Every market researcher today is facing one urgent question: How will GenAI be reshaping the researcher’s role in 2026?

This post launches a three-part series that uses scenario planning, a powerful framework for navigating multiple possible futures, to help answer that question. Rather than trying to predict a single outcome, scenario planning helps organizations prepare for a range of plausible futures by understanding which forces are already in motion and which remain uncertain.

Series Structure

• Part 1 (this article) maps the key trends and forces shaping market research in 2026 and establishes the foundation for our scenarios.
  
• Part 2 uses those trends to construct four possible futures for researchers through structured scenario planning.
  
• Part 3 shows how to build your own scenario plan, mapping your organization’s conditions, constraints, and culture onto actionable futures.

One truth is already clear: the market research industry is standing at a critical crossroads. How researchers embrace or resist GenAI in the near term will fundamentally shape their relevance, influence, and value in the years ahead.

GenAI Market Research Trends 2026

These are the key trends already reshaping market research. Some are already taking hold, while others may accelerate, stall, or combine in unexpected ways. Together, they set the boundaries of what futures are plausible and provide the foundation for the scenario planning in the next part of this series.

Trend 1: Agentic AI’s Impact on Research Workflows

Impact: Very High
Certainty: Medium

Agentic AI is beginning to automate meaningful portions of research work, particularly tasks many researchers experience as drudgery such as drafting instruments, coordinating workflows, synthesizing inputs, and producing first-pass outputs. What remains uncertain is how deeply this automation will extend beyond execution into core research judgment. In 2026, the question will not be whether agentic AI changes workflows, but how much of the research process it is trusted to run autonomously.

Trend 2: Speed Above All Becomes the Dominant Client Priority

Impact: Very High
Certainty: High

As AI reshapes expectations, clients are increasingly prioritizing speed over other dimensions of the traditional research iron triangle. Faster turnaround is no longer a bonus. It is becoming table stakes. This shift pressures researchers to deliver insights quickly, often before questions are fully formed, redefining how rigor, depth, and value are perceived.

Trend 3: Trust in AI Outputs Remains an Open Question

Impact: Very High
Certainty: Low

While AI-generated insights are proliferating, confidence in their reliability remains a significant hurdle. A critical distinction is emerging between AI-derived outputs generated solely by the model (such as prompt-based syntheses or synthetic responses), and AI-assisted work that is grounded in raw transcripts, survey data, or documented evidence. Buyers and stakeholders are increasingly demanding transparency on how these outputs are validated and reviewed. Ultimately, the industry’s level of trust will determine how deeply automation can be integrated into the research lifecycle.

Trend 4: AI vs. Human Interviewers

Impact: High
Certainty: Medium

AI interviewers are increasingly used for structured, early-stage qualitative work, raising questions about how far their capabilities will go. Advances in audio, video, and conversational AI suggest broader use, but uncertainty remains around depth, adaptability, and credibility. In B2B research especially, the balance between AI-led and human-led interviewing is still being negotiated.

Trend 5: The Researcher’s Role Shifts from Executor to Orchestrator

Impact: Very High
Certainty: High

As AI takes on more executional tasks, the researcher’s role is shifting toward orchestration. This includes designing workflows, validating outputs, integrating inputs, and guiding interpretation. Unlike many other forces in play, this role evolution is relatively certain, even if the pace of change varies by organization.

Trend 6: Self-Service Insights Become the Default Behavior

Impact: High
Certainty: Medium

Stakeholders are increasingly turning directly to AI tools to answer questions on demand, bypassing traditional research workflows altogether. Whether this behavior leads to better decisions or greater confusion depends on governance, data quality, and how well insight systems are designed.

Trend 7: Synthetic Data Expands in Both Qual and Quant Research

Impact: Medium to High
Certainty: Medium

Synthetic data is moving beyond niche applications into mainstream research use cases, from modeling scenarios in quantitative research to supplementing sparse qualitative inputs. Acceptance is growing, but standards for validation, transparency, and appropriate use are still evolving.

Trend 8: AI’s Ability to Read Human Emotion Comes Under Scrutiny

Impact: High
Certainty: Low

AI tools increasingly claim to detect emotion through tone, facial expression, sentiment, and behavior. While technically impressive, confidence in these interpretations remains uneven. Belief in emotional AI, rather than technical capability alone, will determine how influential this force becomes.

Trend 9: “Good Enough” Becomes a Strategic Threshold

Impact: Very High
Certainty: Low to Medium

As AI-generated syntheses improve, many teams begin to ask whether insights that are good enough are sufficient for decision-making. If widely accepted, this mindset would fundamentally reshape demand for bespoke research. Whether and where organizations draw this line remains uncertain.

Trend 10: The Decline of the Traditional Research Deck

Impact: Medium
Certainty: Medium

AI is accelerating the production of visuals, summaries, and narratives, reducing reliance on static slide decks as the primary insight deliverable. Decks are unlikely to disappear entirely, but their role is changing as more interactive and conversational formats emerge.

Trend 11: Bespoke AI Models Enter the Research Stack

Impact: Medium
Certainty: Low

Some organizations are investing in their own AI infrastructure, training models on proprietary data and internal knowledge. Where this happens, research increasingly involves educating and refining AI systems rather than delivering insights directly to leadership. Adoption is likely to be uneven by 2026.

Trend 12: Researchers Build Bespoke Tools Through Vibe Coding

Impact: High
Certainty: Medium

As GenAI lowers the barrier to software creation, more researchers are building lightweight, bespoke tools through vibe coding. This shifts part of the researcher’s value from tool user to tool creator. How far this behavior scales, and how organizations govern and support it, remains uncertain.

Trend 13: Hallucination, Validation, and Liability Gain Visibility

Impact: Very High
Certainty: Low

As AI-generated content influences real decisions, errors and hallucinations become more visible and more consequential. High-profile failures or liability concerns could significantly slow or reshape adoption. Whether this becomes a defining force depends on how often and how publicly things go wrong.

Trend 14: Cross-Border Research Becomes Easier and Faster

Impact: Medium
Certainty: Medium to High

AI-driven translation, moderation, and synthesis are reducing many traditional barriers to cross-border research. This will expand access and speed, though cultural nuance and contextual understanding will remain ongoing challenges.

Trend 15: Continuous Listening Challenges Point-in-Time Research

Impact: High
Certainty: Medium

Always-on data streams and AI-enabled analysis are pushing organizations toward continuous listening models rather than discrete studies. Adoption is growing, but many teams are still working through how to manage signal overload while preserving strategic interpretation.

GenAI in Market Research 2026: From Trend to Transformation

Together, these trends form the backdrop for preparing research practices for the GenAI reality of 2026. They reflect a critical shift as GenAI moves from experimentation to expectation, reshaping workflows, client demands, and the fundamental definition of the researcher’s role.

These forces do not point to a single outcome. Instead, they create a range of plausible futures, depending on how strongly each one plays out and how they interact with one another.

Next in the Series: Scenario Planning for the AI Era

In Part 2, we will use these trends as building blocks to explore four possible futures for market research in 2026, and what each reveals about organizational readiness for the GenAI era.

In Part 3, we will show how to build your own scenario planning exercise, mapping your team’s conditions, constraints, and ambitions onto a clear and actionable plan for the next 12 months.

Because the future of research will not be determined by GenAI itself. It will be determined by how researchers choose to use it, and which future they choose to prepare for.