Ep. 48 - FINALE

This is the final episode of the Finders to Builders podcast.Thank you so much for tuning in and for your support all these years!

The difference between doing research and building a research practice is the difference between going to the gym once every few months and actually getting fit. One-off studies won't create lasting impact. A practice is an intentional, continuous investment — a system that captures, connects, and applies knowledge to drive better decisions. I cover the three pillars (culture, infrastructure, communications), and the signs that tell you it's time to build the thing properly.(00:11) Research vs Practice(00:44) Sporadic Research Pitfalls(01:55) Gym Analogy Momentum(03:02) What a Practice Looks Like(03:54) Three Pillars Framework(05:39) Signs You Need Practice(07:59) Buy In and Scaling(09:21) Connective Tissue Vision(10:03) Wrap Up and Where to Follow

Researchers often over-index on rational arguments, assuming clear findings will drive action. But decisions are also shaped by emotions and organizational dynamics — political, personal, and power pressures that influence how stakeholders respond. Instead of blaming stakeholders for "not getting it," we should research and empathize with them the way we do with users, and frame recommendations within their context. When stakeholders feel understood, trust builds and decisions become easier.(00:11) Decisions Drive Research(00:43) Myth of Pure Rationality(01:54) Why Stakeholders Push Back(02:24) Research Your Stakeholders(03:41) Politics and Pressure Factors(04:33) Frame Recommendations for Context(05:55) Communication Builds Trust(06:32) Facts Plus Emotions(08:02) Recap and Next Steps(09:14) Outro and Reach Out

In the final guest episode of Finders to Builders, Thomas Wilson breaks down why large organizations aren't ready to spin up AI agents, how AI-first thinking amplifies "move fast, break things," and why stakeholder interviews often beat user research for actionable insight.(01:14) AI Has Been Here(02:58) Everyday AI Examples(04:57) LLMs And Job Shock(06:00) Org Readiness First(09:06) Move Fast Fallout(12:58) Keys To The Kingdom(15:47) Stop Bot Hype(17:46) Pilot AI For Value(19:37) Not Ready to Replace Creatives(20:44) LLM Hype vs Real Value(21:55) Using AI as a Work Tool(24:03) Quality Over Speed and Scale(25:20) Enshittification and Experience Rot(27:38) Doing Good Still Profitable(29:14) AI First Replaces Product First(31:24) Drop the Jargon Use Metrics(35:36) Stakeholders Hold the Truth

PMs, designers, marketers, and others are increasingly leading research in organizations that don't have dedicated researchers. My three tips: align on and protect a fixed research capacity, make research usable across the organization, and build with the expectation that someone may later take it to the next level.(00:56) Research Audience Shift(04:11) AI and Layoff Impact(06:21) Reframing Podcast and Book(07:11) New Challenges for Roles(08:00) Tip 1 Protect Time(09:32) Tip 2 Share Research(10:35) Tip 3 Build for Future(11:45) PWDR Mindset Shift

Researchers rarely sit in top-level strategy meetings, so real impact comes from starting lower—informing interface, feature, and product decisions—and from building advocates as teams see the value of research. Over time, this earns a seat at higher tables. Impact is about breadth (being at more tables), not just height.(00:11) Welcome and the hype(00:41) Why strategy talk falls short(03:43) Decision altitudes framework(05:06) Where research creates value(06:29) Build the ladder upward(08:08) Impact is width not height(08:45) Many tables mindset(09:25) Wrap up and where to find me

Today I talk with Antonia Landy about what product operations really is: building repeatable systems for high-impact work in complex product environments, often making things harder short-term to improve them long-term. We unpack common misconceptions (product ops as “just process and templates”), where product ops sits today (more understood but often expected to be done without added headcount), and how product ops tasks always exist—someone is already doing them, intentionally or not.(01:10) What Product Ops Means(02:39) Common Misconceptions(03:49) Product Ops Today(05:27) Doing Ops Without Headcount(08:16) Bottom Up Or Top Down(11:30) Selling Ops Value To Leaders(14:38) Research Ops Collaboration(17:52) Advice For Researchers(20:51) When To Hire Product Ops(23:11) Fixing The Operating Model(26:13) Maturity Models And Data Use(28:47) From Small Fixes To Culture(32:45) Three Tips And Wrap Up

Today we'll talk about the Inside Triangulation Score, which is a simple framework used to gauge confidence in findings from various research sources. (01:22) Welcome and New Setup(02:19) Why Data Quality Matters(03:38) Where to Use the Score(05:07) Framework Overview and Scoring(10:30) Adding Scores and Interpreting Results(13:01) How to Apply It and Wrap Up

In the final episode of this AI miniseries, I share my favorite user research use case for AI: drafting documents and protocols. Because I dread writing long docs, I use AI for a head start and then polish the output, saving about 70% of my time. I describe a small, quick experiment where I trained local LLMs by feeding them dozens of my research documents, interview and usability protocols.(01:21) The Simple Experiment(02:17) Document Templates and Structure(02:43) Question Bank and Guardrails(03:12) Before and After Results(04:23) Workflow with Voice Prompts(05:33) Sharing the Training Prompt(07:20) AI Empowers Researchers

Second part with the great Dr. Ari Zelmanow!(00:11) Why Speed Matters(00:60) Speed vs Rigor Reframed(03:34) The Backhoe vs Shovel(04:22) Democratizing Research Fears(05:45) Evidence Based Collaboration(09:03) Practical Enablement at Scale(09:48) AI for Faster Cheaper Research(12:19) Becoming the Business Counselor(14:47) All Research Is Strategic(17:13) Three Tips for the Future(18:43) Wrap Up and Where to Find Ari