Research teams produce more data today than ever before. At the same time, their influence on product decisions is declining. That's not a contradiction; it's the consequence of how most teams work: too slowly for the pace at which products are built. The Qualtrics 2026 Market Research Trends Report makes this gap measurable for the first time – and the numbers are uncomfortable.
What the Qualtrics Report Shows
Qualtrics surveyed over 3,000 research professionals across 14 countries. The central finding: Teams that don't use AI tools are four times more likely to lose organizational influence than teams that have integrated AI into their processes.
That number doesn't stand alone. 72 percent of AI-using teams report that their organization's dependence on research has increased; for traditional teams, it's 37 percent. Those who use AI get asked more often, get included more often, get heard more often. Those who don't are drifting to the margins.
AI in research is no longer a niche, either: 53 percent of respondents use AI regularly, and 90 percent have at least tried it. The question is no longer whether research teams will adopt AI, but when the last holdouts will lose their footing.
Why Research Teams Are Losing Influence
The loss of influence has a structural cause, and it's surprisingly mundane: research is too slow.
According to the Hubble State of User Research Report 2026, an average research project takes 42 days. Discovery projects need as long as 60 days – two months before the first usable insights are available. By that time, the product team has long since decided what to build.
51 percent of researchers say they need more time for analysis. 76.9 percent admit they haven't fully exhausted their insights when time was tight. And 36.3 percent name recruitment as the biggest cause of project delays.
These aren't competency problems; they're capacity problems. Most research teams are too small for the number of questions the organization throws at them. So they prioritize – and everything that doesn't make the cut gets decided by someone else. Without data, on gut feeling.
This creates a self-reinforcing cycle: Too little capacity leads to delayed results. Delayed results lead to decisions without research. Decisions without research lead management to question the value of research – which means less budget, which means even less capacity.
What AI-Using Teams Do Differently
The difference isn't that AI-using teams have better researchers. It's that they deliver faster – fast enough that their results are still relevant when the decision is on the table.
AI-moderated interviews run in parallel, asynchronously, around the clock. Instead of three interviews per day, the AI conducts thirty or three hundred simultaneously. Instead of two weeks of scheduling, conversations begin within hours. Instead of manual transcription and weeks of coding, the AI analyzes responses in real time.
This changes more than speed; it changes the role research can play within the organization. If you can deliver solid answers within 48 hours to the question that came up in yesterday's product meeting, you'll be asked to the next one. If you need two months, you won't.
In the Qualtrics report, 78 percent of respondents predict that AI agents will independently conduct more than half of all research projects by 2028. That may sound like speculation; it's the assessment of people who know the market from the inside.
57 percent of respondents report growing interest in qualitative research within their organizations. More demand, same capacity – without AI, that equation has no solution.
From Cost Center to Strategic Partner
Deloitte's State of AI in the Enterprise 2026, which surveyed 3,235 business and IT leaders in 24 countries, paints a revealing picture: 66 percent of companies report productivity and efficiency gains from AI. Only 34 percent use AI for deep process transformation.
For research teams, this means: most who adopt AI use it as an accelerator for existing workflows – faster transcription, automated summaries, more efficient literature reviews. That's useful, but it doesn't change the team's role.
The real shift happens when research stops being a project and becomes a continuous process. When you're not running a study every six months but talking to users every week. When insights don't end up in an 80-page report gathering dust in a SharePoint folder but flow directly into product decisions.
Here's what that looks like in practice: QUALLEE runs up to 150 interviews in parallel, in five languages, for under five euros per conversation. Analysis runs automatically; results are available within hours. These aren't theoretical figures – they're the costs and timelines we work with every day.
The decisive point isn't the price but what it enables: research stops being a budget line item that gets debated and becomes a natural part of every product decision.
What This Means for Your Team
PwC writes in its AI Predictions 2026: "In 2026, the AI story will shift from experimentation to execution." Deloitte's data confirms it: the adoption of agentic AI – AI that independently handles multi-step tasks – is projected to rise from 23 percent to 74 percent. Within two years.
That's not a gradual shift; it's a tipping point. And for research teams, the question is concrete: Are you among the 72 percent whose organization increasingly depends on research – or among the teams that are four times more likely to lose relevance?
Three steps that make the difference:
Identify one use case instead of overhauling everything at once. Recruitment, moderation, or analysis – one area is enough to feel the impact. The Lyssna study on research trends 2026 shows that 88 percent of researchers see AI-powered analysis as the most important trend. That's a good starting point.
Introduce speed as a metric. Not "How many studies do we run per year?" but "How quickly do results arrive after the question was asked?" Measuring that number reveals where the problem lies.
Embed research in the decision process rather than running it alongside. Insights that are finished after the decision has been made have no influence. The cadence has to match – and AI is the only lever that fundamentally changes the cadence.
Try It Yourself
QUALLEE makes qualitative depth interviews fast and affordable enough that they no longer need to be a project – they can become routine. Three interviews are free, with no credit card and no commitment.
Frequently Asked Questions
Does AI replace the UX researcher?
No. AI handles the time-intensive parts: moderation, transcription, initial coding. Strategic interpretation, placing findings in business context, and deriving actionable recommendations remain with the human. What changes: the researcher spends less time on data collection and more on the work they were trained for.
How reliable are the Qualtrics numbers?
The report is based on over 3,000 respondents in 14 countries, surveyed in Q3 2025. The sample covers research professionals across all seniority levels and industries. It's one of the most comprehensive studies on AI in market research to date.
How quickly can AI be integrated into existing processes?
For pure tool integration: a few hours. For workflow adaptation: a few weeks. The decisive factor isn't the technology but the willingness to question existing processes. Teams that use AI as an accelerator for current workflows benefit immediately; teams that fundamentally rethink how they work benefit sustainably.
What does it cost to get started with AI-powered research?
With QUALLEE, the entry cost is zero – three interviews are free. The Starter plan is 49 euros per month for 15 interviews. For comparison: a single traditional depth interview costs between 1,250 and 2,500 euros including recruitment, moderation, and analysis.
Does AI moderation work for sensitive topics?
Yes, with limitations. AI has no interviewer bias and creates no social pressure, which can be an advantage for sensitive topics. Participants in studies report higher trust and a stronger sense of being heard. In highly sensitive contexts – such as therapeutic settings or crisis intervention – a human moderator remains essential.
