The ResearchOps Review

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Channel • 2 Shows

Technology

Technology

Updated Weekly
Technology

Technology

Updated Biweekly

Jul 16

The Shortest Route to Shipping AI Research Systems? Play.

Daniel Gottlieb is the Head of Research Operations for Microsoft’s CoreAI, where he builds scalable infrastructure for complex UX workflows. Leveraging a PhD background in scientific research logistics and animal behaviour from UC Davis, he focuses on optimising qualitative data management and integrating AI tools into modern research practices. He’s a prominent voice in the field and was featured in the ResearchOps 2.0 documentary series, which explores the past, present, and future of research operations. How to AI UXR is supported by Strella, an AI-powered customer research platform that partners with you to build, moderate, and synthesise interviews, allowing you to go from question to actionable insights in just a few hours. In This Conversation In this conversation, Daniel Gottlieb shares how he moved from feeling behind on AI-enabled coding to building invaluable tools for research operations. What began as a series of personal learning projects—messing around, even—became a set of working research systems: a lab booking app, an interactive budget presentation that will blow your mind, and an AI-powered research library for Microsoft CoreAI. The How to AI UXR Map This series builds on the insights shared in the How to AI UXR map, a five-page map that charts key trends, helps you pinpoint your AI maturity level, and offers practical, real-world applications you can adapt to your research systems. Download the Map We cover: - How Daniel went from “I’m not a coder” to building invaluable ResearchOps tools - Why a homemade video game starring his dog, Tootsie, became a low-stakes way to learn professional AI-building skills - How he vibe-coded a lab booking app for Microsoft CoreAI, complete with an arcade Easter egg - Why ResearchOps teams may not always need to wait for engineering bandwidth or a third-party tool to fix everyday workflow problems - How Daniel turned a budget conversation into an interactive story about researchers, customers, recruiting spend, and business impact - What happened when he helped his father transform 24,000 astronomy observations into a searchable deep-sky website - How that personal project became the foundation for an AI-powered research library at work - Why the library is designed to surface evidence, caveats, and gaps—not false confidence - What AI means for the future of ResearchOps: more making, more maintenance, and a very different kind of job Partway through the episode, Priya Krishnan, the cofounder and COO of Strella, shares her take on the conversation. Connect with the Guests - Daniel Gottlieb, Head of Research Operations for Microsoft’s CoreAI - Priya Krishnan, cofounder and COO of Strella Things Referenced - Microsoft CoreAI - Visual Studio Code (VS Code) - GitHub Copilot CLI - Steve Gottlieb’s Deep Sky How to AI UXR is supported by Strella, an AI-powered customer research platform that partners with you to build, moderate, and synthesise interviews, allowing you to go from question to actionable insights in just a few hours. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theresearchopsreview.com
Jul 22

