How to AI UXR

Brooke Sykes is a senior manager of Firefox user research at Mozilla. A dedicated champion for a healthier, more open internet, she works closely with cross-functional leadership across product management, design, and engineering to ensure data-driven insights actively shape product strategy. Beyond research leadership, Brooke is focused on delivering operational excellence, team empowerment, and scale, and regularly evolves organisational practices for the Firefox community.Allison Robins is a mixed-methods researcher with nearly a decade of experience across B2B and B2C contexts, and currently leads research for Mozilla Firefox’s productivity workstream. She’s spent the past few years experimenting with AI in real workflows, separating hype from substance, and learning how to use these tools in ways that sharpen thinking and protect the parts of the craft that matter most.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 ConversationFor many research organisations, AI adoption has moved beyond play and into the trickier work of reshaping people management, practice, and team culture. Leaders are asking teams to move quickly, while individual contributors (ICs) are still trying to understand what good use looks like, how much judgement they should retain, and how their competence will be perceived in a working environment whose norms are…well, not yet normal.In this final episode of How to AI UXR, Brooke Sykes and Allison Robins from Mozilla’s Firefox research team give this, shall we say, “unsettled period” a useful name: the messy middle. Brooke speaks from the manager’s side of that transition. For her, the job isn’t simply to tell a team to adopt AI; it’s to translate a broad mandate to match her team’s routines, personalities, and principles, create space for experimentation and psychological safety, recognise uneven skill levels, and be honest that managers are also learning on the job.Allison brings a practitioner’s vocabulary to one of the most overused phrases in AI work: human in the loop (HITL). Rather than treating HITL as a single activity, she describes three modes of AI use: the self-automator, the centaur, and the cyborg, as outlined in a Harvard Business School paper titled, “Cyborgs, Centaurs and Self-Automators: The Three Modes of Human-GenAI Knowledge Work and Their Implications for Skilling and the Future of Expertise.“ While each mode has its place, none is sufficient on its own. Instead, research judgement lies in knowing when to hand off a bounded task, when to retain authorship while delegating parts of the work, and when to stay in continuous dialogue with AI.Together, Brooke and Allison offer a pragmatic way to think about AI maturity in research. Their aim isn’t to resolve the messy middle or to pretend that they’ve already developed a settled practice. Instead, it’s about being clearer about the risks, more precise about AI evaluations, more tolerant of uncertainty, and better equipped to choose the right kind of human involvement for the work at hand.The How to AI UXR MapThis 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 MapIn this episode, we cover:* Why “adopt AI” isn’t a strategy, and how research managers must make practical decisions about workflows, experimentation, guardrails, and verification to achieve that goal* How uneven AI literacy affects team adoption, with some researchers already using AI as an always-available thought partner while others are still building confidence and fluency* Why psychological safety matters when teams are learning AI together, especially when people need room to ask basic questions, make imperfect experiments, and revise their views as the technology develops* How Allison frames the central tensions of AI use, including the fact that AI can accelerate individual output while creating more work for others, and can help people build skills while also giving them misplaced confidence* Why the social stigma around AI use can leave researchers caught between pressure to use the technology and concern that visible use will make their work seem lazy, superficial, or less credible* How the self-automator, centaur, and cyborg modes offer a more useful vocabulary for deciding when to hand work to AI, when to retain strategy and judgement, and when to use AI as a thought partner* Why skilled AI use is not a fixed personality type, but a contextual judgement that changes according to stakes, urgency, tool capability, and the level of verification available* Why Brooke and Allison both encourage people to start by experimenting themselves, staying curious, and using the existing strengths of researchers to learn inside the uncertainty rather than waiting for settled best practicePartway 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.Connect with the Guests* Brooke Sykes, Senior Manager, Firefox User Research, Mozilla* Allison Robins, Staff User Researcher, Mozilla * Priya Krishnan, cofounder and COO of StrellaHow 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

Amanda Amyx is a design UX research leader who specialises in translating compelling customer insights into strategic product decisions. Currently serving as the senior director of design and research at Hatch, a company dedicated to sleep health and wellness technology, she leads teams at the intersection of consumer empathy and business growth. 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 ConversationThese days, research and product teams are being asked to produce more evidence, more quickly, and across more experiences than traditional, human-driven methods can support. AI moderation is an attractive solution, but it also raises a question for research leaders: if a machine can conduct an interview, where does that leave researchers?In this episode, Amanda shares how her team is using AI-moderated studies as a supplemental source of evidence alongside surveys, human-led interviews, prototype testing, and continuous listening. She sees AI moderation as a powerful supplement, not a substitute for human-moderated research.The value of the method, as Amanda shares, isn’t that AI moderation removes the need for research skill and judgement. Instead, when designed carefully, AI moderation can help teams collect natural-language feedback at greater scale, compare findings across sources, and hear from participants in moments that would otherwise be difficult, or impossible, to reach. Say when your newborn is screaming at three o'clock in the morning; a real-life customer situation for Hatch.The How to AI UXR MapThis 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 MapIn this episode, we cover:* Why AI moderation still requires researchers to design strong studies, define where probing should happen, and recognise that poor questions will still produce poor evidence.* Why Amanda sees AI moderation as another source of evidence rather than a replacement for human-moderated research, especially when teams need greater scale or additional validation.* How her team has used AI moderation to understand growth audiences, including what brands people trust, what routines support sleep, and what lengths people go to for a good night’s rest.* Why speed matters, not only because research can be completed faster, but because teams can gather reactions to concepts and prototypes overnight—data that would otherwise be too late or impossible to gather.* How AI moderation can extend research into moments that human scheduling rarely reaches.* Why Hatch has been careful about expectation setting, including telling participants upfront when they’ll be interviewed by an AI moderator and giving them other ways to share feedback.* Why her advice to sceptical researchers is to try AI moderation first as a participant, then pilot it on low-stakes studies where they already have enough expertise to judge whether the output is useful.Partway through the episode, Priya Krishnan, the cofounder and COO of Strella, shares her take on the conversation.Connect with the Guests* Amanda Amyx, Senior Director, Design & Research at Hatch* Priya Krishnan, cofounder and COO of StrellaHow 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

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 ConversationAs 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 MapThis 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 MapIn 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 processPartway 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 StrellaHow 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

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 ConversationFor 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 MapThis 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 MapIn 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 StrellaHow 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

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 ConversationAs 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 MapThis 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 MapIn 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 claimPartway 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 StrellaHow 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