Move over sticky notes—AI is now just as common as team collaboration when it comes to analyzing research. A new global study by user research platform Lyssna reveals that 54.7% of research professionals are using AI-assisted tools to help synthesize findings—virtually tying with traditional collaboration (55.0%) as the top method of turning feedback into insights.
Based on responses from 300 research practitioners worldwide—including researchers, designers, marketers, and product managers—Lyssna’s report, From Chaos to Clarity: How Teams Synthesize Research in 2025, paints a clear picture: AI has officially gone mainstream in the insights business.
Welcome to the AI-First Research Era
The majority of AI adopters are using tools for summary generation (82.9%) and pattern recognition (61.0%)—tasks that previously chewed through hours of human time and attention. In an industry where 65.3% of research synthesis now happens within 1-5 days, speed is everything. And AI is delivering.
“AI is being integrated into research workflows not as a replacement for human judgment, but as a tool that handles repetitive tasks and surfaces data,” said Mateja Milosavljevic, CEO of Lyssna.
In other words, AI is cleaning the house so humans can decorate. It’s the assistant—not the strategist.
Who’s Doing the Synthesizing?
One of the report’s more striking findings is the broad participation in research analysis. It’s no longer the sole domain of UX researchers: designers, PMs, and marketers are just as likely to be knee-deep in user data. This trend reflects the ongoing democratization of research—a movement that’s been growing since remote tools and agile product cycles went mainstream.
The takeaway: research synthesis is becoming a shared responsibility, not a specialized silo.
Trust Issues? Still a Thing.
Despite AI’s growing role, trust in AI for strategic decisions remains cautious. While 82.9% use AI to generate summaries, only 47.6% feel confident letting AI produce actionable recommendations. That’s a clear signal that human interpretation and context still matter—a lot.
“AI is great at mining data and pattern-matching,” Milosavljevic noted, “but humans are key to sense-making and strategic alignment.”
This echoes a broader industry truth: while AI can accelerate analysis, the leap from data to decision still belongs to people—at least for now.
Implications: AI as the New Baseline
For teams still debating whether to bring AI into their workflow, Lyssna’s study sends a clear message: you’re already behind the curve. With AI adoption becoming the norm—not the exception—product and research teams that resist automation risk being outpaced in both speed and strategic agility.
That said, the most effective teams aren’t fully automating, but instead blending AI capabilities with human oversight. The hybrid approach is proving to be the sweet spot: AI gets you to the insight faster, while people ensure it’s actually the right insight.
As 2025 continues to unfold, the question is no longer “Should we use AI in research?”, but rather “How do we use AI responsibly, effectively, and collaboratively?”
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