AI For Market Research in 2026: Evolution, Process, and Tools

Artificial intelligence is changing how market research works. What used to be a slow, manual process is now fast, adaptive, and continuous. Today, companies can analyze behavior almost in real time, interpret unstructured feedback at scale, and generate strategic insights whenever they need them.

How is AI used for market research?

AI has evolved from basic automation to a layered ecosystem combining machine learning (ML), generative AI (GenAI), and agentic AI. As the technology has advanced, researchers’ roles have shifted from managing data to making strategic decisions.

Early AI applications focused on structured data tasks such as cleaning survey responses or modeling churn with ML. This improved accuracy and scale but remained mostly reactive, limited to predefined inputs and outcomes.

Generative AI took things a step further. It can interpret qualitative data such as open-ended survey responses while understanding context and following complex instructions. It can also generate hypotheses and support creative thinking when interpreting results.
For example, a GenAI chatbot could review 1,000 survey responses, analyze sentiment, and rank how strongly each response supports a particular opinion.

Agentic AI pushes this even further. It acts autonomously across different systems and continuously monitors data streams from CRM tools, social media, or third-party reports. It can interpret signals and trigger actions on its own. For instance, it might detect a drop in customer sentiment in a region and automatically generate a report with updated product or pricing recommendations.

The evolution from rule-based automation to autonomous, insight-generating AI makes AI not just a research tool but an active partner in shaping market direction.

Key benefits of AI in market research

AI, particularly generative and agentic systems, delivers measurable advantages across the market research lifecycle. These benefits span efficiency, depth of insight, and strategic responsiveness.

1. Faster turnaround and reduced costs: AI automates time-intensive tasks like data cleaning, transcription, coding of open-ended responses, and even drafting reports, cutting time and costs by orders of magnitude.
2. Deeper understanding of customer sentiment: GenAI models can process large volumes of qualitative data to surface nuanced emotions, emerging concerns, or shifts in perception, which traditional tools often overlook.
3. More adaptive and ongoing research: Agentic AI supports always-on research by continuously analyzing new data as it arrives, making it possible to track sentiment or performance in real time without re-running full studies.
4. Scalable personalization and segmentation: AI identifies granular customer segments and tracks how they evolve, enabling tailored marketing strategies that respond to live behavior rather than static personas.
5. Enhanced competitive awareness: Automated intelligence gathering from public sources, including social media and competitor sites, helps organizations detect threats or opportunities faster than manual methods.
6. Improved strategic decision-making: By surfacing patterns and simulating outcomes, AI helps teams model potential decisions, such as market entry timing or pricing changes, before committing resources.
7. Greater insight accessibility across teams: AI tools can translate raw data into natural language summaries or visual dashboards, making insights understandable and actionable for non-technical stakeholders.

The evolution of AI in market research

AI in market research has evolved through three major phases: traditional machine learning, generative AI, and agentic AI. Each phase has expanded how researchers collect, interpret, and act on data.

1. Traditional machine learning in market research

Traditional machine learning (ML) has been part of market research for more than two decades. It focuses on identifying statistical patterns in both structured and unstructured data, enabling researchers to process large datasets far more efficiently than manual or rules-based methods.

Common applications include:

• Quantitative survey analysis: ML models can uncover hidden relationships between demographic variables and responses, revealing subgroups with distinct preferences. Clustering algorithms segment consumers by behavior, purchase history, or sentiment, while classification models predict how likely a customer is to respond to an offer or churn.
• Sentiment analysis: Supervised ML models trained on labeled datasets categorize feedback from reviews, call transcripts, or social media as positive, negative, or neutral. Over time, they can detect subtle tone changes that signal shifts in consumer attitudes.
• Forecasting: Time-series models trained on historical sales and demand data help predict product performance, pricing sensitivity, and seasonal trends.

Traditional ML remains widely used across organizations of all sizes. It is affordable, proven, and reliable for focused use cases where statistical confidence and repeatability matter most.

