AI Revolutionizes Market Research

AI Transforming Market Research

Companies have invested heavily in market research for decades. They have often been slowed down by surveys and biased panels, which has led to delayed insights. Each year, billions are spent on market research. However, very little of that is allocated to software.

There is a shift from labor spend to software with AI. Some AI companies are using speech models to create AI survey platforms. These platforms perform video interviews and use LLMs to analyze the results. These AI-enabled startups are changing how organizations understand customers, make choices, and scale.

Some AI research firms are replacing the traditional survey and analysis process. Instead of using panels and asking for opinions, they simulate societies of AI agents. These agents can be observed, queried and experimented with, as they model human behavior. Market research is evolving into a continuous advantage.

The Evolution of Customer Research

Customer research has integrated software gradually. Research in the 1990s involved manual data collection and analysis. In the early 2000s, companies such as Qualtrics and Medallia introduced online surveys, real-time analytics, and mobile surveys. They developed customer and employee experience management tools using surveys. SurveyMonkey made quick surveys more accessible, but this resulted in fragmented efforts and inconsistent methods. These tools lacked the needed scale for enterprise research.

Consulting firms like McKinsey created divisions to implement software research tools for insights and customer segmentation. These projects took months, cost millions, and used biased panels. It takes weeks to recruit participants, conduct surveys, analyze results, and create reports. Survey results are delivered without opportunities to dive deeper. Most enterprises use quarterly research, which fails to provide ongoing insights. Small ideas often go untested because traditional research is expensive. Companies can get stuck with outdated tools.

The Rise of UX Research Tools

In the late 2010s, UX research tools emerged that were designed for product teams. Companies started integrating user research into development. Tools such as Sprig, Maze, and Dovetail enabled faster decisions through usability tests, in-product surveys, and prototype feedback. These tools highlighted the importance of integrated research. However, they were team-focused and less applicable to non-software companies.

AI-Native Research and Generative Agents

AI-native research builds on UX research, and insights are applicable across teams and industries. AI has accelerated surveying and reduced its cost. Surveys can be generated and adapted quickly. Analysis now happens in hours instead of weeks. Insight libraries identify patterns and extrapolate signals. AI makes research accessible to smaller companies and broadens the types of decisions that can be informed by data. AI-powered tools are now used across marketing, product, sales, customer success, and leadership teams.

Even AI-powered surveys are limited by the variability of human panels. Generative agents, detailed in the paper “Generative Agents: Interactive Simulacra of Human Behavior,” show how simulated characters powered by LLMs can display human-like behavior using reflection, memory, and planning. One commercial use for this is market research.

Simulating Consumer Behavior with AI

For example, before launching a skincare product in France, a company could simulate thousands of agents based on French beauty consumers. These agents would be seeded with data from reviews, CRM, social listening (such as TikTok trends), and past purchases. The agents could interact, view influencer content, shop in virtual stores, and post opinions on social feeds, evolving as they gain information.

Sophisticated techniques make these simulations possible. Agents are based in memory architectures grounded in qualitative data. Methods like RAG and agent chaining enable multi-step decision-making, creating simulations that reflect customer journeys. Fine-tuned models enhance agent behavior. Simulation startups like Simile and Aaru indicate the future: always-on populations that act like real customers.

Agentic simulation reinvents research and decision-making. It also overcomes traditional research limits by integrating into workflows. This represents a leap in fidelity. Companies that succeed will master distribution and adoption. Qualtrics and Medallia prioritized adoption and loyalty.

The Importance of Accuracy and Speed

Accuracy is important, especially when AI tools are compared to human research. There are no established benchmarks, so companies define their own metrics. Success involves reaching a threshold that is good enough for the use case. Many CMOs are comfortable with outputs that are directionally accurate, especially since the data is cheaper, faster, and updated in real time.

Startups can move quickly and become embedded in workflows. However, they must refine their products as benchmarks emerge. The risk lies in over-engineering for accuracy. Startups that prioritize speed and integration can set the standard. AI-native research companies are better positioned to change market research. Legacy firms lack the automation infrastructure. AI-native players are incentivized to push boundaries. To drive impact, insights must be applicable across teams. The era of lagging research is ending. AI-driven market research is changing how we understand customers.

Companies that adopt AI-powered research tools will gain insights, make better decisions, and unlock a competitive edge. As shipping products becomes faster, the real advantage lies in knowing what to build.