How are machine learning (ML) and artificial intelligence (AI) specifically changing market research methodologies?

Machine learning (ML) and artificial intelligence (AI) are transforming market research methodologies by enabling the processing of open-ended data, proactive community management, survey management (detecting fraud), employing chatbots and virtual moderators, analyzing text and passive data, automating reporting, undertaking secondary research, leveraging the Internet of Things (IoT), and processing voice-activated device data. These applications allow for more efficient and comprehensive data analysis, leading to deeper insights into consumer behavior and preferences.

What's the key difference between automation and AI in the context of market research, and why is this distinction important?

The key difference lies in their functionality: automation performs predefined tasks based on rules, whereas AI learns, adapts, and makes autonomous decisions. Automation uses sophisticated software, while AI has the ability to learn and apply that learning. Understanding this distinction is crucial because it highlights AI's added value in market research. AI can uncover insights that automation alone cannot, but it's important not to consider them mutually exclusive, because it is the collaboration of the two that makes the difference.

Beyond just automating tasks, how can artificial intelligence (AI) enhance the creativity and strategic thinking of market researchers?

Artificial intelligence (AI) enhances creativity within research by automating routine tasks, which frees up researchers to focus on higher-level strategic thinking. By reducing the time spent on manual processes, researchers can devote more attention to exploring new research questions, developing innovative approaches, and providing deeper insights to clients. This shift enables researchers to leverage AI's efficiency while applying their unique human skills to the more complex aspects of market research.

What potential advancements does Ben Page of Ipsos envision for AI in market research, and what limitations does he acknowledge?

Ben Page, CEO of Ipsos, UK, envisions artificial intelligence (AI) as a catalyst for revolutionizing market research through real-time analysis of social media data, rapid analysis of video, audio, and text, intelligent question creation, and automation of routine tasks. However, he acknowledges that AI in market research is still in its early stages, with much potential yet to be realized. This suggests that while AI offers promising advancements, it is not a fully mature technology and requires further development and refinement.

AI Brain in Market Research

AI in Market Research: Hype or Revolution? Navigating the Future of Insights

Q: What ethical considerations and practical challenges should market researchers keep in mind when integrating AI into their work?

Market researchers should consider the costs, benefits, and ethical implications when integrating AI. It is important to balance the advantages of AI solutions with the human element, recognizing that traditional research methods remain vital for understanding the 'why' behind consumer behavior. Ensuring AI is used responsibly and ethically, and collaborating with researchers, academics, and practitioners to develop trusted AI tools, are crucial for upholding the core values of the market research industry.

Samir D’Costa in Tech & Innovation December 2025 • 5 min read.

"Explore how machine learning and artificial intelligence are reshaping market research, uncovering both opportunities and challenges for businesses."

Machine learning (ML) and artificial intelligence (AI) are rapidly transforming the landscape of market research. The latest GRIT survey highlights automation as a key topic, signaling a significant shift in how research is conducted. This article delves into the opportunities and challenges presented by ML/AI, exploring whether these technologies will revolutionize the sector, serve as a mere distraction, or lead to meaningful improvements in methodologies, skills, and client services.

Industry leaders hold varied perspectives on the integration of AI in market research. Jane Frost, Chief Executive Officer at the Market Research Society (MRS), emphasizes that AI is already present and offers substantial opportunities. However, she cautions against allowing it to become an overused term with unfulfilled promises, akin to the fate of big data. The real challenge lies in using AI intelligently and strategically.

Ben Page, CEO of Ipsos, UK, envisions AI as a potential catalyst for revolutionizing market research in several ways. These include real-time analysis of social media data, rapid analysis of video, audio, and text, intelligent question creation, and automation of routine tasks. Despite these promising advancements, Page notes that AI in market research is still in its early stages, with much potential yet to be realized.

AI vs. Automation: Understanding the Spectrum of Technologies

Distinguishing between automation and AI is crucial. Automation typically involves machines performing tasks based on predefined rules, such as generating visual outputs from data. AI, on the other hand, enables machines to learn, adapt, and make decisions autonomously. While automation relies on sophisticated software, AI's distinguishing feature is its capacity to learn and apply that learning, offering significant added value to market research.

Forbes describes AI as the capability of a machine to imitate intelligent human behavior. The focus is less on basic automation and more on exploring the blurred boundaries between automation, ML, and AI within the research process. Understanding this spectrum is essential for harnessing the full potential of these technologies.

Processing Open-Ended Data: AI can efficiently analyze large volumes of unstructured text data from surveys and social media.
Proactive Community Management: AI-powered tools can manage and engage with online communities, identifying key trends and sentiments.
Managing Surveys: AI can detect fraudulent responses and ensure data quality in surveys.
Chatbots and Virtual Moderators: AI chatbots can conduct interviews and moderate online discussions, providing real-time insights.
Analyzing Text and Passive Data: AI can analyze text and passive data from social media to uncover consumer behaviors and preferences.
Automated Reporting: AI can automate the creation of reports, saving time and resources.
Undertaking Secondary Research: AI can quickly gather and synthesize information from various sources.
Internet of Things (IoT): AI can analyze data from IoT devices to understand consumer behavior in real-world contexts.
Voice-Activated Devices: AI can process voice data to understand consumer needs and preferences.

AI can enhance creativity within research by enabling researchers to focus on higher-level strategic thinking. By automating routine tasks, AI frees up researchers to explore new questions and develop innovative approaches.

Navigating the Ethical and Practical Challenges

Integrating AI in market research requires careful consideration of costs, benefits, and ethical implications. While AI solutions can offer significant advantages, it is essential to balance these with the human element. Traditional research methods remain vital for understanding the “why” behind consumer behavior, and human-assisted AI or collaborative intelligence can help bridge this gap. As the market research sector embraces automation and AI, it must ensure that these technologies are used responsibly and ethically. Researchers, academics, and practitioners must collaborate to develop AI tools and build a body of trusted knowledge. By addressing the challenges and opportunities, the market research industry can harness the full potential of AI while upholding its core values.