2. The GenAI revolution

Generative AI is rapidly reshaping market research by making it more creative, agile, and predictive. It is being rapidly adopted; according to a recent study, 62% of market researchers now use GenAI tools, up 23% from the previous year. Here are a few ways generative AI is revolutionizing the field:

• Supercharging workflows and productivity: GenAI speeds up every stage of the research process, from planning to execution. It helps draft project plans, simulate customer personas to help with survey design, craft questionnaires, and assist with in-depth reviews of existing research.
• Generating synthetic data and novel insight types: GenAI enables the creation of synthetic datasets that mimic real consumer behavior, which can be used to simulate responses, test pain points, or model competitor reactions. Additionally, strategies like digital twins allow researchers to unlock new insights with complex simulations of customer behavior.
• Democratizing insights and competitive awareness: GenAI helps surface insights that traditional methods might miss, especially in competitive intelligence and when trying to understand rapidly changing markets. GenAI, because it is accessible to multiple stakeholders in the organization, is helping democratize insights and deepen understanding of customer journeys.

3. The new frontier: Agentic AI in market research

Agentic AI represents a major shift in how organizations extract value from market research. Unlike traditional or even GenAI tools, which still require manual queries and human-led synthesis, agentic AI systems autonomously connect, interpret, and generate strategic insights from both structured and unstructured research sources.

Here are a few ways agentic AI supports market research functions:

• Active reasoning: Agentic AI actively reasons through data to generate actionable intelligence. It can identify emerging market trends, highlight competitive threats, and simulate strategic scenarios. For example, it may detect shifts in consumer sentiment across product lines or correlate sales anomalies with changes in competitor pricing, providing proactive recommendations.
• Unifying fragmented data: In most organizations, valuable research is siloed across teams, vendors, or platforms, making it difficult to build a comprehensive market picture. Agentic AI integrates these disparate sources into a single intelligence layer. By continuously monitoring and interpreting data, including reports, customer feedback, and competitor briefings, it surfaces patterns and relationships that might otherwise be lost.
• Increases depth of analysis: Agentic AI reduces the time analysts spend hunting for data, while enhancing the depth and reliability of their insights. It augments human analysts by handling complex data interpretation at scale.

Core functions and capabilities of AI in modern market research

Automated data collection and aggregation

AI simplifies data collection by pulling information from multiple sources such as surveys, CRM systems, social media, review sites, and third-party databases. It automates tasks like scraping competitor websites, capturing customer feedback, and combining structured and unstructured data into one unified dataset. This reduces manual work and ensures a steady stream of fresh, relevant information for analysis.

Qualitative and quantitative analysis

AI can interpret both structured and unstructured data. It helps analyze open-ended survey responses, interviews, and social media posts to identify themes, emotions, and intent. For quantitative data, machine learning models detect patterns, segment audiences, and uncover correlations between variables. Generative AI adds another layer by performing context-aware analysis based on natural language instructions, offering richer and more flexible insights.

Automated competitive and market intelligence

AI tools continuously monitor the digital landscape, scanning news outlets, press releases, social media, and review platforms. They detect changes in competitor activity, pricing, messaging, or consumer sentiment, providing early signals of market shifts. This enables organizations to adjust strategies proactively instead of relying on manual monitoring.

Predictive analytics and trend forecasting

Machine learning models trained on historical data can forecast future outcomes such as product demand, customer churn, or pricing impact. Generative and agentic AI can also simulate different market scenarios and generate forward-looking insights as conditions evolve. This helps businesses anticipate change and plan strategically rather than react to it.

Advanced segmentation and personalization

AI enables highly detailed segmentation based on real-time behavior, preferences, and engagement. Clustering and classification models uncover new or evolving customer groups that might not appear in traditional analyses. These insights support more targeted marketing, customized product development, and stronger customer experiences.

Best practices for AI market research

1. Define clear research objectives

Start by identifying what you want your research to achieve, such as finding new growth opportunities, understanding customer challenges, or testing pricing strategies. Having clear objectives helps you choose the right data sources, methods, and AI tools to get meaningful answers.

