Global studies have revealed a surge in AI adoption in user research, but human cooperation remains essential

Machine Learning


A comprehensive global study of 300 research experts reveals that artificial intelligence is rapidly centered at the heart of research analysis, with 54.7% of practitioners currently effectively linked to traditional team collaboration methods (55.0%) as the most common synthetic approach.

From chaos to clarity: In 2025, the team integrates research, published by user research platform Lyssna, researchers, designers, product managers and marketers to understand how teams can translate raw user feedback into actionable insights.

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Important findings:

  • AI integration is mainstream: Over half of researchers incorporate AI tools into their analytical workflows, primarily generating an overview (82.9% of AI users) and identifying patterns (61.0%).
  • Speed ​​is important: Almost two-thirds of research integration (65.3%) are completed within 1-5 days, with time-consuming manual work cited by 60.3% of practitioners as their biggest complaints.
  • Democratization continues: Research integration goes far beyond the role of dedicated research as designers, product managers and marketing professionals are actively involved in analyzing user data.
  • Confidence remains high: Despite the challenges, 97% of participants expressed at least moderate confidence in the synthesis process.

“Our research shows some interesting points,” he said. Mateja MilosavljevicCEO of Lyssna. “AI is not an alternative to human judgment, but is integrated as a tool to process repetitive tasks and surface data, and as a tool to free people to connect more dots.”

Human surveillance remains essential

Although AI is excellent at early data processing, practitioners maintain a strong preference for human surveillance in interpretation. Only 47.6% of AI users trust technology to translate insights into actionable recommendations, compared to the 82.9% they use to generate summary.

“We found that the most successful teams are those that can balance AI capabilities with human insights,” Mateja added. “AI is good at mining data and pattern matching, but humans are key to sensemaking and strategic alignment.”

Also Read: The role of AI in automated dental treatment planning: From diagnosis to prosthetics

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