Agentic AI: The Future of UX Research Workflows
- Philip Burgess
- 16 hours ago
- 3 min read
By Philip Burgess | UX Research Leader
When I first started working in UX research, the process often felt slow and repetitive. Gathering user feedback, analyzing data, and generating insights took weeks, sometimes months. I knew there had to be a better way. That’s when I began exploring agentic AI—intelligent systems that can act autonomously to support and improve UX research workflows. Since then, I’ve seen firsthand how this technology can transform the way we work, making research faster, more accurate, and more insightful.
What Agentic AI Means for UX Research
Agentic AI refers to artificial intelligence systems designed to perform tasks independently, making decisions and taking actions without constant human input. In UX research, this means AI can:
Collect and organize user data automatically
Identify patterns and trends in user behavior
Generate hypotheses and suggest next steps
Automate routine tasks like transcription and coding
This level of autonomy frees researchers from manual work and lets them focus on interpreting results and designing better experiences.
How Agentic AI Improves Research Speed and Quality
One of the biggest challenges in UX research is managing large volumes of data. Traditional methods require hours of manual effort to transcribe interviews, tag responses, and analyze feedback. Agentic AI can handle these tasks in minutes. For example, AI-powered transcription tools convert audio to text instantly, while natural language processing algorithms categorize responses by theme or sentiment.
In my experience, using agentic AI tools cut the time spent on data preparation by over 50%. This speed doesn’t come at the cost of quality. AI systems can detect subtle patterns that humans might miss, such as emerging user frustrations or unexpected behavior trends. These insights help teams make informed design decisions faster.
Real-World Examples of Agentic AI in UX Research
Several companies have started integrating agentic AI into their UX workflows with impressive results:
UserTesting uses AI to analyze video feedback, automatically highlighting key moments and emotional reactions. This helps researchers focus on the most relevant parts of user sessions.
Lookback.io employs AI to transcribe and tag user interviews, speeding up the analysis phase and improving accuracy.
Dovetail integrates AI to summarize qualitative data, making it easier to share findings with stakeholders.
These tools show how agentic AI can handle repetitive tasks while enhancing the depth of insights.
Challenges and Considerations When Using Agentic AI
Despite its benefits, agentic AI is not a magic solution. It requires careful implementation and ongoing oversight. Here are some points I learned along the way:
Bias in AI models: AI systems learn from existing data, which can contain biases. Researchers must validate AI outputs to avoid skewed conclusions.
Transparency: Understanding how AI arrives at its suggestions is crucial. Black-box models can reduce trust in findings.
Human judgment remains essential: AI supports but does not replace human intuition and creativity. Researchers still need to interpret results and make final decisions.
Data privacy: Handling user data responsibly is critical, especially when AI processes sensitive information.
Balancing these factors ensures agentic AI enhances rather than hinders UX research.
What the Future Holds for UX Research Workflows
Agentic AI will continue to evolve, becoming more capable and integrated into everyday research tools. I expect to see:
Smarter AI assistants that proactively suggest research questions or methods based on project goals
Real-time analysis during user testing sessions, enabling immediate adjustments
Greater collaboration between AI and human researchers, combining speed with empathy and context
These advances will make UX research more agile and responsive, helping teams create user experiences that truly meet people’s needs.
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