2. Select the right AI market research tool

Not all AI tools are built for the same purpose. Traditional machine learning models are ideal for predictive analytics and quantitative insights, while generative AI can assist with text analysis, survey creation, and social listening. When choosing a platform, consider how easily it integrates with your current systems, how user-friendly it is, and how transparent its outputs are.

3. Automate data collection and processing

AI can take the heavy lifting out of data gathering. Set up automated pipelines to pull information from surveys, CRM systems, online reviews, social media, and competitor reports. Include preprocessing steps like removing duplicates, tagging sentiment, and extracting entities to turn raw data into a clean, structured dataset that is ready for analysis.

4. Use AI to generate insights

Machine learning and NLP can reveal patterns and insights that might otherwise go unnoticed, such as emerging market trends or subtle shifts in customer sentiment. AI-powered visualization tools can then help you communicate those insights clearly to decision-makers.

5. Validate with human expertise

AI can process huge amounts of data, but it still needs human oversight. Before acting on AI-driven insights, have experts review them to ensure they make sense within the business context. They can identify any overfitting, irrelevant correlations, or misinterpretations that AI might produce. This step builds confidence in your results and ensures decisions are both data-informed and grounded in experience.

6. Translate insights into strategy

Finally, connect your insights to business strategy. For example, use customer segmentation data to refine your messaging, or leverage trend forecasts to explore new markets. AI can also help you model different strategic outcomes so teams across marketing, product, and pricing can act on the findings effectively.

Notable AI-based market research tools

Similarweb

As market research evolves toward automation and real-time intelligence, Similarweb stands out for embedding AI directly into the data discovery process. From context-rich data delivery to autonomous trend detection, Similarweb’s AI-powered capabilities help businesses move faster from questions to actionable insights. Two standout innovations illustrate this shift:

Similarweb MCP (Model Context Protocol)

Similarweb’s MCP server is designed to make AI systems truly intelligent by providing context-rich access to trusted digital market data. Unlike traditional APIs that only deliver raw data, MCP helps AI agents understand what data is available and how to use it effectively.

Through MCP, companies can build content-focused AI agents that:

• Analyze keyword gaps and competitor performance
• Identify high-performing pages and new customer insights
• Transform search intelligence into actionable strategies within minutes

By pairing Similarweb’s MCP with general-purpose AI platforms like Claude, Cursor, and more, teams can move from manual data collection to autonomous, insight-driven workflows for SEO, content, and market research.

MCP’s natural language integration allows users to ask questions in plain English and receive structured, contextualized insights, making AI agents more adaptive, trustworthy, and productive across business functions.

Similarweb AI Trend Analyzer Agent

The AI Trend Analyzer Agent is Similarweb’s always-on digital researcher, analyzing real-time search data to reveal not only what’s trending, but why.

By combining Similarweb’s keyword and search demand data with real-time web signals, the AI Trend Analyzer detects sudden demand spikes, clusters related keywords, and correlates them with relevant events, campaigns, or news stories.

This agent helps Insights, Marketing, and SEO teams:

• Detect emerging trends before competitors do
• Understand what’s driving changes in consumer behavior
• Connect spikes in search demand to real-world causes
• Distinguish between seasonal shifts and genuine market movements

See how it works:

SurveyMonkey

SurveyMonkey is an AI-powered survey platform that helps organizations build, distribute, and analyze surveys with greater speed and precision. Trained on billions of responses and over 25 years of proprietary data, its AI is purpose-built for survey design and analysis, making it a strong tool for modern market research. The platform simplifies the entire survey lifecycle, from creation to insight generation, using intelligent automation and real-time guidance.

Key features include:

• AI-powered survey creation: Generate surveys from a simple prompt or import existing content to transform it into well-structured, professional surveys.
  
• Answer recommendations and type prediction: Receive auto-suggested answer options and optimal question types based on your input, reducing setup time and error.
• Survey quality checks: Get real-time suggestions to improve structure, question clarity, and formatting for better data quality.
• AI analysis suite: Instantly analyze open-ended responses, identify sentiments, and surface key themes without manual tagging.
• Chat-based insight generation: Ask natural-language questions about your data and receive charts, summaries, and tables in seconds.

