B2B marketers are entering an era where AI acts as a co-analyst—speeding analysis, connecting structured and unstructured data, and automating first-pass reporting—while human judgment remains the arbiter of truth.
The Harris Poll’s “Role of the Researcher in 2026 and Beyond” found AI is now entrenched across research workflows, with widespread daily use translating into faster market sensing, quicker message iteration, and more agile decision support for complex B2B buying committees.
Yet the report makes clear that trust is the growth constraint. Accuracy, bias, privacy, and explainability are top concerns, and leading teams are responding with a human-led, AI-supported model: researchers as “Insight Advocates” who validate outputs, align findings to industry and role nuances, and translate insights into clear narratives for executives. Scalable enablement— secure platforms, guided workflows, auditability, and training— proves as important as the AI capabilities themselves.
Gary Topiol, Managing Director, QuestDIY, The Harris Poll and Erica Parker, Managing Director, The Harris Poll, answered our questions about how B2B organizations are adapting, how this shift enables continuous message testing, sharper portfolio and partner evaluations, faster brand and campaign readouts, and providing rapid feedback loops that inform revenue decisions.
Demand Gen Report (DGR): Based on your findings, what are the top three “highest ROI” AI research use cases specifically for B2B marketers in the next 12 months?
Gary Topiol: According to our data, the highest ROI can be found in data analysis, including looking across multiple data sets, analyzing both structured and unstructured data and automating insight reports. Not only does this save time, but it also frees up professionals to focus on more strategic tasks.
The top three benefits currently are improved accuracy (44%), surfacing otherwise missing insights (43%) and improved speed of insights (43%) Using AI to surface insights in an automated way enables researchers to think more strategically about how these insights can be used to optimize B2B marketing.
DGR: Any benchmarks for how frequently top teams retest messaging as markets shift?
Erica Parker: Although we didn’t ask about message testing specifically, we do know that AI adoption in market research is almost universal, being leveraged frequently across the research workflow but particularly to help with speed to insights.
DGR: How often are teams using AI-assisted research to pressure-test messaging by industry/role?
Parker: According to our survey, nearly all (98%) of market research professionals are using AI and quite frequently at their job. Over 7 in 10 (72%) are using it at least daily if not more often. They’ve leveraged it most often for data analysis (structured and unstructured) and findings, specifically analyzing multiple data sources together (58%), analyzing structured data (54%), automating insight reports (50%), analyzing open ended text (49%), summarizing findings (48%).
DGR: What explanation frameworks helped win over skeptical B2B executives— audit trails, side-by-side human vs. AI reasoning, confidence scoring?
Topiol: The prevailing view is to keep ‘humans-in-the-loop’ with 60% of respondents saying that their workflows are human led with either some or significant AI support. By taking on the role of ‘AI Supervisor’ confidence is more likely to be maintained.
DGR: For B2B orgs, which skill gaps mattered most (storytelling, prompt engineering, experimental design)? How are top teams structuring “AI co-analyst” roles alongside marketers and researchers?
Parker: The next generation of market research professionals will need to be far more versed in AI with tool proficiency (35%), an understanding of its ethical use (35%), and critical thinking to fact check its output (33%). Coupled with prompt writing (32%), programming skills (32%), and continued attention to detail and quality control (33%).