What It Takes to Make Complex AI Systems Usable Across Teams

Nam Pham is a senior UX researcher at DoorDash, where they use mixed-methods research to build zero-to-one products. Lately, Nam’s been building dining out as a new category and shaping affordable DoorDash dining. They’re also developing research evaluation practices, making sure that users—in all their messy, human complexity—stay front and centre as the team builds AI and large language model (LLM) products. Before DoorDash, Nam led research on homeowner and seller products at Realtor.com. They studied at Parsons School of Design, where they developed a deep belief in participatory design. How to AI UXR is supported by Strella, an AI-powered customer research platform that partners with you to build, moderate, and synthesise interviews, allowing you to go from question to actionable insights in just a few hours. In This Conversation AI research systems are becoming increasingly easy to build, but how do you ensure those systems are reliable, properly maintained, and deployable beyond the person who built them and their local drive? In this episode, Nam Pham shares a powerful AI research system—one of the most advanced I’ve seen—that’s now part of DoorDash’s core UX infrastructure. In this episode, Nam demos their research workflow in Cursor and a study plan for a specific delivery question. This system helps DoorDash researchers, designers, and product managers move through the research process, from scoping and planning through instrument design, survey programming, analysis, and sharing. We talk about how Nam and their DoorDash colleagues use a more traditional tool stack, combined with skills, harnesses, hooks, scripts, connectors, and an internal “skills marketplace” to share and update AI skills that help teams do research. The primary takeaway from this episode is this: AI might enable you to build solo, but building in collaboration with engineering, design, analytics, and others will enable you to deliver systems that operate beyond one person and their local machine. The How to AI UXR Map This series builds on the insights shared in the How to AI UXR map, a five-page map that charts key trends, helps you pinpoint your AI maturity level, and offers practical, real-world applications you can adapt to your research systems. Download the Map In this episode, we cover: - How DoorDash turned individual AI experiments into shared research infrastructure - What it takes to make an agentic research system usable by researchers, designers, and product managers - Why a useful AI skill is closer to an operating procedure than a prompt - How internal knowledge from research repositories, company systems, and Slack can shape research planning - Why Markdown, reference files, Python scripts, and deterministic code help make LLM outputs more reliable - How AI can programme a Qualtrics survey while the researcher keeps working, and where human review still matters - How an internal “skill marketplace” for sharing and updating AI skills supports adoption, maintenance, and shared standards - How collaboration with engineering, design, analytics, and quantitative research changed what was possible - Why Nam argues for patience: teach the agent, test it with colleagues, observe where it fails, and continuously and collaboratively improve the system Partway through the episode, as a leader building one of the tools these researchers are exploring and using, Priya Krishnan, the cofounder and COO of Strella, shares her take on the conversation. Things Referenced - Cursor, an AI coding agent - Claude Code, another AI coding agent - Glean, an AI platform for work - MCP (Model Context Protocol) connectors are universal, plug-and-play software bridges that allow AI agents to safely access external tools, databases, and third-party applications - Markdown is a lightweight plain-text formatting language that uses simple symbols like #, *, and - to structure documents. It’s relevant to AI because it bridges human intent and machine processing, allowing LLMs to easily understand, organise, and output complex information without wasting computing power. - Python is a computer programming language that’s used to give clear instructions to computers. It acts like a translator, turning English-like words into a format that a machine can easily understand. - SQL (Structured Query Language) is the standardised programming language used to communicate with, manage, and retrieve data from relational databases. - AI harnesses are the surrounding software infrastructure, such as tool execution, memory, and safety guardrails, that safely control an AI model and enable it to autonomously execute multi-step tasks. - AI coding hooks are automated, user-defined scripts that trigger at specific points in an AI assistant’s workflow to enforce guardrails, run tests, or format code. - Agent journaling is the process by which an AI coding agent maintains a continuous, detailed log of its internal reasoning, tool executions, and step-by-step progress to help developers audit, debug, and understand its decision-making and workflow. - Skill chaining is the process of an AI agent sequentially linking multiple specific capabilities or tools together, using the output of one action as the input for the next to accomplish a complex, multi-step goal. Connect with the Guests - Nam Pham, Senior Researcher at DoorDash - Priya Krishnan, cofounder and COO of Strella How to AI UXR is supported by Strella, an AI-powered customer research platform that partners with you to build, moderate, and synthesise interviews, allowing you to go from question to actionable insights in just a few hours. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theresearchopsreview.com
5d ago

