A new study finds that 98% of researchers now use AI as part of their day-to-day workflow. What does this mean for the future of the insights industry? Is job security under threat? Or is automation empowering researchers?
Artificial intelligence has been subtly reshaping the role of researchers for some time now. The true extent of this new world of insights has now been revealed in research from QuestDIY and The Harris Poll .
AI is embedded into every aspect of our lives
The undercurrent of AI has permeated into all aspects of our lives and for researchers, the reality is no different. A study of more than 200 research professionals found that the use of AI is omnipresent and on the rise – integrating itself into every aspect of their plans and protocols.
The vast majority of researchers (98%) reported using AI at least once in their work over the past year, with 72% saying they use it at least once a day or more (39% daily, 33% several times per day or more).
Welcoming a brave new world of insights
This widespread integration has been welcomed on the whole. A large majority view the proliferation of AI as positive, with 89% saying AI has made their work lives better (64% somewhat; 25% significantly).
The research finds that AI is mostly being used to speed up how research is carried out and delivered. Researchers report using AI mainly for jobs such as analysis and summarizing.
What are researchers mainly using AI for?
- Analyzing multiple data sources (58%)
- Analyzing structured data (54%)
- Automating reports (50%)
- Coding / analyzing open-ends (49%)
- Summarizing findings (48%)
AI as a ‘co-analyst’
However, there are concerns around data privacy, accuracy, and trust. Research professionals recognize AI’s potential, but also its limitations. The industry doesn’t view AI as a replacement, but more of an apprentice of sorts.
“Researchers view AI as a junior analyst, capable of speed and breadth, but needing oversight and judgment,” says Gary Topiol, Managing Director, QuestDIY.
Giving them more time for strategy and innovation
Despite needing oversight and careful management, the efficiency gains are real. More than half (56%) say AI saves them 5 or more hours per week. This is because AI enables faster analysis with 43% saying it increases the speed of insights delivery. Plus, many of the researchers (44%) say that it improves accuracy and surfaces insights that might otherwise be missed (43%).
This extra time has empowered researchers to spend more time on strategy and innovation. More than a third of researchers (39%) said that this freed-up time has made them more creative.
Human led, AI supported
AI is not only accelerating tasks for insight professionals, but also enriching the quality and impact of insights delivered. The ideal model is human-led research supported by AI; where AI tackles the repetitive tasks (coding, cleaning, reporting) and researchers focus on interpretation, strategy, and impact. Humans remain in charge, with AI doing the heavy lifting.
However, despite this, there are legitimate barriers to adoption, which include data privacy and security (33%), effective training (32%), and having the time to learn and experiment with these tools (32%).
Quality insights, not just data volume
This suggests that it’s more of an enablement and governance issue than it is a tooling problem, i.e. it’s not about layering on tools, but more about ensuring the data is credible and researchers are trained to spot abnormalities. Indeed, the number one frustration levied at AI from the researchers spoken to was accuracy and the risks of hallucinations. Almost a third (31%) say they had to spend validating outputs due to concerns around validity.
But the more researchers rely on AI to speed up deliverables, the more likely acute errors (hallucinations) will be felt. As the report highlights, at the macro level, AI is revolutionizing decision-making, personalizing customer experiences, and speeding up product development.
For researchers, this creates both pressure and opportunity. Businesses now expect agile, real-time insights – and researchers must adapt their skills and workflows to meet that demand.
Rather than focusing on the quantity and sheer volume of research insight professionals are able to deliver with these tools, we should instead be looking at quality. This includes QAing data, but could start to involve bringing insight professionals into the C-suite more. Not just relying on research to tell organizations what is happening and why, but also what should we do next?
This is where the humans take center stage.
The researcher of 2030
If we’re confident that AI can be relied on to deal with the grunt work, it can allow the researcher role to shift up the value chain as AI takes over the cleaning up of data, coding, first-pass insights, and much more. The researcher role will then shift into interpreting the data, defining the contexts, strategic storytelling, building out ethical models, and being the voice of reason.
By 2030, researchers expect that AI will be helping them with a myriad of tasks that their time would otherwise be taken up with. Tasks such as generating survey drafts and proposals (56%), supplying synthetic or augmented data (53%), automated cleaning, setup, and dashboards (48%), and predictive analytics (44%). To do this effectively they’ll need to ensure that AI is embedded into their workflow. They’ll need to start treating AI not as a plugin, but as core infrastructure for analysis, research, reporting, survey builds, and analyzing open-ended questions.
As Topiol says, “The future is human-led, AI-supported. “AI can surface missed insights – but it still needs a human to judge what really matters.”
‘More opportunity than threat’
That may be why many researchers aren’t concerned about AI coming for the jobs. Just 29% cite job security as an issue. On balance, many see AI as more of an opportunity than a threat. The majority (59%) view it as primarily a support, and 36% see it as an opportunity. Importantly, 89% say AI has already improved their work lives.
And arguably it may even lead to fresh opportunities and elevated roles as strategic leaders within businesses and organizations. As researchers become unburdened by analysis-heavy workloads, it’s time for them to step out from the shadows and take the spotlight.
Translating data into decisions that shape organizations
The researcher of the future won’t be defined by technical execution alone, but by
strategic judgment, adaptability, and storytelling. Their role will be to supervise AI systems, ensuring rigor, accuracy, and fairness. They’ll be expected to guide stakeholders with culturally sensitive, ethically grounded narratives. And translate data into decisions that shape business strategy.
Research teams of the future will require ‘AI Insights Agents’ to work alongside human Research Supervisors and Insight Advocates, complementing their roles.
As we look ahead to 2030, the researcher of the future needs AI not to do their job, but to enable them to become more efficient and strategic with their job. Those who are using AI correctly will find that it frees them up from day-to-day legwork of analysis to become more strategic and creative in their output. They’ll start to evolve more into leaders who use the insights they’ve gleaned to influence decision making upstream. They’ll be uplifted by their AI co-analysts, not replaced by them.
Read next: Study Reveals a Triple Threat: Explosive Data Growth, AI Agent Misuse, and Human Error Driving Data Loss
by Web Desk via Digital Information World
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