AI thematic analysis: Accelerate your workflow to qualitative insights (+ top 5 tools)

Thematic analysis is an excellent method for extracting actionable insights from qualitative data. But it’s undeniably time-consuming. You need to:

AI thematic analysis can accelerate the analysis process, reducing your time spent on the long and winding road to insights—freeing up more time to share and action those findings.

Artificial intelligence (AI) enhances thematic analysis by automating steps of the process and reducing the time needed to gain deeper insights. This makes it a great choice for resource-stretched Research teams that want to accelerate coding, theme identification, and summary creation after conducting qualitative research.

Here’s how AI thematic analysis compare to manual analysis:

AI is especially helpful in supporting thematic analysis when dealing with a large amount of qualitative user data. Sifting through so many interview transcripts is not only overwhelming, but it can tie up your resources when they’re needed elsewhere.

AI can also support thematic analysis:

Your success with AI-powered thematic analysis greatly depends on the tool you choose for the job.

Prioritize thematic analysis software that:

We realize those are a lot of boxes to tick, which is why we’ve scoured the web (and our own product page) for the top five AI thematic analysis tools. Let’s cut to it.

Maze is a leading user research platform that makes gaining actionable insights easier and faster.

It comes with a suite of moderated and unmoderated UX research methods like Interview Studies, Feedback Surveys, Card Sorting, and Website Testing—just to name a few!

With a research recruitment Panel of over 3 million testers, AI-powered analysis, and reporting capabilities, Maze covers every step of the user research process, from recruitment to insights.

Maze offers AI-powered thematic analysis for Interview Studies. After automatically transcribing your interview sessions, it groups responses by themes and finds patterns in a matter of minutes. If you’re looking to dive deep into data, Maze also supports manual analysis, sentiment analysis, and automated summaries.

Maze has a:

ChatGPT is a generative AI model developed by OpenAI. Often considered the poster child of AI models, it generates human-ish answers by analyzing a large number of web pages from the internet.

ChatGPT acts as an assistant and can complete tasks such as analyzing data or categorizing interview transcriptions into groups, codes, and themes. Using ChatGPT, you can generate answers based on instructions and the information you provide.

ChatGPT has a:

Looppanel is a user research analysis and UX research repository platform. It offers various tools for streamlining data and insight management, such as an AI notetaker, qualitative analysis, and reporting capabilities.

Looppanel has various AI-powered features, such as automatic transcription, and it can organize responses, group them into codes and themes, and extract video clips of key quotes.

Looppanel has a:

NVivo is a collaborative quality analysis platform made by Lumivero. The tool allows UX researchers to import, organize, and explore their data and extract insights with the help of AI-powered features. NVivo supports a wide range of data types, including text, audio, video, and even social media.

NVivo has AI-powered coding, automatically organizing data into overarching themes.

NVivo costs between $118 and $2,038, depending on your use case. It offers product variations for academics, governments, and commercial.

User Evaluation is an AI-driven platform designed to streamline customer research and help businesses uncover valuable insights. It offers a variety of features, from qualitative analysis to reporting, along with a range of collaboration options for quick sharing.

User Evaluation uses AI to quickly convert interview transcripts into codes, themes, and insights. Like Maze, it also offers user sentiment analysis and creates stakeholder-ready reports.

User Evaluation has a:

AI-powered thematic analysis is efficient—no doubt about it. But it’s not without ethical concerns. Factor in these considerations before you start analyzing qualitative data to ensure you’re using AI the right way.

When you add AI into your thematic analysis workflow, you’ll need to explicitly state how you’re using it in your process. Some people aren’t comfortable with their data being used by machine learning (ML) models.

Besides being upfront and transparent about AI in your written consent forms, make sure you’re also in compliance with regulatory laws such as the General Data Protection Regulation (GDPR). Implement security measures to protect user data from unauthorized access, breaches, or misuse of sensitive information.

AI makes mistakes. While supremely efficient at analysis, it’s far from the perfectly neutral robot many enthusiasts make it out to be. If there's bias in your model’s training set, there will be bias in the results, too.

Bias isn’t necessarily a product of data sets, either. Some machine learning algorithms produce skewed results because they aren’t aligned with real-world diversity.

For example, an analysis tool might be trained overwhelmingly on one demographic group, leading to skewed results that are unrepresentative of the whole population. The best way to mitigate this is to ensure diversity in your chosen participants.

Sometimes AI will generate shallow results. It often fails to see the nuances that come with body language and implied gestures. If you don’t continuously complement AI findings with human-found insights, you might end up doing your entire user base a disservice.

By over-relying on AI, UX researchers miss the chance to immerse themselves in qualitative data. And without deep knowledge of user needs, insights can end up harming the product instead of helping.

So, what does AI thematic analysis look like with Maze?

Let’s say you’ve just conducted a round of user research sessions using Maze Interview Studies. The deadline for your project looms just around the corner, and stakeholders are hungry for insights.

This scenario is where Maze’s AI-powered analysis feature can be hugely helpful. After results come pouring in, Maze lets you automatically identify themes within your transcripts.

Then, you can head on back to the results page to review your newly created themes.

Maze will show you:

Think it missed something? You can always go back and create manual themes based on your transcripts and assign responses as desired.

Maze also automatically generates summaries and reports based on your new themes and insights—taking you from user interview transcripts to shareable reports with just a few clicks!

AI puts your transcripts on the fast track to qualitative insights, minimizes errors, and handles large data sets at scale. While it can certainly augment thematic analysis, AI can’t do the whole thing for you—especially if you want in-depth insights.

The best way to do it is with a UX research tool like Maze. Not only will you be able to automate thematic analysis, but you can also recruit, design, deploy, and analyze all your UX research projects with the help of Maze’s AI-powered features.