Automate, Augment, Keep Human

Dave Chen is senior director of UX Foundations & Enablement at 1Password, where he oversees user research, design systems, and design and research operations. He focuses on bridging user research and product design to scale highly secure, simple, and intuitive digital solutions. Before joining 1Password, Dave led multidisciplinary teams at companies like Flipp, General Mills, and Nielsen, building a background in consumer insights and market research. He holds an MBA from Wilfrid Laurier University and a Bachelor of Mathematics from the University of Waterloo. Outside 1Password, Dave writes on UX leadership and practical strategies (see Dear Danielle & Dave) to help UX teams build credibility, navigate organisational change, and scale their craft. How to AI UXR is supported by Strella, an AI-powered customer research platform that partners with you to build, moderate, and synthesise interviews, allowing you to go from question to actionable insights in just a few hours. In This Conversation As AI tools become more entrenched across organisations, research leaders are being asked where AI should be used, and where it shouldn’t. The answer is rarely as simple as “yes” or “no.” Some parts of the research workflow are repetitive, time-consuming, and well-suited to automation; other parts benefit from AI as a thinking partner; while others still depend on the researcher’s judgement, relationships, and ability to read human nuance. In this episode, Dave Chen shares the framework his team at 1Password has developed to make those distinctions more navigable. Rather than treating AI adoption as a general mandate, the team maps the research workflow across three categories: automate, augment, and keep human. The result is a simple visual that helps researchers, partners, and leaders understand where AI is already being used, where it may be helpful, and where the human role remains essential. Dave walks you through that framework and discusses how his team is using AI to reduce friction in research discovery, support tagging and synthesis, and create more engaging internal research outputs. This is the takeaway that encapsulates this conversation: rather than applying “AI everywhere” by default, it’s most useful when teams decide through careful analysis of the work, workflow, and their systems where it fits. The How to AI UXR Map This series builds on the insights shared in the How to AI UXR map, a five-page map that charts key trends, helps you pinpoint your AI maturity level, and offers practical, real-world applications you can adapt to your research systems. Download the Map In this episode, we cover: * How Dave’s team at 1Password created a simple visual framework for deciding where AI belongs in the research workflow * Why the team divides research work into three categories: what to automate, what to augment, and what to keep human * How the framework helps researchers respond more thoughtfully to broad organisational pressure to apply AI everywhere * Why repetitive, low-friction tasks, such as historical research lookup, can be strong candidates for automation when the right guardrails are in place * How 1Password uses a custom Slack-based agent to help stakeholders find past research, reduce repeated requests to research teams, and surface areas where research may need to be refreshed * Why tagging, coding, synthesis, and theming sit in the augment category rather than being treated as fully automated work * How AI can help researchers notice patterns, test interpretations, and move through data more efficiently, while still requiring researchers to stay close to the evidence * Why Dave’s team is cautious about synthetic users and AI moderation for the kinds of cybersecurity and enterprise research they conduct * How the team is experimenting with AI-generated research outputs, including more visual and interactive internal reports built with Dust * Why Dave advises research teams to start using AI tools in small, practical ways, learn what they are good and bad at, and then decide where they fit in the research process Partway through the episode, as a leader building one of the tools these researchers are exploring and using, Priya Krishnan, the cofounder and COO of Strella, shares her take on the conversation. Things Referenced * 1Password is a password manager and secure access platform used by individuals, families, and organisations to manage passwords, passkeys, secrets, and other sensitive information. * Cursor is an AI-assisted code editor that helps users write, edit, and understand code. * Dust is an AI platform for creating custom assistants and AI-powered workflows. * Dust frames are interactive, website-like visual documents and dashboards created automatically by AI agents on the Dust platform (see above) that can present research findings in a more interactive, website-like format. * Synthetic users are AI-generated or AI-simulated research participants used to explore possible behaviours, reactions, or needs. * AI moderation refers to AI-led research interviews or conversations. * “I-Me-Mine AI” is The ResearchOps Review founder Kate Towsey’s phrase for individual, self-directed use of AI to augment personal work, as distinct from designing AI-enabled systems that operate across a team or organisation. Read “The Research Operating System Too Few Are Building: Why “I-Me-Mine AI” Isn’t Enough”. Connect with the Guests * Dave Chen, Head of UX Research, Design System & UX Operations and cowriter of Dear Danielle and Dave. * Priya Krishnan, cofounder and COO of Strella How to AI UXR is supported by Strella, an AI-powered customer research platform that partners with you to build, moderate, and synthesise interviews, allowing you to go from question to actionable insights in just a few hours. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theresearchopsreview.com
Jul 9

Near-Instant Research Coaching is Now Possible—and It Works

Michelle Bejian Lotia is a staff experience researcher at Ramp, where she’s been building AI-powered research tools to manage research intake, orchestration, and coaching. Over more than twenty years, Michelle has built and led research teams at Asana, Zapier, and Trainline, establishing voice-of-customer programmes and insights infrastructure. Throughout her career, she’s been driven by one question: How do you bring customers closer to the people building products—and make it scalable? Michelle holds a master’s degree in information from the University of Michigan. How to AU UXR is brought to you by Strella, a customer research platform that uses AI to run in-depth interviews and generate actionable insights in just a few hours. In This Conversation In this conversation, hosted by Kate Towsey, Michelle shares how deadline pressure, combined with the possibilities of AI, led to one of the most compelling examples I’ve seen of AI being used not just to speed up research but also to uplift the craft. Michelle built a transcript-based coaching system that evaluates research calls, provides “tough but fair” feedback, and helps ninety non-researchers improve their craft within minutes of a research session. In this episode, Michelle will walk you through the tool, so make sure to watch closely. This series builds on the insights shared in the How to AI UXR map, a five-page map that charts key trends, helps you pinpoint your AI maturity level, and offers practical, real-world applications you can adapt to your research systems. → Download the map. In this episode, we cover: * How Michelle built the UXR Interview Coach in a matter of hours * How the system scores research interviews against a custom-built rubric and delivers private, actionable feedback in Slack * Why “tough but fair” feedback lands differently when it is timely, specific, and evidence-based * What the team learned by analysing research quality across roles, call types, and quarters * How the rubric evolved as the system encountered more nuanced customer conversations * Why AI’s real value for research may be in enabling new systems, not simply automating old workflows or augmenting analysis * Michelle’s advice for researchers experimenting with AI: start with one painful or underperforming area and make it better one step at a time Partway through the episode, Priya Krishnan, the cofounder and COO of Strella, shares her take on the conversation as a leader building one of the tools that these researchers are exploring and using. Connect with the Guests * Michelle Bejian Lotia, Staff UX Researcher at Ramp * Kate Towsey, founder of The ResearchOps Review * Priya Krishnan, cofounder and COO of Strella How to AU UXR is supported by Strella, a customer research platform that uses AI to run in-depth interviews and generate actionable insights in just a few hours. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theresearchopsreview.com
Jul 30

