies for everyone.

CHAPTERS
00:00 Intro
04:06 AI Skills in Job Descriptions Reality Check
06:52 Research Strategy for AI Portfolio Positioning
14:23 Audience Context Determines AI Skill Positioning
17:49 Past Present Future Portfolio Framework
27:10 Common Portfolio Mistakes in AI Era
35:46 Future of AI Skills in Applications
38:39 Favorite Research Interview Techniques
RESOURCES
Lenny’s Podcast featuring Jenny Wen, Design Lead for Claude - https://www.youtube.com/watch?v=eh8bcBIAAFo
2024 AI in User Research Report - https://www.userinterviews.com/ai-in-ux-research-report 
CONNECT WITH ANETA KMIECIK:
- LinkedIn: Aneta Kmiecik
- Instagram: @ux.aneta
- Newsletter: Be Your Own Design Team
- Portfolio Course: uxportfolio.co

Ben Wiedmaier sits down with user research consultant Nikki Anderson to explore how researchers can stay relevant when budgets are tight and stakeholders are stressed. Nikki runs Drop in Research where she helps teams conduct research, facilitate better meetings, and build research practices from the ground up. She shares how companies are increasingly coming to her with validation requests rather than discovery work, driven by pressure to ship fast and hit numbers. The conversation reveals practical strategies for mixed methods research, using facilitation to drive action from insights, and leveraging AI tools without compromising research quality.
Nikki explains why she creates forcing functions to slow stakeholders down when they want to rush into AI features without understanding the problem. She advocates for parallel convergent research design where surveys, interviews, and secondary research happen simultaneously to deliver insights faster. The discussion covers why synthesis remains a bridge too far for AI, how to turn research presentations into collaborative workshops, and specific ways researchers can demonstrate value through facilitation skills.
Highlights
00:00 Intro
05:20 Mixed Methods and Data Triangulation
10:07 Finding Small Tests When Teams Won't Wait
14:46 Stakeholder Stress Driving Validation Requests
23:30 Facilitation as Core Research Skill
40:03 Using AI for Research Planning
45:39 Demonstrating AI Skills to Stakeholders
Resources
- AI for User Research 101 Course
- 30+ Tools for every phase of UX Research
- AI Context Engineering for Research Course

Ben talks with Kristen DeLap, fractional COO at Econify, to explore how UX'ers can build influence without formal authority. Kristen brings a unique perspective, having evolved from interior design to leading product teams at Herman Miller and now working as an operations executive. She shares practical strategies for understanding decision-making processes, translating design work into business impact, and creating the conditions where teams do their best work.
They also cover essential skills for today's design professionals, including decision literacy, stakeholder translation, and the art of challenging assumptions diplomatically. Kristen emphasizes that while AI can generate design artifacts, it cannot create alignment across teams or orchestrate complex organizational dynamics. She offers concrete tactics like using customer stories to explain roadmaps, minimizing big presentation moments in favor of ongoing stakeholder engagement, and treating colleagues like users to better understand their motivations and constraints.
Highlights
11:08 Design Skills That Can't Be Replaced by AI
15:56 Decision Literacy in Organizations
21:42 Surprising Realities of Leadership Meetings
24:36 Building Influence Without Organizational Power
29:01 Storytelling via Roadmaps Exercise
35:47 What Trade Off Are You Most Uncomfortable Making
Resources
A Framework for Decision-Driven Research
Using Research Roadshows for Influence
The UX Designer's Guide to Research

Erin and Ben chat with Laura Klein, Principal Experience Specialist at Nielsen Norman Group, about the practical realities of AI in user research. Laura teaches a class on AI for researchers and brings a refreshingly balanced perspective to cut through the hype. She explains why those "five simple prompts that will change your life" are mostly nonsense and shares where AI actually works well versus where researchers need to proceed with extreme caution.
Laura also shares her green, yellow, and red light framework for AI adoption. She champions AI for tasks like transcription and meta-analysis of old research data, while warning against using it for synthesis and insights generation without serious human oversight. The conversation explores how AI can help teams move faster without sacrificing quality, the importance of collaborative analysis sessions, and why junior researchers need extra guidance when using these tools.
This episode offers practical guidance for any researcher trying to separate AI reality from the marketing promises flooding their inbox.
Highlights
07:07 Green light, yellow light, red light framework
11:59 Framework for deciding when to use AI
21:06 Will AI change the researcher role
31:23 Focus on what AI makes possible
35:17 Skills for advocating against AI hype
42:07 AI makes things that look like insights
Resources
Laura's NN/g AI Course
The AI in UX Research Report
Using AI Moderation for Research