While traditional market research firms are still burning the midnight oil to deliver much-anticipated research reports, a revolution is quietly taking place. Artificial intelligence (AI) is not only redefining how we understand consumers but also poised to completely disrupt this massive industry worth $140 billion.
For decades, enterprises have poured billions of dollars into market research in an attempt to better understand customer needs, yet they have been constrained by slow surveys, biased focus groups, and delayed insights. In an industry that consumes $140 billion annually, the value of software technology has been negligible. Traditional human-driven consulting firms like Gartner and McKinsey each hold valuations of $40 billion, while software platforms Qualtrics and Medallia are valued at $12.5 billion and $6.4 billion respectively. These figures only account for external expenditures.
With the rise of AI technology, we are witnessing another market that is preparing to shift its spending from labor-intensive approaches to software solutions. Early AI participants have begun leveraging voice-to-text and text-to-voice models to build AI-native research platforms that can autonomously conduct video interviews, analyze results using large language models, and generate presentations. These pioneers are growing rapidly, signing substantial contracts, and capturing budget shares traditionally allocated to market research and consulting companies.
These AI-driven startups are reshaping how organizations gain insights from customers, make decisions, and execute on a large scale. However, most startups still rely on panel providers to find survey respondents. Now, we are seeing a batch of AI research companies starting to fully replace expensive manual research and analysis processes.
Instead of recruiting human panels to ask for their thoughts, these companies can simulate an entire society composed of generative AI agents. These agents can be queried, observed, and experimented with, mimicking real human behavior. This transforms market research from a lagging, one-time input into a continuous dynamic advantage.
In the past few decades, the traditional market research field has slowly integrated software technology. In the 1990s, research was mainly conducted manually, using pen and paper to collect and analyze data. Companies like Qualtrics and Medallia introduced online surveys in the early 2000s, followed by real-time analytics and mobile device-based survey collection. These two companies utilized surveys to build deeper experience management tools around customers and employees.
Meanwhile, the rise of bottom-up self-service tools like SurveyMonkey enabled individual teams to conduct quick, lightweight surveys, expanding accessibility but often leading to fragmented efforts, inconsistent methodologies, and limited organizational visibility. These tools lacked the governance, scalability, and integration needed for enterprise-level research operations.
Consulting firms like McKinsey established specialized departments dedicated to deploying software-based research tools for large-scale customer segmentation and consumer insights. These projects typically take months, cost millions of dollars, and rely on expensive, biased focus groups. The research process usually takes weeks to recruit participant panels, conduct surveys, analyze results, and create reports. The findings are often delivered in packaged form to buyers, with little opportunity to revisit the process or delve deeper into discoveries.
Most companies still depend on quarterly surveys to guide major product launches, but this does not provide the continuous insights needed for rapid daily decision-making. Due to the high cost of traditional research, small investments and early ideas often go untested. Even companies eager to modernize find themselves trapped in outdated tools and slow processes.
In the late 2010s, a wave of user experience research tools designed specifically for product teams rather than consultants or research operations emerged. Companies began embedding user research into development cycles instead of outsourcing it. Tools like Sprig, Maze, and Dovetail enabled faster, customer-focused decision-making through unsupervised usability testing, in-product surveys, and prototype feedback.
These research tools demonstrated the importance of integrated research in modern enterprises. However, although these tools provided real-time value to software-driven teams, they were less focused on non-software companies and primarily optimized for team-level use rather than cross-functional usage. AI-native research companies built upon the progress of user experience research, with insights being immediate and applicable across teams, products, and industries, whether or not they were software-native.
AI has increased the speed of research and reduced costs. It makes it easy to quickly generate surveys and adjust questions in real time based on people's responses. Analysis that once took weeks can now be completed in hours. Insight databases learn over time, identifying patterns across projects and inferring early signals. This transformation not only makes research more accessible to smaller companies but also expands the range of decisions informed by data, from early product concepts to detailed positioning issues that were previously too expensive to investigate.
Now, AI-driven research tools are being used by marketing, product, sales, and customer success teams as well as more users in leadership positions within companies. These improvements are significant, but even AI-driven surveys are still limited by the variability and availability of human panels, often relying on third-party recruitment to reach respondents, restricting pricing control and differentiation.