Three Lenses for Thinking Clearly About AI in Research

Dr. Llewyn Paine is an AI consultant, product strategist, and innovation leader with nearly two decades of experience working in emerging technology. As the principal of Llewyn Paine Consulting, she specialises in helping product leaders implement evidence-based rigour and responsible AI practices in their UX and design workflows. Her career includes leading initiatives on intelligent agents and physical AI at Microsoft and developing experimental media at Disney. Paine serves as the lead curator for Rosenfeld Media’s Designing with AI conference and frequently speaks at major institutions, including the Library of Congress. How to AI UXR is supported by Strella, an AI-powered customer research platform that partners with you to build, moderate, and synthesise interviews, allowing you to go from question to actionable insights in just a few hours. In This Conversation As the How to AI UXR map shows, research teams are now using AI across every part of the research workflow and beyond. They’re drafting screeners, summarising interviews, shaping reports, coaching non-researchers, automatically routing research requests, building end-to-end research systems—and learning just how much those systems cost to maintain. Velocity is the name of the game, but the important question isn’t whether AI can make research faster; it’s whether research teams can move faster and preserve the judgement, evidence, and methodological care that make research valuable in the first place. In this episode, Llewyn argues that AI in research should begin with judgement. Rather than treating AI adoption as a race towards more output, she makes the case for returning to the disciplines researchers already know well, including jobs to be done (JTBD), systems design, social science, statistics, and evaluation. This episode is a useful corrective to the pressure many teams feel to “AI everything.” Llewyn’s view is not that researchers should reject AI or position themselves as blockers to progress; it’s that they should understand the tools well enough to assess risk, ask better questions, and decide where AI supports better outcomes rather than simply producing more material. This conversation will give you a clearer way to assess where AI belongs in your research system, what evidence to ask for, and what to watch for before you trust the work it helps produce. The How to AI UXR Map This series builds on the insights shared in the How to AI UXR map, a five-page map that charts key trends, helps you pinpoint your AI maturity level, and offers practical, real-world applications you can adapt to your research systems. Download the Map In this episode, we cover: - Why a practice being popular does not make it good research practice - The three lenses research teams need when evaluating AI: stakeholder needs, social science, and computer science - Why teams should begin with the stakeholder’s job to be done before choosing an AI tool - What an AI harness is, and why the model itself is only one part of the system (Llewyn shares a great Teenage Mutant Ninja Turtles analogy) - Why Llewyn draws a distinction between AI that increases output and AI that improves outcomes - Why qualitative synthesis is one of the most tempting and highest-risk uses of AI in research - The difference between accuracy problems, such as incorrect quotes, and omission problems, where AI misses the most interesting or important insight - Why operational use cases, including coaching, routing, training, and support, may be among the most valuable applications of AI for research teams - Why researchers should understand enough about AI to ask for evidence, challenge weak claims, and avoid becoming passive consumers of vendor or influencer promises - How token costs may push teams towards better system design, clearer context, and more thoughtful use of AI - Why researchers do not need to carry evaluation work alone, and how they can partner with engineering and others to assess whether tools are doing what they claim Partway through the episode, as a leader building one of the tools these researchers are exploring and using, Priya Krishnan, the cofounder and COO of Strella, shares her take on the conversation. Things Referenced - Jobs to be done (JTBD) is a framework for understanding what stakeholders are trying to accomplish, rather than beginning with a tool or deliverable. - AI harnesses are the surrounding software infrastructure that gives a model access to tools, context, rules, workflows, memory, and guardrails. - Evals (evaluations) are structured ways of testing whether an AI system is producing outputs that meet defined criteria. - Markdown is a lightweight plain-text formatting language that helps humans and machines structure information clearly. - CSV files are simple spreadsheet-style files often used to move structured data between tools. - Tokens are the word fragments and other units of text that AI systems process as input and output. - Designing with AI is a Rosenfeld Media conference that Llewyn helps curate. - Paul Ford is a technology writer and software builder (best known for Bloomberg’s “What Is Code?”) who argues that AI makes human judgement and accountability more important, not less. - Oen Michael Hammonds is a UX and AI practitioner who uses “AI speed bump” to describe adding deliberate friction and checks so teams don’t ship unsafe or unreliable AI systems. - World Usability Day is an annual global event (held on the second Thursday in November) focused on usability and human-centred design through talks and local meetups worldwide. - Teenage Mutant Ninja Turtles is a pop culture franchise whose villain Krang operates a mechanical body, used here to illustrate an AI “harness” (system) versus the model (brain). - Pac-Man is a classic 1980 arcade game used as a metaphor for tokens as the “bits” an AI system consumes and produces. Connect with the Guests - Llewyn Paine, founder and consultant, Llewyn Paine Consulting - Priya Krishnan, cofounder and COO of Strella How to AI UXR is supported by Strella, an AI-powered customer research platform that partners with you to build, moderate, and synthesise interviews, allowing you to go from question to actionable insights in just a few hours. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theresearchopsreview.com
09/30/2025

