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

These 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.

In this episode, we cover:

1. 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.

2. 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.

3. 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.

4. 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.

5. How AI moderation can extend research into moments that human scheduling rarely reaches.

6. 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.

7. 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 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.

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 need to work out where AI should be used—and where it shouldn't. The answer to these questions is rarely a binary “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 depend on researchers’ relationships, judgement, and ability to read human nuance.

In this episode, Dave 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 will help you map an AI operating model.

The takeaway that encapsulates this conversation is this: rather than applying “AI everywhere” by default, AI becomes far more useful when teams decide through careful analysis of their work and systems where and when it best fits.

In this episode, we cover:

• Why the team divides research 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 (and why Strella’s COO Priya Krishnan sees AI moderation as a distinct methodology rather than a lesser version of a human interview).

• 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’re 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.

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.

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.

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.

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.

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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 demos a powerful AI research system—one of the most advanced I’ve seen—that’s now part of DoorDash’s core UX infrastructure. 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 also 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.

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 “skills 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.

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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 animal behaviour from UC Davis and scientific research logistics, he focuses on optimising qualitative data management and integrating AI tools into UX 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.

We cover:

1. How Daniel went from “I’m not a coder” to building invaluable ResearchOps tools

2. Why a homemade video game starring his dog, Tootsie, became a low-stakes way to learn professional AI-building skills

3. How he vibe-coded a lab booking app for Microsoft CoreAI, complete with an arcade Easter egg

4. Why ResearchOps teams may not always need to wait for engineering bandwidth or a third-party tool to fix everyday workflow problems

5. How Daniel turned a budget conversation into an interactive story about researchers, customers, recruiting spend, and business impact

6. What happened when he helped his father transform 24,000 astronomy observations into a searchable deep-sky website

7. How that personal project became the foundation for an AI-powered research library at work

8. Why the library is designed to surface evidence, caveats, and gaps—not false confidence

9. 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.

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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 AI UXR is brought to you 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, 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 your screen is on.

In this episode, we cover:

1. How Michelle built the UXR Interview Coach in a matter of hours

2. How the system scores research interviews against a custom-built rubric and delivers private, actionable feedback in Slack

3. Why “tough but fair” feedback lands differently when it is timely, specific, and evidence-based

4. What the team learned by analysing research quality across roles, call types, and quarters

5. How the rubric evolved as the system encountered more nuanced customer conversations

6. Why AI’s real value for research may be in enabling new systems, not simply automating old workflows or augmenting analysis

7. 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.

Connect with the Guests

• Michelle Bejian Lotia, Staff UX Researcher at Ramp

• Priya Krishnan, cofounder and COO of Strella

How to AI UXR is brought to you 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.

Subscribe now

How to AI 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.

Successfully integrating AI into the user experience research (UXR) workflow is the top objective on almost every research and ResearchOps professional’s mind. How to AI UXR is a map for building AI-augmented research operations. It was produced to give you an overview of the key trends, help you identify the maturity level at which you’re implementing AI—crawl, walk, or run—and provide you with a list of implementations being used by research professionals across the globe, which you can experiment with too.

Download the Map

As we discovered while making this map, the topic is vast and progressing so quickly, both within and beyond the field, that one can hardly keep up. In fact, the Run level detailed on the map is still evolving—it’s a primordial soup of innovation. As such, the How to AI UXR map is a snapshot in time (who knows how long it will remain accurate), but we hope it will set the tone for the kind of systemic, operational work you can (and should) do with AI rather than the “I-Me-Mine-AI”1 approach that has dominated the field.

How to AI UXR: The Map

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Download the five-page map, including an introduction, the Crawl, Walk, and Run levels of AI augmentation, and a glossary.

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The How to AI UXR map includes information on how it was produced, how to “read” it, a list of contributors, and a glossary—AI is introducing so many new terms, it can be hard to keep up. As you will no doubt agree, the map is also detailed. The production unearthed significantly more information than could be shared in a single infographic. Apart from introducing the map, this article covers what didn’t fit: key trends, the specifics of AI-augmented research operations, and the downsides that must be managed alongside the significant upsides that AI is offering many intrepid research teams.

The Five Shifts Reshaping UXR

It will come as no surprise that AI is fundamentally reshaping how UX research is done and consumed. But what’s more interesting is how quickly and intrinsically AI is changing the roles of research professionals,2 and how it’s being used to enrich and amplify both human-produced research and research operations.

