Editorial
The Gist
- AI adoption is reshaping research influence and budgets. Teams using synthetic research and agentic AI report significantly higher organizational reliance, while traditional teams are four times more likely to lose influence.
- Purpose-built synthetic research is driving faster, earlier innovation. Domain-trained models now rival human panels for reliability, enabling rapid testing, earlier insight generation and shorter research cycles.
- Execution gaps threaten competitive advantage. Misalignment between leadership expectations and frontline execution leaves AI investments underutilized and slows teams that fail to operationalize new capabilities.
Like many other components of the broader business environment, the market research industry has reached an AI tipping point. Research teams using purpose-built AI, such as synthetic data and agentic capabilities, are gaining significant budget and organizational support. In contrast, traditional teams are four times more likely to report declining influence within their organizations.
The 2026 Market Research Trends report from Qualtrics reveals a critical divergence in organizational strategic influence driven by AI adoption. Rapid access to consumer insights and business intelligence is now a substantial competitive advantage. Teams using cutting-edge approaches like synthetic research and agentic AI report that their organizations rely on research significantly more than they did a year ago (72% agreement). This dependency translates directly into budget gains.
Let’s look at some key takeaways and their impacts on marketing and other business leaders.
Table of Contents
- Advanced AI Adoption Translates to Influence and Budget
- Purpose-Built Synthetic Research Drives Reliable Innovation
- A Gap in AI Confidence and Execution Costs Competitive Advantage
- Where Leaders Should Focus
- AI Is Reshaping Market Research—and the Competitive Gap Is Growing
Advanced AI Adoption Translates to Influence and Budget
First, the research found that teams utilizing synthetic research, agentic AI and purpose-built capabilities are reporting significant increases in organizational dependency on their research (72%). Conversely, teams that have not moved beyond basic AI are losing out on budget, influence and strategic relevance. Traditional research teams were 4X more likely to report declining organizational reliance.
This dynamic also means that because synthetic research is often quicker and less expensive, research is moving earlier in the innovation cycle and is able to ask potentially bigger questions. Leaders who support advanced AI enable their teams to become essential strategic partners, translating insights directly into budget gains. Unfortunately, those who lag will find it difficult to overcome this competitive advantage.
Purpose-Built Synthetic Research Drives Reliable Innovation
According to the study, early attempts to use general-purpose large language models (LLMs) such as ChatGPT or Claude for research failed due to a lack of demographic diversity and nuance in their responses, as compared to human responses. This resulted in generic responses that rarely mirrored real-world results.
However, as synthetic research has evolved, so have the tools. More finely-tuned, purpose-built models, trained on domain-specific datasets, produce results approaching close proximity to human responses. Among researchers who have adopted synthetic data, 45% now view it as their most reliable data source, surpassing traditional online panels.
Purpose-built synthetic data is also able to acts as what the study terms a "cultural radar," enabling faster concept testing against emerging trends. Researchers who have adopted synthetic data are, according to the study, 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 acceleration allows marketing teams to test messaging faster. This reduces research timelines from a week to hours, while also allowing organizations to move faster on early-stage testing before validating high-stakes decisions with costlier and more time-consuming human panels.
A Gap in AI Confidence and Execution Costs Competitive Advantage
Despite significant AI investment, many organizations still face execution challenges due to misalignment between leadership and frontline individual contributors (ICs). For example, 68% of leaders surveyed consider themselves synthetic data experts, compared to only 41% of ICs. Similarly, 39% of research leaders believe AI has revolutionized their processes, while only 19% of frontline teams agree. ICs often find that figuring out how to use new AI tools takes longer than doing the work the traditional way.
This misalignment leads to expensive AI tools going unused or underutilized and results in wasted resources, including training and platform fees. Competitors with better organizational alignment are moving faster. Because of this, the high expectation for AI efficiency from leadership often clashes with the reality experienced by ICs, who are simultaneously concerned about AI outpacing their team's abilities.
Where Leaders Should Focus
There is clearly momentum behind strategic use of synthetic research, though there are some barriers to overcome. To be successful, leaders should focus on investment, accessibility and alignment to operationalize AI effectively.
Invest in Purpose-Built, Specialist Research Capabilities
According to the study, successful leaders are shifting budget priority to research software with AI embedded and specialist capabilities like conversational analytics (49% adoption) and visual content analysis (49% adoption). These tools deliver richer insights and extract qualitative data in hours rather than weeks.
Democratize Insights via Agentic AI
Enable product managers, marketing teams and executives to test concepts, analyze sentiment and explore markets without waiting for intermediaries or submitting tickets. The goal is to remove the barrier of specialist knowledge by simply allowing teams to ask the right questions, not to outright replace them.
Ali Henriques, executive director of Qualtrics Edge says, “the human is still very much required and necessary to act on the data, coming to conclusions, making decisions, and then acting upon them.”
This integration points to a future where, as 78% of researchers predict, AI agents will run over half of all research projects by 2028, highlighting the imminent capacity shift.
Related Article: Agentic AI Is Forcing a Rethink of Customer Experience Leadership
Bridge the Misalignment Gap
To ensure frontline teams buy-in and utilize new capabilities, organizations must establish shared definitions of success, provide hands-on training and ensure teams at every level understand both the practical application and potential of AI. Leaders must recognize that ICs may require support to overcome the initial difficulty and learning curve of new tools.
Embrace a Blended Research Approach
Use the speed and cost-efficiency of synthetic models for early-stage testing, rapid assumption validation and filling quotas. Combine this with human panels to validate high-stakes decisions, ensuring maximum confidence and efficiency. Just as organizations should not replace their researchers completely with AI, they should continue to work with humans to participate in tests. Balancing when and how to use each approach will rely on human-guided strategies.
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AI Is Reshaping Market Research—and the Competitive Gap Is Growing
As it has infiltrated so many other aspects of marketing, communications and the business at large, it is actively reshaping how research teams work. The competitive gap is widening quickly between those research teams that are embracing advanced AI, and the latecomers that will find the gaps difficult to overcome. The future of market research points to a thoughtful blending human expertise with synthetic intelligence.
This shift is not about replacing human research, but about providing researchers with powerful tools to operate at the speed and scale demanded by today’s markets. Ultimately, a shift towards greater adoption of synthetic research enables faster movement, more hypothesis testing and more confident decisions at every stage of innovation. The moment for leaders to act and align their teams around this technological shift is now, before the gap becomes insurmountable.
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