EP #4: Building Enduring Systems Amidst Constant Change

ResearchOps 2.0 is brought to you by User Interviews—the only solution you need to recruit high-quality participants for any kind of research. In just ten years, ResearchOps has transformed from an obscure Silicon Valley speciality into a vibrant global profession. Despite economic headwinds, ResearchOps roles are emerging across industries far beyond tech, and the field is evolving at breathtaking speed. But where have we come from, and where are we going? In this five-part audio documentary, we’re exploring the past, present, and future of ResearchOps—and it’s exciting, fascinating, and, at times, pretty mindblowing stuff! Each episode features the voices of Cha Cha Club members, senior research leaders, and the smart minds behind User Interviews—the only solution you need to recruit high-quality participants for any kind of research. To scale up; tune in. In This Episode In this fourth and penultimate episode of ResearchOps 2.0, we’re exploring a timely topic: how to build enduring research systems even when change seems to be the order of the day—every day. You’ll learn top tips from people who’ve managed to ride the winds of change and evolve to deliver successful research operations. Here’s what this episode covers: (1.08) About this Episode (2.12) Change Isn’t the Exception, It’s the Rule (4.52) The Elephant-in-the-Room Topic: Layoffs (10.39) Layoffs Impacted People All over the World (11.55) Layoffs from an Economics Point of View (16.30) But Layoffs Are Only One Form of Change (20.18) A Real-World Transformation at LinkedIn: Taking an Initiative-First Approach 24.05) Attach Yourself to Interesting Problems, Not Solutions (25.25) You Must Deeply Understand the Business: ‘Read the Tea Leaves’ of the Organisation (29.00) Work on the Highest Priority; The Biggest Bang for Buck (31.24) Carolyn’s Top Tip: ‘Businessfy’ Your Work (33.11) Enable People to Own Things on Their Own (35.00) Build Adaptability Into Your Research Systems (37.45) Setting Healthy Boundaries to Manage Constant Change (40.16) Good Relationship Building Can Help You Manage Change (41.18) Make Invisible Work Visible (43.19) Managing the Human Cost of Constant Change: Building Resilience Within Yourself (47.03) About the Next, Final Episode of ResearchOps 2.0 (48.35) End This episode features Luana Cruz, Research Operations at Itaú Unibanco; Lexi Breitz, the Director for Research and Customer Insight at Headway; Mia Mishek, Senior UX ResearchOps Manager at Target; Carolyn Morgan, ResearchOps and Enablement Manager at Cisco; Lydia Iana, a ResearchOps specialist; Noel Lamb, the Senior Manager for ResearchOps at ServiceNow; Tim Toy, the Senior Manager for ResearchOps at Adobe; Rodrigo Dalcin, the Staff User Experience ResearchOps at Wealthsimple; Kalee Dankner, the Senior Manager for ResearchOps at LinkedIn; Carina Cook, Director of Product Insights and Operations (ESPN BET/theScore) at PENN Entertainment, Inc.; and Dennis Meng, the co-founder and CPO of User Interviews. What a lineup! Things Mentioned - Cha Cha Club—a members’ club for full-time ResearchOps professionals. - ‘The 2025 Research Budget Report’ by User Interviews About the Next Episode The next and final episode of this series drops on October 14th, and it’s dedicated to the people of ResearchOps: the diverse, smart, and incredibly authentic people who make ResearchOps what it is. We’ll cover major themes like burnout, making invisible work visible, and layoffs—the human stuff that’s defined our experience of ResearchOps over the past decade. Subscribe to The ResearchOps Review to get smart thinking all about ResearchOps delivered straight to your email inbox. Brought to You By A Cha Cha Club production, sponsored by User Interviews—the only solution you need to recruit high-quality participants for any kind of research. Credits Produced and narrated by Kate Towsey. Co-produced with Cha Cha Club manager, Glenn Familton, and ResearchOps experts, Jenna Lombardo and Renato Venter. Aria, the robotic voice, was generated by Soundly. Explore the music and (most) sounds featured in this episode via this Epidemic Sound playlist. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theresearchopsreview.com
09/16/2025

