How AI is Changing UX Research: From Interviews to Ready-Made Insights with Dovetail, Maze, and ChatGPT

UX research helps understand how people actually use a product, where problems arise, and which solutions are worth testing before development. But quality research takes time. You need to formulate hypotheses, find respondents, prepare questions, conduct interviews or tests, process recordings, identify recurring issues, and turn all of this into clear insights for the product team.

AI is gradually transforming this process. It doesn't eliminate the need to talk to users, nor does it make product decisions for the team. Instead, AI can take on a significant portion of the work involved in preparing, structuring, and analyzing information.

Let's explore how Dovetail, Maze, and ChatGPT can be used at various stages of UX research and where automation can truly speed up a product team's workflow.

Where AI Can Help in UX Research

UX research consists of much more than just talking to a user. A single session can involve a lot of additional work: preparing the script, transcribing recordings, finding key highlights, grouping issues, comparing responses from different participants, and preparing findings for the team.

This is precisely where automation delivers the greatest value. AI can be used to prepare research, work with transcripts, identify recurring themes, analyze responses, create initial drafts of findings, and tailor results for designers, developers, or executives.

However, real users and real data must still remain the foundation of the research. AI helps you work faster with the gathered information, but it shouldn't generate it in place of users.

Dovetail for Managing Large Volumes of Research

When a team regularly conducts interviews, usability tests, and collects customer feedback, information accumulates rapidly. Individual notes can end up scattered across different documents, findings from one study get forgotten after a few months, and it becomes difficult for a new team member to understand what has already been researched.

Dovetail helps turn these materials into a centralized research repository. The platform can automate part of the repetitive work, particularly assisting with tagging, identifying themes, and creating summaries. At the same time, the team can always trace back to primary sources to verify the data behind any specific conclusion.

This is especially useful when dealing with dozens of interviews or a large volume of customer feedback. Instead of starting the analysis from scratch every time, the team gets a structured environment where they can identify patterns across different studies.

For example, a SaaS product might have separate interviews with administrators, managers, employees, and customers. Over time, dozens of hours of recordings pile up. AI helps spot recurring problems faster and manage large volumes of information, but the final interpretation still rests with the researcher or product team.

Maze for Rapid Interface Testing

Not every UX question requires a full-scale interview. Sometimes you just need to test a specific user flow or an isolated interface solution.

For example:

• Can users find the required feature?

• Is the menu structure intuitive?

• Can they complete the primary user flow?

• Are people interpreting the new screen correctly?

Maze can be used for such tasks. The platform supports various product research formats, including prototype testing, website and mobile product testing, card sorting, tree testing, surveys, as well as moderated and unmoderated studies.

AI can also be used directly within the research process. It helps structure the study, detect potentially biased questions, analyze responses, generate transcripts, and surface common themes among the results.

AI moderation allows you to scale parts of your research when you need to gather feedback from a larger audience. Instead of a completely static script, the system can ask follow-up questions based on participant responses to gather more context.

When Maze Cannot Replace Talking to a User

Quantitative metrics are great at answering the question of what happened. For example, you can see that users fail to complete a specific flow, spend a long time searching for an element, or frequently misclick.

However, numbers don't always explain the underlying reason. A person might misunderstand a term, distrust the next step, or expect completely different system behavior. Without additional context, the team sees the problem, but doesn't always understand why it occurred.

That is why testing results are best paired with qualitative methods. First, you can spot an issue in user behavior, and then uncover the root cause through an interview or moderated usability testing.

ChatGPT as a Tool for Working with Research Materials

To leverage AI in UX research, you don't necessarily need to adopt a specialized research platform right away. A portion of the tasks can be accomplished using ChatGPT.

For instance, at the start of a study, you can describe the product, target audience, problem statement, and hypotheses to generate an initial interview guide outline. The team can then refine the questions, remove leading phrasing, and tailor the script to specific users.

After conducting the research, ChatGPT can be used to work with the collected materials. For example, you can analyze transcripts from multiple interviews, identify recurring issues, extract user expectations, compare differences across segments, or group observations by specific themes.

At the same time, it is crucial not to treat an automatically generated output as a finalized research conclusion. If AI identifies a pattern, it should always be verified against the users' original responses.