The concept of generative agents was initially proposed in the landmark paper "Generative Agents: Interactive Simulation of Human Behavior." Researchers demonstrated how simulated characters driven by large language models could exhibit increasingly human-like behavior, influenced by memory, reflection, and planning. While this idea initially garnered attention for its potential to build realistic social simulations, its significance extends beyond academic curiosity. One of its most promising commercial applications is market research.
Here’s a concrete example: Before launching a new skincare product in France, a beauty company could simulate 10,000 agents modeled after Gen Z and millennial French skincare consumers. Each agent would be fed data from customer reviews, CRM histories, social media listening insights (such as trends about skincare routines on TikTok), and past purchase behaviors. These agents can interact with each other, watch simulated influencer content, shop in virtual store shelves, and post product opinions on AI-generated social media, evolving over time as they absorb new information and reflect on past experiences.
What makes these simulations possible is not just off-the-shelf large language models but an increasingly complex technology stack. Agents are now anchored in persistent memory architectures, often based on rich qualitative data such as interviews or behavioral histories, allowing them to evolve over time through accumulated experiences and contextual feedback. Contextual prompts provide behavioral history, environmental cues, and prior decisions, creating more nuanced and realistic responses.
Behind the scenes, methods like retrieval-augmented generation (RAG) and agent chains support complex multi-step decision-making, producing simulations that reflect real-world customer journeys. Fine-tuned multimodal models trained on specific tasks—across text, visual, and interactive training—push agent behavior beyond text alone.
Early platforms are already leveraging these methods. AI-driven simulation startups like Simile and Aaru (which has just announced a partnership with Accenture) hint at an upcoming trend: dynamic, always-on crowds that behave like real customers, ready to be queried, observed, and experimented with.
Agent simulation not only accelerates workflows that once took weeks but fundamentally reinvents how research and decisions are made. It also overcomes many traditional research limitations by creating a research tool that can exist within workflows. This leap is not just about efficiency but also about fidelity.
If history is any guide, the companies that will dominate in this wave of AI will not only possess the best technology but also master distribution and adoption. For instance, Qualtrics and Medallia achieved early success by prioritizing adoption, familiarity, and loyalty, deeply embedding themselves in universities and key industries.
Accuracy is clearly important, especially when teams compare AI tools with traditional, human-led research. But in this category, there are no established benchmarks or evaluation frameworks, making it difficult to objectively assess the quality of a given model. Companies experimenting with agent simulation technology often have to define their own metrics.
The key is that success doesn't mean achieving 100% accuracy but reaching a threshold that is good enough for your use case. Many chief marketing officers we spoke with expressed satisfaction with outputs that achieve at least 70% accuracy, particularly because data is cheaper, faster, and updated in real time. In the absence of standardized expectations, this creates a window for startups to move quickly, validate through actual usage, and embed early in workflows.
That said, startups must continue to refine their products: benchmarks will emerge, and the more you charge, the higher client expectations will become. At this stage, the risk lies more in imperfect outputs than over-engineering for theoretical accuracy. Startups prioritizing speed, integration, and distribution can define emerging standards. Those that delay for perfect fidelity may find themselves stuck in endless pilot projects while others move into production.
AI-native research companies are better positioned than traditional ones to redefine market research expectations. While traditional market research firms may possess deep panel data, their business models and workflows are not built for automation. In contrast, AI-native participants have developed dedicated tools for AI-driven research and are structurally incentivized to push frontiers rather than protect the past. They are ready to own both the data layer and the simulation layer.
The widely cited paper "1000 Generative Agent Simulations" illustrates this convergence: its co-authors relied on AI-generated real interviews to seed agent profiles—similar to pipelines AI-native companies have already scaled up. To make an impact, insights must extend beyond UX and marketing teams to include product, strategy, and operations. The challenge lies in providing sufficient service support without recreating the heavy overhead of traditional institutions.
The era of lagging research is coming to an end. AI-driven market research is changing how we understand customers, whether through simulation, analysis, or insight generation. Early adopters of AI-driven research tools will gain faster insights, make better decisions, and unlock new competitive advantages. With product launches becoming faster and easier, the real edge lies in knowing what to build.