New data finds research teams are splitting into two tiers – and the gap is accelerating
Editor’s note: Ali Henriques is executive director of Edge at Qualtrics.
A year ago, the question for research teams was simple: Are you using AI? Today, that is not even a question. With 95% of researchers now using AI tools regularly or experimenting with them, adoption is universal.
The new question – the one that determines competitive advantage – is how you're using AI. And Qualtrics 2026 Market Research Trends report, based on insights from over 3,000 researchers across 17 countries, shows the research landscape is splitting into two distinct tiers, and the gap between them is widening fast.
The budget and influence divide
Traditional research teams aren't just falling behind; they're losing strategic relevance at an alarming rate. Fifteen percent of traditional researchers report their organizations now rely less on their insights than a year ago, nearly four times the overall rate. Thirty-seven percent see flat or declining demand for their research, compared to just 20% of cutting-edge teams.
Meanwhile, 72% of teams deploying synthetic research, agentic AI and purpose-built capabilities report their organizations depend on research significantly more than a year ago. That increased reliance translates directly to budget gains and expanded authority.
This isn't about AI adoption in the abstract. It's about what kind of AI you're adopting and how strategically you're deploying it.
The shift from generic to purpose-built AI
Here's what separates the winners from the rest: researchers are increasingly abandoning general-purpose AI tools in favor of specialized, purpose-built platforms.
Just a year ago, 75% of researchers used general-purpose AI tools like chatbots. Today, that's dropped to 67%. In the same period, adoption of AI capabilities embedded directly in research platforms has grown from 62% to 66%.
This shift matters because purpose-built AI understands research workflows, research data structures and research quality standards in ways generic tools never will. The teams moving to specialized capabilities aren't just working faster, they're doing fundamentally different work.
For example, researchers adopting synthetic survey responses are conducting significantly broader, more strategic work: they are 11% more likely to engage in early stage innovation, 7% more likely to conduct go-to-market research and 5% more likely to perform final product testing. This means that in addition to enhancing their efficiency and lowering costs, these teams are expanding the scope and strategic impact of research with the same resources.
Among researchers who have adopted synthetic data, 45% now view it as their most reliable data source, outpacing traditional online panels. That number may shock some long-time industry professionals. As researchers continue to experiment and get comfortable using synthetic data, it will represent a fundamental shift in how research gets done.
Agentic AI: The next competitive frontier
While some teams are still figuring out basic AI workflows, others are already deploying the next wave: AI agents that can run studies and analyze output with guidance and guardrails.
Fifteen percent of researchers are actively using AI agents today, and 78% believe these agents will handle more than half of research projects end-to-end within three years. The impact is already measurable: 84% of researchers regularly using agentic AI report significantly more efficient operations, compared to 68% who haven't tried it yet.
But the real transformation isn't about efficiency. It's about democratization. Research agents are closing the gap between how much research stakeholders want and how much research teams can deliver, not by making researchers work faster, but by enabling self-service insights.
Product managers can test concepts without submitting tickets. Marketing teams can analyze sentiment without waiting for reports. Executives can explore data without going through intermediaries. One researcher can now enable dozens of stakeholders to find answers to routine questions, freeing the research function for complex, high-stakes work that genuinely requires expert judgment.
Thirteen percent of researchers now name democratizing insights as the single biggest benefit of AI. As agentic capabilities mature, that number will only grow.
The hidden risk for AI-forward organizations? Leadership-team misalignment
Here's the challenge even AI-forward organizations face: a growing disconnect between how leaders and individual contributors experience AI transformation.
Seventy-two percent of research leaders believe their organizations rely more on research than a year ago, while only 44% of individual contributors share that view. Leaders report AI has revolutionized their research processes (39%), but just 19% of frontline researchers agree.
There is a similar gap in terms of perceived experience using new AI solutions. Eighty-four percent of leaders have experimented with synthetic research compared to 68% of individual contributors. Sixty-eight percent of leaders consider themselves synthetic data experts versus 41% of individual contributors.
This misalignment isn't just a communication problem. It's costing organizations real competitive advantage. When leadership believes they're innovating and teams remain skeptical, expensive AI tools go underutilized, pilot projects stall and competitors with better organizational alignment move faster.
The teams capturing budget increases and strategic influence aren't just the ones adopting AI fastest. They're the ones bringing their organizations along, establishing shared definitions of success, investing in hands-on enablement and addressing job security concerns directly rather than pretending AI won't change roles.
Keeping pace with AI innovation
The methodologies are proven. The tools exist. The competitive gap between organizations that move decisively and those that hesitate is growing and it's becoming increasingly difficult to close.
Traditional research teams viewing content generation as cutting-edge are already losing ground to teams embracing conversational analytics and agentic workflows. Research teams that can't demonstrate ROI, expand strategic scope or keep pace with AI innovation are watching their budgets stagnate and their organizational influence erode.
The question for 2026 isn't whether to adopt AI. It's whether you're continuously innovating with purpose-built capabilities that transform how research delivers value and whether your entire organization, from leadership to frontline teams, is aligned enough to execute on that transformation.