AI as a Second Opinion on Research Findings

There is another useful scenario that is not directly tied to automation: AI can be used to sanity-check your own conclusions.

After finishing a study, the team often already has a preconceived notion of the problem. Because of this, it's easy to focus more on information that confirms the initial hypothesis while overlooking signals that contradict it.

You can feed the study structure, results, and current conclusions into AI and prompt it to find alternative explanations, contradictions, or data points that don't fit the main hypothesis.

This doesn't mean the interpretation proposed by AI will be correct. However, this approach can prompt the team to take another look at the material and challenge their assumptions before making a product decision.

One Research Result for Different Teams

Another UX research challenge arises after the analysis is complete: different team members require different levels of detail in the results.

A designer might need specific issues within user flows and the interface. A Product Manager wants to understand the impact of the problem on the product and its priorities. Developers need context for upcoming changes, while leadership cares about business implications.

AI can help adapt a single set of verified insights into different formats without having to rewrite the entire report. For example, a detailed research report can be transformed into an executive summary for leadership or a list of specific UX issues for designers.

In doing so, it is vital to maintain the connection to the original sources and ensure that automated summarization doesn't alter the core findings of the research.

Can Users Be Replaced with AI Personas?

One of the most obvious ideas for using AI is to create a user persona profile, feed it to a language model, and ask it to evaluate the product on that persona's behalf.

This approach can be useful for internal dry runs of a script. For example, a team can run through a draft interview guide to see where questions are repetitive, where context is missing, or where the conversational flow breaks down.

However, an AI's response does not represent real user behavior. A real person might misunderstand a button label, overlook an obvious element, perform an unexpected action, or use the product in a way the team never anticipated.

It is precisely these situations that often yield the most valuable UX insights. Therefore, an AI persona can be a handy tool for preparation and hypothesis testing, but not a replacement for a real user.

How to Combine Dovetail, Maze, and ChatGPT

These tools don't necessarily compete with each other. They can be utilized at different stages of the same research project.

ChatGPT can be used at the beginning to work through hypotheses, outline the research structure, and prepare materials. Maze can help validate specific product flows with users and collect behavioral and qualitative signals. Dovetail becomes invaluable when research volume grows and the team needs a centralized hub to analyze and store knowledge.

As a result, AI doesn't work in place of UX research, but inside the process. One tool helps prepare the study, another conducts the testing, and the third structures the accumulated insights.

What UX Research with AI Can Look Like

The process starts with defining the problem and formulating hypotheses. After that, the team creates a research plan, defines the target audience, and builds the research script.

In the next phase, interviews, usability tests, or unmoderated studies are conducted with real users. The recordings and responses are transformed into structured data, after which AI can help identify recurring themes, group observations, and draft an initial synthesis.

Then, a designer or researcher verifies the conclusions against the primary sources, prioritizes key issues, and formulates product recommendations. In other words, AI accelerates data processing, but the final decision remains with the team.

What AI Shouldn't Decide for the Product Team

It is easy to mistake speed of analysis for research quality. Just because AI generated a list of ten problems in a matter of minutes doesn't mean those ten problems are the ones that must be solved.

You need to understand the user's context, the frequency of the issue, its impact on the core flow, and the product's business goals. One issue might occur frequently but barely affect the outcome. Another might happen only in a single critical scenario yet completely block task completion.

Likewise, an automated summary should not become the sole source of truth. Critical decisions should always be cross-checked against original user responses, recordings, and behavior.

AI Accelerates Research, but Doesn't Replace UX Thinking

The main advantage of AI in UX research isn't that you can now do away with researchers or users. Automation allows you to spend less time on mechanical work and more time on understanding the problem.

Transcription, initial clustering of findings, or report drafting can be partially automated. But understanding why a user behaves in a certain way, how critical a problem is to the product, and which solution should be tested next must be decided by the product team.

For SaaS, CRM, ERP, and other complex digital products, this is especially crucial. The more roles, user journeys, and business processes a system encompasses, the more dangerous it becomes to make design decisions based solely on assumptions.

AI makes UX research faster, helps process more information, and reduces routine tasks. Yet it is real users who provide the context, behavior, and unexpected observations for which research is conducted in the first place.