If there’s one insight the How to AI UXR map aims lands, it’s this: AI’s most meaningful impact on research won’t be a collection of time-saving tactics; it will be the shift from individual work to systems work; from “How do I use AI?” to “How does our organisation build research that is faster, safer, and more reusable with the help of AI?” This shift is particularly well illustrated in the Run level of the map (see page 4). These are the major themes that emerged from the production:

1. Proactive rather than reactive. “The insights are great, but too late,” is fast becoming a complaint of the past. Well-designed AI systems allow research professionals to operate proactively. The focus is now on insight quality management and on predicting what stakeholders will need before they can articulate it. To support this shift, research teams are doing gap analyses across roadmaps, repositories, and recruitment panels, then delivering operations updates, insights, and weekly summaries before stakeholders have thought to submit a request.

2. Conversing with data. Analysis and synthesis are shifting from linear coding and sorting to a reflexive dialogue. As one contributor put it, “The amount of time I spend ‘conversing with my data.’ I think I’ve actually developed a deeper understanding of our users.” Rather than use AI to automate analysis and synthesis, savvy researchers are using AI as a whetstone to sharpen their thinking, query their data for contradictions and edge cases, and surface their own cognitive biases. For more on this topic, read “Calibration Matters More Than Automation: What AI’s History Suggests About Building Agentic Research Systems”3 by ResearchOps consultant George Jensen.

3. The “make” phase. The democratising power of AI means researchers can now more easily bridge the gap between insights and implementation. Researchers are vibe-coding functional prototypes, co-creating designs with participants in real time, fixing low-risk user interface issues in the product, producing on-brand copy, and, most interestingly of all, sharing insights not via decks or reports but in the “language” of designers and product managers: as high-fidelity prototypes. No longer is the democratisation of specialised crafts only a concern for researchers; designers and engineers are now concerned, too.

4. Qualitative research at quantitative scales. AI-moderated sessions are enabling researchers to run hundreds of interviews per week, and AI analysis is allowing them to parse huge qualitative datasets in ways that were previously impossible. For some researchers, that sentence may be enough to keep them up at night, but, rather than create black box research—invisible processes that seem impossible to interrogate—researchers are using these technologies to size issues before they commit to human research, prepare for high-risk human research, or to enable low-risk studies that would previously have been deprioritised. AI moderation is proving to be a valuable lever for research teams, but as with unmoderated research, you must define and implement the right operational guardrails and repeatable evaluation, or “eval,” practices to ensure speed doesn’t deteriorate quality.

5. Insights delivered in multimedia formats. Static PDF reports (another traditional bugbear for research professionals: “Why did no one read my report?”) are being replaced by multimedia content like podcasts, interactive vibe-coded websites, and Retrieval-Augmented Generation (RAG) chatbots that allow research consumers to use natural language to discover what they need to know, when they want to know it. If you’re unfamiliar with RAG systems, McKinsey offers a useful explainer: “What Is Retrieval-Augmented Generation (RAG)?”.4 Which of these multimedia content experiments will stand the test of time—novelty is finite—and the increasing issue of information overwhelm is anyone’s guess. This writer thinks that RAG-based chatbots are the best bet.

ResearchOps Is Becoming Agentic System Design

AI is ultimately a knowledge technology that has made most, if not all, activities within the research workflow systematisable. For this reason, the How to AI UXR map doesn’t include a dedicated section for ResearchOps or, on that note, knowledge management—it’s all systems, so it’s all research operations, and it’s all about managing knowledge. This same evolution is also making research and ResearchOps roles increasingly indistinct. Many researchers are transitioning from “traditional” research roles to almost full-time system design roles. Simultaneously, ResearchOps professionals can no longer focus solely on research system delivery; to deliver AI-augmented research systems, they must now also have a deep understanding of research craft.

That said, six themes emerged that are specifically related to the ResearchOps role, and they’re worth noting here:

1. From support to system design. This transition was already underway well before AI,5 but AI has accelerated it. Rather than offer administrative support, the primary role of ResearchOps and the increasing number of researchers delivering operations, is to build the infrastructure that allows AI-assisted research to happen efficiently and safely across entire organisations. “Run” research professionals (see page 4) are now designing complex agentic systems and repeatable eval processes to support ongoing reliability.

