Consumr.ai outlines how AI systems are reshaping consumer intelligence

New Delhi: Consumer intelligence is undergoing a structural change as established research frameworks struggle to reflect how decisions are now formed, according to a new report by Consumr.ai.

The report, TwinSights: The Consumer Intelligence Trends Shaping 2026, argues that static personas and post-campaign analysis are increasingly being used to explain outcomes after they occur, rather than capturing how consumer behaviour develops in real time. It notes that decision-making has become fragmented, context-driven and distributed across multiple touchpoints, often before brands register a measurable signal.

The study highlights that consumer journeys are becoming less linear and more influenced by “invisible audiences” and AI systems, leaving insight models rooted in fixed assumptions at risk of falling behind.

Commenting on the findings, Vivek Bhargava, Co-Founder, Consumr.ai, said, “We are witnessing a paradigm shift in the consumer research framework as predictive simulation, which marks a turning point for consumer intelligence. Instead of reporting on past outcomes, brands can now anticipate behaviour and test decisions in real time. That shift, from hindsight to foresight, is what meaningfully reduces decision risk, enabling brands to test creative, messaging, and strategic decisions before committing budgets.”

The report outlines several shifts reshaping how brands understand consumers. It notes that individuals shaping purchase intent are often not visible through standard targeting and attribution systems. As third-party signals weaken, first-party behavioural data is emerging as a strategic advantage. The study also points to AI’s growing role in discovery, making it necessary for brands to understand how decisions are interpreted and influenced by algorithmic systems.

TwinSights further suggests that consumer intelligence is moving away from episodic research cycles towards a continuously updated system driven by observed behaviour and AI-led interpretation.
A key theme in the report is the role of “invisible audiences”.

According to the study, behaviour-led intelligence frequently reveals hidden influencers shaping decisions upstream, users whose motivations do not align neatly with traditional segments, and pre-intent consumers who form demand well before search or purchase activity becomes visible. The report suggests these groups will account for disproportionate growth and brand leverage in 2026.

The study also examines how AI tools such as ChatGPT, Gemini and Perplexity are reshaping discovery and trust. It introduces the concept of Answer Engine Influence (AEI), describing it as a layer where AI-generated responses shape trust, preference and purchase intent before consumers visit websites or encounter advertising.

As third-party data signals continue to weaken, the report concludes that brands grounded in first-party behavioural data are better positioned to interpret consumer intent, influence earlier stages of the journey and be accurately represented by AI systems that increasingly mediate discovery.