EP #3: Taking a Platform Approach to ResearchOps

ResearchOps 2.0 is brought to you by User Interviews—the only solution you need to recruit high-quality participants for any kind of research. In just ten years, ResearchOps has transformed from an obscure Silicon Valley speciality into a vibrant global profession. Despite economic headwinds, ResearchOps roles are emerging across industries far beyond tech, and the field is evolving at breathtaking speed. But where have we come from, and where are we going? In this five-part audio documentary, we’re exploring the past, present, and future of ResearchOps—and it’s exciting, fascinating, and, at times, pretty mindblowing stuff! Each episode features the voices of Cha Cha Club members, senior research leaders, and the smart minds behind User Interviews—the only solution you need to recruit high-quality participants for any kind of research. To scale up; tune in. In This Episode In this third episode, we’re diving into the importance of taking a platform approach to ResearchOps. But “what is a platform approach?” you might ask. (1.08) About this Episode (1.49) At the Intersection of Art, Technology, Research, and Design (3.57) But What Is a Research Platform? (6.04) Case Study No. 1: Intuit’s Data Validation Tool (9.06) Building Custom Features to Meet Unique Business Needs (10.29) Case Study No. 2: An Insights Platform at IBM (12.07) Creating an Impact Long Into the Future and Well Beyond Your Immediate Team (13.23) The Network Effect of a Research Platform—and Massive Scalability (14.24) A Platform Needn’t Be a Huge Technical Investment (16.41) Fostering an Integration Between People, the Business’s Goals, and Research Goals, Too (19.57) Making the Shift from a Full-Service Operating Model to a Platform Approach (21.51) A Platform Must Be a Win-Win-Win (24.30) The Pros and Cons of a Platform Approach (27.43) All-in-One Versus Best-in-Class Tools (28.50) Platforms as Water Coolers Versus Filing Cabinets (30.54) Walled Gardens and Capitalising on Your Own Data (32.03) Choose Your Platform Partners Well (34.41) The New Skills We Need to ‘Platformize’ Things (38.12) A Super-Inspiring Moment with Garett (39.30) What’s the Opposite of a Research Platform? Hint: It’s Not the ‘F-Word’: Fire Drills. (41.18) Casey, What's the Future Research Platform of Your Dreams? (And He Lit Up.) (43.27) Garett Wraps up with the Most Motivating Quote Ever! (45.57) End This episode features Casey Gollan, Garett Tsukada, Carina Cook, Lexi Breitz, and Basel Fakhoury. The next episode drops on September 30th, and it’s all about building robust ResearchOps systems in the midst of constant change—advice that everyone needs. Subscribe to The ResearchOps Review to get smart thinking all about ResearchOps delivered straight to your email inbox. Brought to You By A Cha Cha Club production, sponsored by User Interviews—the only solution you need to recruit high-quality participants for any kind of research. Credits Produced and narrated by Kate Towsey. Co-produced with Cha Cha Club manager, Glenn Familton, and ResearchOps experts, Jenna Lombardo and Renato Venter. Aria, the robotic voice, was generated by Soundly. Explore the music and (most) sounds featured in this episode via this Epidemic Sound playlist. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theresearchopsreview.com
Aug 5

