By 2026, market research won’t be defined by tools, methodologies, or new data streams. It will be defined by something far simpler: the choices researchers make about how to work with artificial intelligence.
By Erica Parker Managing Director at The Harris Poll
AI has already moved from hype to habit. According to a new Harris Poll and QuestDIY study of 219 U.S. insights professionals, 98% have used AI in their job in the past year and 72% are using it daily or more. When an entire sector hits that level of adoption, the question is no longer whether AI will reshape research workflows – it’s how researchers will respond.
And right now, they’re responding with a mix of pragmatism, optimism, and clear-eyed caution.
AI is already the co-analyst in the room
For years, research teams have talked about automation as a future ambition. That future arrived quickly. The most common AI use cases today are already fundamental to the job: analyzing multiple data sources (58%), analyzing structured datasets (54%), writing reports (50%), coding open-ends (49%), and summarizing findings (48%). More than half of researchers now save at least five hours a week through AI-powered tasks.
A second shift matters even more: 89% say AI has made their work lives better. Not marginally better – meaningfully better. They’re moving faster, surfacing insights they may have otherwise missed, and delivering outputs at the pace modern businesses demand.
Researchers aren’t treating AI as a novel add-on. They’re treating it as infrastructure.
But the risks are real – and they are slowing teams down
The flip side of acceleration is scrutiny. Researchers are deeply aware of the limits and liabilities that come with large language models and automated analytics.
The biggest blockers to adoption aren’t ideological; they’re practical. Data-privacy concerns top the list (33%), followed closely by time to experiment (32%), time for training (32%), and integration challenges (28%). These aren’t hypothetical scenarios – they’re daily frictions that shape what teams will and won’t hand over to AI.
And the risks aren’t just external. Researchers cite real, hands-on drawbacks:
- Increased reliance on tools that sometimes produce errors (39%)
- More time spent validating AI outputs (31%)
- New risks around data accuracy or quality (37%)
- Concerns around ethics and privacy (28%)
The critical point is this: AI speeds up the mechanical work but increases the cognitive work. For every minute saved on analysis, another minute may be added to sense-checking, validating, or explaining results to stakeholders. The gains are real – but so is the oversight burden.
What emerges is a new model: The human-led, AI-supported research team
The study makes one thing unambiguously clear. Researchers do not envision a future in which AI replaces them. They envision one in which AI becomes a junior team member – fast, broad, tireless, but in need of oversight.
That future is already taking shape
Around 29% of researchers say their workflow today is “human-led with significant AI support,” and 31% describe it as “mostly human with some AI support.” By 2030, most expect AI to become a strategic decision-support partner, taking on autonomous tasks such as generating first-pass reports, drafting surveys, creating hypotheses, analysing multimodal datasets, and helping to identify patterns across structured and unstructured sources.
Meanwhile, the researcher moves up the value chain. They become the validator, the storyteller, the ethical steward, and the advisor. They become, as the report phrases it, an “Insight Advocate” – the person who connects AI-generated output to business-critical decisions.
The researcher role is being rewritten – and the skillset is shifting fast
The report maps out a clear evolution in the researcher’s identity. What once centred on technical mastery now centres on judgment.
The standout researcher of 2026 will be:
- Culturally fluent – able to interpret signals across contexts
- Technologically confident – working seamlessly with AI, not nervously around it
- A strategic storyteller – translating outputs into decisions
- An ethical overseer – ensuring fairness, privacy, and methodological integrity
- A sharp validator – catching hallucinations, errors, and misalignments before they reach leadership
This is not a softening of the job. It’s making research more robust and strategic. Researchers will spend more time advising, explaining, interpreting, and interrogating – and less time crafting tabs, cleaning datasets, or formatting slides.
AI won’t reduce the amount of research – it will increase it
One of the most surprising findings in the study is that teams expect to do more research in an AI-driven world, not less.
AI removes the bottlenecks that traditionally slowed teams down. It enables rapid exploration, hypothesis testing, and iterative refinement. And as one respondent put it, “The faster we move with AI, the more we need to check if we’re moving in the right direction.”
This creates a paradox: speed creates demand for more rigor. Automation requires more oversight. AI may compress timelines, but it expands the responsibility to validate, challenge, and contextualize.
And this is where platforms like QuestDIY come in
The report doesn’t shy away from the reality that teams need better tools, not just better intentions. The biggest adoption barriers – time, training, accuracy, privacy – aren’t solved by generic AI models. They’re solved by purpose-built research platforms with embedded governance.
QuestDIY, built by The Harris Poll, is designed around that principle. It infuses AI throughout the workflow – building surveys, drafting questions, ingesting multimodal data, and surfacing insights – but it does so inside a secure, standards-driven environment with ISO/IEC 27001 certification. Researchers can field global studies, access high-quality samples, and generate first-pass analysis in hours, not weeks.
It doesn’t replace the researcher. It removes friction so researchers can operate at their highest value: translating data into decisions.
The real story: AI isn’t the threat – complacency is
If there is a single takeaway from this research, it’s that AI is neither savior nor saboteur. It is a multiplier.
It multiplies speed.
It multiplies scale.
It multiplies the consequences of good – or bad – judgment.
The future researcher is not someone who simply uses AI. It’s someone who understands where AI is powerful, where it is fragile, and where human insight must lead.
By 2026, the researchers who thrive will be the ones who treat AI as a co-analyst – not a crutch, not a gimmick, and not a threat. The ones who pair automation with stronger ethics, sharper thinking, and tighter storytelling. And the ones who build workflows on platforms designed for speed, security, and strategic impact.
AI will not define the future of research. Researchers will.
