AI is reshaping marketing research by synthesizing scattered data, flagging quality issues, and turning static reports into interactive resources, but human judgment still determines what the insights mean and which actions to take, according to practitioners at a recent Market Research Society of India discussion.
From scattered information to connected intelligence
For large companies, research reports, trackers, social analytics, and panel data often sit in silos. AI can bring these together, making insights searchable and accessible across teams. Soham Chakravorty, CMI Manager for Skin Cleansing at Hindustan Unilever, said, "AI has moved from being a source of uncertainty to becoming a practical enabler for marketing research." He pointed out that AI helps organizations connect scattered data and push insights beyond the market research department. But the technology cannot assign meaning. "AI helps amplify research functions, but human insight gives them meaning," Chakravorty said. The ability to empathize with consumers and interpret context remains a distinctly human skill.
The new data quality challenge
AI-generated survey responses are making traditional fraud-detection methods-like checking for unnatural language-less reliable. Ashwani Kumar Singh, Strategic Business Advisor at TrayiStats AI Technologies, believes the focus should not be on whether an AI wrote the answer but on whether the response is low-effort or inconsistent. Machine learning models can now analyze typing rhythm, cursor movement, browser activity, and response patterns to assign risk scores. "The final decision, however, should remain with a human researcher," Singh said, comparing it to airport security where technology clears straightforward cases and flags others for manual inspection.
Why explainability is becoming essential
As AI enters more business decisions, researchers need to understand why a system flagged a response or made a recommendation. Singh emphasized that AI should deliver not just a score but also the behavioral signals behind it. This transparency lets researchers justify data-quality decisions to clients and builds trust in automated processes. The demand for explainable AI is rising alongside its use in marketing research workflows.
Brand health expands beyond surveys
Traditional brand tracking is being supplemented by digital behavioral signals. Faisal Khan, Managing Director of TheSixth.AI, noted that search activity, social conversations, online reviews, and even how AI assistants describe a brand now shape perceptions. This creates a new dimension for brand managers: they must monitor not only consumer opinion but also how AI systems represent their brand in response to queries. As consumers increasingly turn to AI for recommendations, brand management is becoming a two-front effort.
Research as a living resource
AI can turn static research reports into interactive tools. Users can ask follow-up questions, generate charts, and create custom reports without relying on manual data pulls. This makes insights more widely available across an organization and reduces the risk that research is archived and forgotten. Insights become a continuously usable resource rather than a one-time deliverable.
Human judgment retains the final call
Despite AI's growing capabilities, the defining edge remains human expertise. The technology can process more information and scale faster, but it cannot independently gauge cultural nuance or emotional weight. The future model combines quality data, explainable AI, and human interpretation. Researchers will continue to decide what the data actually means and what action to take.
Why this matters for marketers
Marketers must adapt to a research environment where AI is both an enabler and a new source of complexity. Understanding how AI systems assess data quality, flag anomalies, and explain their decisions is becoming a core competency. Professionals who pair AI-driven efficiency with the ability to interpret context and make judgment calls will lead. Resources like the AI for Market Research Analysts Learning Path can help build the necessary skills to integrate AI without offloading the final decision to the machine.
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