The leading applications of AI include desk research, automated insight generation, brainstorming, and automated transcription. Currently, at least half of organizations utilize these applications, and this number is expected to rise to at least three-quarters within the next two years.
When comparing current and future usage, the most significant growth is seen in AI applications for quality checks, sentiment analysis, analytical processes, and competitor intelligence.
AI supports desk research by enabling document and paper scanning as well as analysis. Many AI applications are designed to automate insights, including the analysis of open-ended text to extract meaning beyond simple code counts.
AI-powered tools assist with note-taking, coding responses, and back-end tasks using platforms such as Coloop, Decipher, ChatGPT, and Perplexity.
In contrast, quantitative analysis still relies heavily on human analysts for methods such as K-means clustering, latent class analysis, and max-diff. AI is not yet fully trusted to make these strategic decisions.
AI is also revolutionizing quality checks. Traditionally, online surveys were assessed using standard metrics such as length of interview (LOI), trap questions, and straight-lining detection. However, AI-powered machine learning can now evaluate respondent engagement levels and assign scores accordingly determining whether responses should be used or even if a respondent should remain a panel member.
Additionally, AI enhances the survey experience for respondents through AI-driven probing tools. Large language models (LLMs) improve surveys by mimicking human-like conversations, ensuring depth, focusing on key study objectives, and enhancing engagement and response quality.
AI is also applied to sentiment analysis, real-time market monitoring, analytics, and modeling. Predictive models can estimate outcomes—such as the success of an advertisement—without requiring traditional surveys. AI tools further support market research by analyzing social media for valuable insights.
ChatGPT has become the AI industry leader by a considerable margin, but firms are using a range of other applications, with some brands having achieved a market lead in AI including Displayr, Qualtrics, CoLoop, Perplexity, Anthropic / Claude, and Indeemo that have already been used by 10% or more of research agencies.
An astonishing n=44 different AI branded solutions have either already been used or expect to be adopted in the future!
There is an appetite for most firms to develop their own in-house AI applications. The preference is to develop these in-house (57%) rather than with an external vendor (35%), showing that confidentiality and ownership of the IP are important to research firms. Smaller firms like to use external vendors for many of their supply functions (including AI), so that they can remain as lean as possible and focus only on the advisory business.
Use of AI within client organizations
Newer brands are often more willing and able to adopt AI for consumer insights. Compared to older brands with established consumer insight practices, they tend to prefer agile approaches, building in-house teams and capabilities to experiment with AI while avoiding the slower processes of traditional market research agencies.
Larger corporations may adopt AI more cautiously due to concerns about ethics, accountability, and responsibility in decision-making. The public sector tends to be the most resistant to AI adoption.
More broadly, AI is viewed as a tool to augment internal capabilities and enhance efficiency. However, AI-generated outputs are carefully scrutinized to assess risks, particularly in sensitive industries such as finance. Vendors using AI must undergo reassessment processes to ensure compliance and maintain trust.
Clients perceive higher risks in AI, making companies more cautious in adoption—especially in highly regulated industries such as finance and healthcare, where the consequences of data breaches are severe. As a result, organizations may choose to implement internal versions of AI tools like ChatGPT. AI integration requires structured policies to ensure ethical usage and regulatory compliance.
Clients are often more proactive in using AI for desk research and competitor intelligence, as these activities are typically conducted in-house.
Media companies, which have vast amounts of internal customer data, are leveraging AI to gain deeper insights. AI is used to analyze viewing habits and genre preferences, helping to understand audience behavior and personalize recommendations. Some of this is approached through a “test-and-learn” methodology, as audience reactions are not always predictable. Challenges arise when applying these insights across different countries and cultures.
Other challenges include understanding light users or mixed-profile users due to insufficient or noisy data. For example, shared accounts can lead to skewed insights and poor recommendations for light users.
AI is often used alongside primary research to enhance the understanding of customer journeys and areas of dissatisfaction.
Marketing teams are leveraging AI in the innovation process, media targeting, real-time modification of creatives and messaging, and targeting specific audience segments.
