AI for market research: what consulting and private equity teams want

For private equity and consulting firms, the competitive advantage in research is not in just collecting more data, but the ability to turn this information into meaningful insights. Whether performing commercial due diligence, testing investment hypotheses, or developing growth strategies, teams are expected to synthesize vast amounts of information, identify telling signals, and deliver clear recommendations under increasingly compressed timelines.

It's no surprise, then, that IncQuery's State of Primary Research in Consulting and Investment Research showed an overwhelming interest in AI survey tools and capabilities that help make sense of data and put it to use.

Here's what respondents said:

• AI-powered data analysis (82%)
• AI-powered secondary research and synthesis (75%)
• Advanced analytics and visualization (66%)
• AI agents for end-to-end research and due diligence (57%)

(Respondents could select more than one option.)

AI expectations

The strongest interest in AI is for capabilities that enhance, not replace, parts of the research process. The results show growing demand for tools that help teams uncover deeper insights, connect information from multiple sources, and communicate findings with greater clarity. This reinforces the idea that AI augments human involvement and expertise. This growing interest in AI for market research reflects a broader shift toward tools that support judgment rather than replace it.

The top three ranked AI capabilities above are all about extracting greater value from the information teams have already collected. Large amounts of data are not valuable if they can't be analyzed efficiently to uncover actionable insights. The competitive advantage comes with making smart, evidence-based decisions, and AI can be the engine that makes this possible.

AI survey tools in practice

AI has a place in every phase of a research project, not just to analyze data. With AI survey tools, teams can see benefits like:

• Richer respondent context: AI that asks dynamic follow-up questions when respondents provide open-ended answers across text, audio, and video response types.
• Stronger initial drafts: AI that turns research objectives, source materials, project briefs, Word documents, templates, and prior surveys into a structured first draft.
• Question refinement: AI that allows users to improve questions and answer choices with simple prompts, without manually rewriting each item.
• Smarter survey expansion: AI that adds new questions tailored to the team's objectives, based on survey context and research best practices.

Quality over efficiency

While respondents expressed strong interest in AI, they're unwilling to trade data reliability for efficiency. Even as AI adoption grows, data reliability remains non-negotiable: teams aren't willing to sacrifice trust in their findings for speed. When respondents ranked future research strategy priorities, improving the quality of insights was significantly higher than other options:

• Improving quality of insights (63%)
• Increasing speed (14%)
• Increasing automation (8%)
• Eliminating low-quality responses or bots (6%)
• Reducing costs (4%)
• Enhancing data security (2%)
• Expanding geographic reach (1%)
• Other/don't know (2%)

The bottom line

The State of Primary Research in Consulting and Investment Research shows that interest in AI is strong for consulting and private equity research, especially as a way to enhance analysis. As teams balance the risks and benefits of using AI, they are prioritizing quality as their top research objective.

Get the report here.