2. Intake, triage, support, and routing. At the Walk level (see page 3), operational systems now regularly include “front door” support bots and agentic intake processes. These systems use custom parameters to offer advice and decide whether a request should be handled via a self-serve template or routed to a human. As one contributor noted, “I’m working on an AI skill this week to act as the intake process for people who do research (PWDR). With certain parameters, such as low or high risk, the agent suggests whether to self-serve or work with UX management on researcher resourcing.”

3. Knowledge management and semantic search. Research knowledge management has been an ongoing challenge for research teams for over a decade, and AI is fundamentally changing the landscape. Primarily, there’s a move from keyword-based search to semantic and vector-based search layers. Repositories trained on research methodology are becoming increasingly hierarchical, and RAG-grounded environments where stakeholders can “converse” with research rather than reading static reports are becoming standard. I built a RAG repository to support the synthesis of the How to AI UXR data.

4. Data-informed learning and development. This is one of the most unexpected and powerful areas for leveraging AI. It’s shifting professional development from generic training to real-time coaching and automated critique systems that analyse research activities against organisational best practices. These tools—often implemented as specialised agents—provide immediate feedback on interviewing skills, identify leading questions in discussion guides, and help non-researchers navigate complex customer archetypes. Interestingly, in the case studies I’ve seen, people seem far more open to constructive feedback from a well-trained AI than from a fellow human.

5. Automation and pipeline integration. Research professionals operating at the Run level are building end-to-end AI pipelines, often Python or n8n-based, to automatically clean transcripts, remove personally identifiable information (PII), enrich metadata, and stage files for analysis the moment a research session ends. Files are also often prepared for intake into the research repository, with human validation. As you’ll learn in the following section, this sort of end-to-end automation requires significant, operationalised mediation.

6. Quality assurance (QA) as a new discipline. AI evals are emerging as a core operational discipline. This includes building systems that enable regular human-in-the-loop (HITL) monitoring of agentic outputs, such as building multi-agent auditing systems in which one agent extracts findings while another independently checks for alternative interpretations or missing evidence. As one contributor noted, “I created an agentic system with Claude Code: one agent extracts findings from interviews, another generates insights, another checks interviews for missing evidence, and another checks for alternative interpretations.” For more on evals, read “Winning the Game of Broken Telephone: A Blueprint for Evaluating AI Across the Research Pipeline” by Lindsey DeWitt Prat.

Seven Risks to Manage as You Scale AI

This map and the contents of this article will no doubt agitate many researchers: “Are you trying to say that AI can do my job?” Many researchers are now embracing AI, and it’s delivering promising upgrades to research, as the How to AI UXR map illustrates. But researchers working at the cutting edge of AI augmentation are equally aware of the critical downsides—not all are technological downsides—and the hard requirement that skilled humans remain in the loop. As a result, they’re building essential “AI antidotes” into their systems. These are the primary concerns:

1. The disintermediation risk. There’s a growing concern that AI-powered “answer engines” will reduce researchers to data aggregators, and that partners will shop for insights that align with their direction of travel rather than commission original research that may contradict it—never mind that it may take substantially more time to produce. An equally important risk is that AI makes research seem so easy that knowledge seekers bypass the essential interpretive scaffolding that makes research findings trustworthy—and that rushed or inexperienced researchers do this, too. None of these problems is new, but AI (the ultimate amplifier) has made these issues more obvious and pressing.

2. Critical familiarisation. Even though AI can process data and produce polished-sounding results, often in moments, experts emphasise that “steeping” in the data remains essential. A well-architected model council (a multi-model research architecture enabling you to simultaneously query several AI models to provide a unified, cross-verified response) may be able to do some evaluation for you, but a direct understanding of the raw data is the only way to guarantee that insights aren’t only accurate but also retain their richness—and truth.

3. Synthetic data loops. When AI study preparation, AI moderation, and even AI participants produce all of the insights (in other words, when human insight is completely removed from research), the risk of unreliable insights is, unsurprisingly, significant. But that doesn’t mean that these tools aren’t handy. To counter synthetic loops, savvy AI users are building systems that provide clear visibility into every stage of the automated pipeline, such as utilising model councils to audit findings and identify alternative interpretations.

