How Agentic AI Is Reshaping Market Research

AI has been reshaping the market research industry for years. From data processing and pattern detection to predictive modelling and automated reporting, AI has already helped researchers work more efficiently. But the next leap, and arguably the most transformative one, is now underway. It involves the shift from passive tools to agentic AI: a model in which autonomous, specialised AI agents take on complex research tasks in parallel, in real time, and with purpose.

At Nextatlas, we embraced this shift early. In 2023, we launched Generate, an AI-powered chatbot designed to help researchers generate trend reports, answer open-ended questions, and explore cultural data. But what began as a successful experiment quickly revealed its limitations. Users wanted more than a single, generalist assistant; they needed a system capable of mirroring the diversity and complexity of real-world insight work.

That realisation led us to build Generate Suite: a constellation of specialised AI agents, each with a focused role in the market research workflow.

What Is Agentic AI?

Unlike a traditional chatbot, which passively responds to queries, agentic AI refers to systems that can operate more autonomously. Agentic AI refers to systems made up of independent, goal-oriented agents that can reason, make decisions, and take initiative to accomplish tasks.

Agentic AI agents can:

• Continuously scan cultural, behavioural, or innovation data
• Generate and test hypotheses
• Build synthetic consumer personas
• Simulate how different audiences might respond to new products or messages
• Collaborate in the background, surfacing insights across domains in parallel

In the context of market research, this changes everything. These agents differ from traditional AI assistants (like chatbots or virtual assistants you might be familiar with) in several ways:

• They are task-specific, designed to perform a distinct function with deep contextual awareness.
• They operate semi-autonomously, meaning they can initiate actions, run background processes, or collaborate with other agents.
• They can be combined, enabling richer, multidimensional analysis across data types and business needs.

This architecture is particularly suited to market research, which is inherently multi-disciplinary. Agentic AI aligns well with the way human insight teams already work: strategists, analysts, designers, and planners all bring different perspectives, and the same is now true for our AI agents.

Why We Rebuilt Generate

The original version of Generate allowed users to interact with our trend forecasting engine using natural language. It simplified access to insights, automated basic synthesis, and enabled agile exploration across industries. Yet it soon became clear that no single chatbot, however well-trained, could fully support the varied demands of brand strategists, researchers, foresight teams, and innovation leads.

Users weren’t just asking for downloadable trend reports as we had foreseen. They were posing complex, context-rich questions that required specific expertise, deeper reasoning, and structured exploration. For example, questions like, “What do eco-conscious Gen Z consumers in Europe value most in sustainable fashion brands?”, “Which ingredient trends are emerging in premium wellness beverages across the US and APAC?”, or “How are luxury travel behaviours shifting post-pandemic, particularly among millennial professionals?”

These are not one-size-fits-all questions. They require distinct research strategies, different sources, and targeted methods of synthesis. In short, they need specialisation, something a single chatbot cannot provide at scale.

This drove our decision to evolve Generate into a suite of domain-trained AI agents, each built for a dedicated purpose and able to collaborate on more complex briefs.

Inside Generate Suite

Generate Suite is composed of several specialised agents, each trained on real-time cultural data and powered by Nextatlas’ proprietary trend data. These include:

• The Persona Agent: Builds synthetic consumer personas derived from over 300,000 early adopters and digital pioneers. These AI personas can be queried, engaged in dialogue, and grouped into synthetic focus groups for scenario testing and value proposition exploration.
• The Innovation Agent: Scans and synthesises signals from patents, startup ecosystems, scientific literature, and product launches to surface whitespace opportunities and innovation trends.
• The Sentiment Agent: Interprets emerging sentiment patterns by analysing linguistic and emotional shifts in online discourse, enabling foresight into consumer mood and motivation.
• The Subculture Scout: Finds emerging subcultures and digital tribes, gaining deep insights into their behaviors, values, and trends. Authentically position your brand at the heart of the next big community.
• The Sustainability Agent: Tracks consumer attitudes, governmental regulation to make smarter decisions in your sustainability strategy, meet evolving consumer expectations, and seamlessly integrate sustainable practices into your strategy.

Each agent is designed not just to respond, but to investigate. Their responses are grounded in evidence, not generative speculation, and can be traced back to data sources for validation.

The Strategic Value of Agentic AI

The strategic value of agentic AI lies in its ability to transform how insights are produced, explored, and applied. Unlike traditional research tools, which often follow a linear, one-question-one-answer format, agentic AI enables a more dynamic and iterative approach. Researchers can explore multiple hypotheses in parallel, combine the outputs of different agents for richer scenario modelling, and adapt their lines of inquiry based on emerging findings. This flexibility allows teams to build research workflows around the needs of a specific brief, rather than conforming to the limitations of a static dashboard.

At its core, agentic AI redefines the role of the human researcher, shifting from manual data processing to the orchestration of intelligent systems. By delegating time-consuming and computational tasks to purpose-built agents, researchers can focus their efforts on higher-level interpretation, contextual thinking, and strategic decision-making. The result is faster, deeper, and more adaptable insight generation, offering tangible benefits to research, brand, and innovation teams operating in increasingly complex and fast-paced environments.

What’s Next for Generate Suite

The current suite already represents a major leap forward in AI-powered insight. But we see this as just the beginning.

Our roadmap includes:

• Increased cross-agent collaboration
• Expanded domain-specific models for industries such as automotive, finance, and CPG
• Deeper integration with client ecosystems and tools
• New UX features to enhance transparency, traceability, and explainability of each agent’s reasoning

Ultimately, our ambition is to enable continuous, conversational research — an always-on system where agents work in the background, surfacing patterns, testing assumptions, and offering perspectives proactively.

The Future Is a Dialogue, not a Dashboard

As insight professionals navigate faster consumer cycles, fragmented data environments, and constant cultural flux, having AI agents that are intelligent, explainable, and proactive will become the norm. At Nextatlas, we foresaw this shift and we built Generate Suite, not as an experiment, but the next step in collaborative insight generation, where humans ask smarter questions, and AI agents help uncover answers that matter.

Agentic AI is both a technical evolution and also a structural one for the market research industry. It reflects a new philosophy of how intelligence, both artificial and human, can work together. For those of us in the business of understanding change, it offers the ability to keep pace with culture as it happens, to ask better questions, and to develop sharper foresight.

Trend lines, data, and information described in this article emerge from the ongoing analysis performed by Nextatlas on its global observation pool made of innovators, early adopters, industry insiders expressing their views on Twitter, Instagram, and Reddit.

To learn more about our AI, discover Nextatlas Methodology here