Building a Self-Educating Research Brain

Jordan Brinkman is the lead UX researcher at ERGO NEXT Insurance, where he leads end-to-end qualitative and quantitative discovery across product and marketing. His article for The ResearchOps Review introduced the second-layer concept in research automation: the observation that integrating AI into research workflows generates a distinct set of governance and operational questions that sit beneath the surface of the apparent efficiency gains. His work examines which parts of the synthesis process can be accelerated responsibly, which must remain human-led, and how research professionals can preserve the psychological and craft dimensions of user research as they adopt (and build) new tools. How to AI UXR is supported by Strella, an AI-powered customer research platform that partners with you to build, moderate, and synthesise interviews, allowing you to go from question to actionable insights in just a few hours. In This Conversation For years, research repositories have promised to make insights reusable beyond the immediate product team. In practice, many research professionals have found them difficult to keep “alive.” This challenge typically lies in the operational overhead required to classify, analyse, cross-reference, update, and maintain repositories that become vast collections of content over time. If you’re trying to build an AI-enabled repository, you’ll find this episode especially timely. Jordan demonstrates his “living research brain,” built with Claude Code, Markdown files, AI skills, human checkpoints, and a growing set of workflows—a system that takes research materials such as transcripts, survey data, A/B test results, and secondary research, then routes them through different analytical workflows before proposing updates to a larger research wiki. Jordan’s research brain isn’t only an example of how AI, or Claude, can process more research material more quickly. It also demonstrates what’s possible when a researcher treats AI as part of a wider system that’s purposefully designed to depend on craft, judgement, governance, and near-constant maintenance. Though AI can identify, route, summarise, cross-reference, and draft insights, humans must still decide what’s methodologically sound and what should be allowed into the organisation’s shared memory: the wiki. Jordan also offers a practical reminder that building with AI isn’t only fast-tracked automation; it’s also systems design—the quality of which depends on how clearly you can explain your work to AI and those around you. The How to AI UXR Map This series builds on the insights shared in the How to AI UXR map, a five-page map that charts key trends, helps you pinpoint your AI maturity level, and offers practical, real-world applications you can adapt to your research systems. Download the Map In this episode, we discuss: - Why AI changes the repository problem by allowing research material to be analysed, queried, cross-referenced, and reorganised over time. - How his system identifies different resource types, including transcripts, reports, survey files, and A/B test results, then routes each one through an appropriate analysis workflow. - Why the system uses Markdown files as its basic structure, and how those files become a wiki-like knowledge base that can evolve as new evidence is added. - How Claude skills help define different analysis processes, and why those skills still need to be shaped by a researcher’s methodological judgement. - Why research professionals working with AI systems may need a much deeper understanding of research craft than was previously required. - Where he places human checkpoints in the workflow, particularly before analysis and cross-referencing are allowed to become new wiki content. - Why unchecked AI outputs can pollute a research system through overgeneralisation, weak synthesis, or misplaced emphasis. - How he is thinking about team access through GitHub, so that others in the organisation can query the research brain and eventually contribute new material. - Why building AI systems creates a new maintenance burden, including logs, versioning, quality checks, token costs, context management, and the need to track what has changed between working sessions. Partway through the episode, Priya Krishnan, the cofounder and COO of Strella, shares her take on the conversation. Things Referenced - Andrej Karpathy is an AI researcher and educator, a founding member of OpenAI, and former director of AI at Tesla. - Claude Code is Anthropic’s agentic coding tool, which can read codebases, edit files, run commands, and work across development tools. - Claude skills are reusable folders of instructions, scripts, and resources that help Claude perform specialised tasks more consistently. - CSV files are simple text files that store spreadsheet-style rows and columns, usually with values separated by commas. - GitHub is a platform for storing, versioning, reviewing, and collaborating on files, especially code. - Markdown is a lightweight markup language for adding structure and formatting to plain-text documents. - NotebookLM is Google’s AI research and note-taking tool, designed to answer questions from sources the user uploads or connects. - RAG (retrieval-augmented generation) is an AI framework that combines search or retrieval from external sources with a language model’s generated response. - Tokens are the units of text that AI systems process as input and output, such as words, word fragments, or punctuation. Connect with the Guests - Jordan Brinkman, Lead UX Researcher, ERGO NEXT Insurance - Priya Krishnan, cofounder and COO of Strella How to AI UXR is supported by Strella, an AI-powered customer research platform that partners with you to build, moderate, and synthesise interviews, allowing you to go from question to actionable insights in just a few hours. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theresearchopsreview.com
10/14/2025

EP #5: The People of ResearchOps

ResearchOps 2.0 is brought to you by User Interviews—the only solution you need to recruit high-quality participants for any kind of research. In just ten years, ResearchOps has transformed from an obscure Silicon Valley speciality into a vibrant global profession. Despite economic headwinds, ResearchOps roles are emerging across industries far beyond tech, and the field is evolving at breathtaking speed. But where have we come from, and where are we going? In this five-part audio documentary, we’re exploring the past, present, and future of ResearchOps—and it’s exciting, fascinating, and, at times, pretty mindblowing stuff! Each episode features the voices of Cha Cha Club members, senior research leaders, and the smart minds behind User Interviews. To scale up, tune in! In This Episode In this fifth and final—”awwww”, we hear you say—episode of ResearchOps 2.0, we’re turning the spotlight on the people of ResearchOps: the curious minds, problem-solvers, and system-builders who’ve made this profession what it is today. We’ll explore who they are, where they’ve come from, and what drives them to transform how organisations learn from their users. If you’re a ResearchOps professional, this episode will make you feel seen and heard. If you’re a leader looking to hire a ResearchOps role, this episode will help you think differently about who might hire. And if you’re looking to transition into a ResearchOps career, this episode will give you a great sense of the skills you need and the incredible profession you’ll become a part of. Here’s what this episode covers: (1.05) About this Episode(2.04) From Primate Welfare to ResearchOps: Daniel Gottlieb(4.58) From Fishery Engineering to ResearchOps: Luana Cruz(6.12) From Anthropology to ResearchOps: Garett Tsukada(7.17) A Smart Start at Adobe: Tim Toy and a Three-Piece Suit(9.38) A More ‘Traditional Route’ with Jared Forney(11.23) ‘Hired by a Detective’ with Mia Mishek (14.00) Levelling Up: Shaping Career Growth in ResearchOps(16.13) The (Soft) Skills That Make Someone Successful in ResearchOps(22.26) We Have a Lot to Learn from Product Management(24.28) ResearchOps Pros Iterate—Constantly(28.00) The Art of Convincing and Selling(30.40) The Reality of Burnout (And Ways to Manage It)(34.16) Knowing When to Say “No” AKA Strategy(39.29) Ask for Help(41.00) The Problem of Invisible Work—or Glue Work(43.08) Making the Invisible Visible: Marketing Tips and a Case Study from Okta(46.33) Everyone Has Impostor Syndrome—Even the Pros(54.00) End This episode features Daniel Gottlieb, Head of Research Operations for Microsoft’s CoreAI; Luana Cruz, Research Operations at Itaú Unibanco; Garett Tsukada, Head of Customer Connect UX Research Operations at Intuit; Tim Toy, the Senior Manager for ResearchOps at Adobe; Jared Forney, Research Operations Principal at Okta; Mia Mishek, Senior UX ResearchOps Manager at Target; ResearchOps specialist, Lydia Iana; Carolyn Morgan, ResearchOps and Enablement Manager at Cisco; Sam Gager, Senior Director, XD Research—Enterprise, Developer, Associate, & Accelerator, Capital One; and Erin May, Chief Marketing Officer at User Interviews. Things Mentioned * Cha Cha Club—a members’ club for full-time ResearchOps professionals * Lenny Rachitsky and Noam Segal. “How Tech Workers Really Feel about Work Right Now.” Lenny’s Newsletter. May 27, 2025. * Research That Scales: The Research Operations Handbook (Rosenfeld, 2024) by Kate Towsey * Tanya Reilly. “Being Glue.” No Idea Blog. * Emma Boulton. “The Eight Pillars of User Research.” Medium. The ResearchOps Community, July 11, 2019. * Kate Towsey. “A Framework for #WhatisResearchOps.” Medium. The ResearchOps Community, October 24, 2018. Brought to You By A Cha Cha Club production, sponsored by User Interviews—the only solution you need to recruit high-quality participants for any kind of research. Credits Produced and narrated by Kate Towsey. Co-produced with Cha Cha Club manager, Glenn Familton, and ResearchOps experts, Jenna Lombardo and Renato Venter. Aria, the robotic voice, was generated by Soundly. Explore the music and (most) sounds featured in this episode via this Epidemic Sound playlist. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theresearchopsreview.com
08/19/2025

