Is AI Finally Modernizing Market Research?

The global insights industry reached approximately $166 billion in 2025, according to ESOMAR's Global Market Research Report 2026. Yet the workflows inside it remain fragmented. According to Greenbook's 2026 GRIT Insights Practice Report, 73% of analytics professionals are now using agentic AI for data preparation and integration. The tools have multiplied, but the context they hold has not. Each solves one piece of the puzzle. None solves the whole thing.

Research projects often move through four stages: planning, data collection, analysis, and reporting. Within those stages sit dozens of steps. Defining objectives. Writing questionnaires. Programming surveys. Cleaning data. Running regressions. Building charts. Preparing reports. Each step often lives in a different tool, and no tool holds more than its own slice of the project. When data is scattered across platforms, context is lost. A finding produced in one system cannot easily be traced back to the question that generated it.

The cost of that fragmentation is substantial. The work that consumes the most time may be the work that adds the least interpretive value. Data can move between systems through manual processes, with no traceability to its source. Analysts often spend their days on operational tasks that could be automated. Data scientists write code to produce regression models. The value lies in understanding what the regression means.

This fragmentation is not inevitable. It is a legacy of how the industry grew, one specialized tool at a time, before AI made a unified alternative possible. Closing the gap requires more than automation. It requires a system that preserves context from the first question to the final report.

Vijay Rajan, founder of Compeers AI, spent 12 years working inside market research before moving into artificial intelligence and machine learning. That sequence matters. "When you are within a system, you are not able to correctly identify the issues of the system," he says. "But if you have been in the system, and you leave, and look at it from the outside, then you are able to identify the problems." What he saw from outside was an industry where the workflow itself had become the bottleneck.

Compeers brings planning, fieldwork, analysis, and reporting into a single platform, covering qualitative, quantitative, and advanced analytics work. The platform abstracts the technical complexity that once required a data scientist. "The value is added from analyzing what the code produced, not from writing the code," Rajan explains. A report that once took hours to assemble can now be produced in minutes. Analysts who spent their time managing tools can instead spend it interpreting findings and advising stakeholders, which is where research creates its actual value.

The speed gain is real, but it is not the point. The point is what happens when context survives the entire process. A survey question, a focus group response, and a regression output all live in the same system, connected to the same objective. Findings become traceable. AI outputs become verifiable. That distinction matters because trust in AI-driven research depends on whether the answers can be checked against the data that produced them.

The industry already senses the shift. According to the Market Research Institute International's 2026 study, 58% of insights professionals believe their function will become more important in the future. Yet only about 1 in 10 say AI is fully embedded in their standard workflows, and no single AI use case exceeds roughly 40% adoption. As one senior insights leader quoted in the study put it, insights teams must move up the value chain to stay relevant. The tools to do that now exist. The question is whether the industry will consolidate around them.

The future of market research will not be defined by how many platforms a team can afford, but by whether its tools preserve the context that makes findings usable. AI has made that consolidation possible. The firms that act on it will free their researchers to do the work that machines cannot: interpret, advise, and decide.