4. Fraud and quality. Increasingly realistic fake participants and AI-enabled responses mean that research professionals need to implement both manual and automated checks. One contributor said, “I’ve come across sessions where it appears that participants are reading out AI responses to questions, potentially creating unwanted synthetic data,” while another shared that they’ve seen “increasingly realistic fake participants—even in video and audio.” Ironically, AI is also being used to counter fraud: research professionals use heuristics (rules of thumb) to flag low-quality participants, and agents to watch for inconsistencies, overly generic or scripted responses, and suspect applications.

5. The verification paradox. This is a critical strategic concern: as the volume of AI-generated research increases, the human ability to verify every citation or summary shrinks, leading to a reliance on potentially hallucinated patterns that can’t be unpicked. To counter the verification paradox, researchers are implementing the multi-agent auditing systems already mentioned, utilising source-linked RAG repositories for instant validation, and refusing to skip the human-led data familiarisation stage for high-stakes work.

6. Organisational and contextual blindness. Current AI models don’t understand office politics, organisational dynamics, or the stakes involved in a research study, or which insights will land with specific stakeholders. At the Run level (see page 4), researchers are designing agentic systems grounded in internal strategy and organisational topology while retaining human-led interpretive scaffolding to ensure insights resonate with the specific stakes and social dynamics of their audience.

7. Shallow insights, false speed, and bias amplification. As is now well known, AI outputs look polished but often lack substance, context, and nuance, increasing the risk that teams build the wrong solution, just more efficiently. Savvy researchers are no longer accepting the first summary a model provides. Instead, they command the model to search for the “needle in the haystack” and ask for contradictory evidence, outliers, and edge cases to sense-check dominant patterns. They’re also working to augment human-led research rather than hand over research generation entirely.

A Map to Help You Navigate, Negotiate, and Slow Down

In producing this map, the feedback has been contradictory: the map presents too much information; it’s overwhelming. The map doesn’t present enough information: we want a list of tools, citations, pace layers,6 and more. As a research professional, you’re likely to know this conundrum well.

We chose to offer a detailed-as-possible map, but one that stripped out as much noise as possible so you can read it as a sort of menu of the ways other research professionals are using AI to augment their workflows. It’s an attempt at documenting enormous complexity in a space that’s moving at breakneck speed, and brings to mind these lyrics from “Headlong” by Queen:

And you’re rushing headlong
You’ve got a new goal
And you’re rushing headlong
Out of control

This map is an attempt to offer some control, a launch pad for negotiating with leadership, and a way to set an AI strategy (while you play and experiment with this new technology, which is an important theme, too). But this map would be even more powerful were you to interpret it and evolve it. The map is copyrighted by The ResearchOps Review, but should you annotate it or be inspired to create something better, please let us know. We would be excited to see what you create, and inspired to keep the conversation going. Please mention The ResearchOps Review on LinkedIn.

Contributors

Thanks to the following AI innovators and makers for their contributions to this production: Adam Valerio, Allison Robins⁠, Angelica Eling⁠, Anette Petersen⁠, Anshuk Chhibber⁠, Arev Pivazyan⁠, Austen Lazarus, Aya Abdelgawad⁠, Brian Greene⁠, Brooke Sykes⁠, Carina Cook⁠, Caroline Cox-Orrell⁠, Casey Gollan, Christen Penny, Corina Kesler⁠, Diana Sapanaro⁠, Dr Asma Qureshi⁠, Emily DiLeo, Farah Faisel, Filip Uzarevic⁠, Graham Gardner, Hannah Mattil⁠, Heidi Austin⁠, Jordan Brinkman⁠, Kaleb Loosbrock, Kalee Dankner, Kathy Shi⁠, Kerttu Sobak, Katie Roehrick⁠, Lydia Iana, Madeline Winer⁠, Marshall Baker, Michel Vogel⁠, Naki Ossom⁠, Nathan Pena⁠, Nicole Hack⁠, Rachel Wigen-Toccalino⁠, Rebecca Klee⁠, Rita Casillas, Shane Melton⁠, Sohvi Silius⁠, Stephanie Kingston, Stephanie M. Pratt⁠, Tamia Sheldon⁠, Tarah Srethwatanakul⁠, Theresa Flood⁠, and Uyhun Ung⁠.

How to AI 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.