EP #1: The Evolution of ResearchOps

ResearchOps 2.0 is brought to you by User Interviews—the only solution you need to recruit high-quality participants for any kind of research. In just ten years, ResearchOps has transformed from an obscure Silicon Valley speciality into a vibrant global profession. Despite economic headwinds, ResearchOps roles are emerging across industries far beyond tech, and the field is evolving at breathtaking speed. But where have we come from, and where are we going? In this five-part audio documentary, we’ll explore the past, present, and future of ResearchOps—it’s exciting, fascinating, and, at times, pretty mindblowing stuff! Each episode features the voices of Cha Cha Club members, senior research leaders, and the smart minds behind User Interviews—the only solution you need to recruit high-quality participants for any kind of research. To scale up; tune in. Subscribe to get sharp thinking all about ResearchOps delivered straight to your email inbox every two weeks. In This Episode In this first episode, we’ll take a whistle-stop tour of the history of ResearchOps, explore what’s driven its rapid evolution, and, in true 2.0 style, what the future holds. (1.08) About this Episode(1.21) Introducing the ResearchOps Generational Framework(3.52) Gen1: The Lab Technicians and Administrators(6.30) The Evolution to Full-Service Participant Recruitment(9.35) The ResTech Revolution(13.00) Hello, Research Democratisation(14.40) Gen2: Research System Designers(17.19) A Push from the GDPR(20.00) Gen3: The Data Orchestrators and Knowledge Architects(24.00) Casey, the Platform Designer and Agent Architect(26.23) Orchestrating the Data Ecosystem(29.35) From Generalists to Super Specialists(32.40) The Age of AI: Vibe Operations(39.15) The Age of AI: Vibe Operations(42.19) The Next Episode Is All About Leveraging AI(43.49) End This episode features Erin May, Lexi Breitz, Ph.D., Kalee Dankner, Tim Toy, Garett Tsukada, Noel Lamb, Sam Gager, Mia Mishek, Basel Fakhoury, Casey Gollan, Daniel Gottlieb, and Dennis Meng. About the Next Episode The next episode drops on September 2nd, and it’s all about leveraging AI for research superstardom. Subscribe to The ResearchOps Review to get smart thinking all about ResearchOps delivered straight to your email inbox. Brought to You By A Cha Cha Club production, sponsored by User Interviews—the only solution you need to recruit high-quality participants for any kind of research. Credits Produced and narrated by Kate Towsey. Co-produced with Cha Cha Club manager, Glenn Familton, and ResearchOps experts, Jenna Lombardo and Renato Venter. Aria, the robotic voice, was generated by Soundly. Explore the music and (most) sounds featured in this episode via this Epidemic Sound playlist. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theresearchopsreview.com

Expert-led insights for people building research operations.