Quantilope

Quantilope is a consumer intelligence platform that uses AI to make advanced market research faster, smarter, and more accessible. For nearly a decade, it has helped organizations bring the consumer voice into strategic decision-making by combining automation with sophisticated research methods.

At the center of Quantilope’s AI capabilities is Quinn, an AI co-pilot that’s fully integrated into the platform. Quinn supports researchers at every stage of a project, from survey design to data analysis and reporting. You can chat with Quinn just like you would with a teammate and ask for help building surveys, analyzing results, or creating charts and reports.

Key AI features include:

• AI research co-pilot: Quinn guides users through each step of the research process and helps answer questions about the platform or key findings.
• Survey creation made simple: Generate survey questions, attributes, and logic instantly, even for complex methods like Implicit Association Tests or MaxDiff.
• Instant data visualization: Ask questions such as “What’s the impact of product attributes on purchase likelihood?” and Quinn will analyze your data and create the charts you need.
  
• Automated reporting: Quinn can pull together the most relevant charts and insights into a report you can edit and share right away.
• Support for advanced methods: Combine AI assistance with trusted techniques like Conjoint, TURF, and Implicit Association Tests to uncover meaningful, reliable insights.

By embedding Quinn directly into its platform, Quantilope turns the entire research process into a simple conversation, helping teams focus less on setup and more on understanding what really matters.

Glimpse

Glimpse is a trends discovery platform that uses search data to identify emerging consumer behaviors before they hit the mainstream. By analyzing hundreds of millions of online behavior signals, it surfaces high-potential trends with strong growth potential.

Key features include:

• Early trend detection: Spot trends months or years before they go mainstream to gain a first-mover advantage.
  
• Search-driven insights: Leverage search data from billions of global users for accurate, unbiased signals.
• People also search data: Understand consumer motivations and related interests for a deeper market context.
• Forecasting capabilities: Predict industry or trend growth trajectories for the next 12 months with high precision.
  
• Topic mapping: Visualize related topics and industry landscapes for better strategic planning.

The future of market research is AI-driven

AI is no longer just a trend in market research: It’s a fundamental shift in how insights are discovered, analyzed, and acted upon. From speeding up survey creation to detecting early market signals, AI helps researchers move faster and go deeper, uncovering insights that were once hidden in noise or overlooked due to time constraints.

As AI capabilities continue to evolve, tools like Similarweb are expanding what’s possible, from analyzing billions of data points to enabling agentic AI workflows that provide tangible competitive insights. These technologies are making market research more continuous, connected, and collaborative across teams.

But even with advanced automation, the role of human judgment remains critical. The most effective use of AI in market research comes from combining its speed and scale with human intuition and business context, turning raw data into a meaningful, actionable strategy.

FAQs

How is AI changing market research?

AI is making market research faster, more accurate, and more scalable. It automates tasks like data collection, trend analysis, and sentiment detection, allowing teams to generate insights in hours instead of weeks.

What are the benefits of using AI in market research?

AI enhances market research by speeding up analysis, reducing costs, improving prediction accuracy, and uncovering deeper insights from large and complex datasets. It helps organizations act on data more confidently and quickly.

What types of AI are used in market research?

Market researchers use traditional machine learning for tasks like segmentation and forecasting, while generative AI helps with content creation, synthetic data generation, and rapid analysis. Agentic AI is emerging for real-time decision support and strategic intelligence.

Can AI replace human researchers?

No. AI supports but doesn’t replace human expertise. While AI handles data-heavy tasks, human researchers are still essential for interpreting results, making strategic decisions, and ensuring insights are relevant and trustworthy.

What are some examples of AI tools used in market research?

Popular tools include SimilarWeb for competitive and digital behavior analysis, SurveyMonkey for AI-enhanced surveys, Quantilope for automated research workflows, and Glimpse for early trend detection. General AI models like ChatGPT are also used for synthesizing insights and drafting content.