The Research Industry Leads In AI Adoption, But Where Are The Real Opportunities?

When ChatGPT was launched in November 2022, it reached 100 million users within weeks—marking the fastest adoption of any product in history.

However, in November 2024, The Economist published an article on AI that cast doubt on its broader economic impact. The report highlighted that few AI startups were profitable, with growing challenges related to model and data constraints. A key concern is that investor enthusiasm for AI could wane just as even greater financial backing is needed to develop more advanced systems, such as "agentic" AI.

A survey cited in this article found that only 5% of U.S. businesses incorporate AI into their products and services. However, informal AI usage is significantly higher, with one-third of U.S. employees reporting they use AI for work at least once a week. Adoption is especially prevalent in certain job roles, such as software engineering and human resources.

Market Research: A Leader in AI Integration

The market research industry stands out as one of the fastest adopters of AI, as its applications and benefits are easier to realize. Research agencies are integrating AI into their solutions and exploring innovative ways to apply it, from designing surveys to generating stimulus materials. According to research from Asia Research Media, most market research firms have either established dedicated AI teams or have employees leveraging AI in some capacity, whether formally or informally.

Despite its advantages, AI adoption in market research presents some unique challenges compared to other industries. The field requires deep contextual understanding, incorporating human and cultural factors that AI still struggles to grasp—at least for now.

Key AI Applications in Consumer Insights

AI is widely adopted in the consumer insight industry, with its most common applications including desk research, automated insight generation, brainstorming, and automated transcription. At least half of organizations are already utilizing these tools, and adoption is expected to reach at least 75% within the next two years.

Looking ahead, the fastest-growing AI applications are expected to be in quality checks, sentiment analysis, analytical tools, and competitor intelligence. Many AI-driven solutions are focused on automating insight generation, such as analysing open-ended text for meaning beyond simple code counts.

AI is also revolutionizing quality control in surveys. Traditionally, online surveys were assessed using basic metrics like length of interview (LOI), trap questions, and straight-lining. However, machine learning tools now evaluate respondent engagement and assign scores to determine the reliability of their responses—or even their suitability to remain on a research panel.

Additionally, AI is enhancing the survey experience for participants through probing tools. AI-driven models, such as large language models (LLMs), enable more natural, human-like interactions within surveys, ensuring deeper insights while keeping respondents engaged and focused on key study objectives.

Beyond surveys, AI is being applied across sentiment analysis, real-time market monitoring, analytics, and modelling. Predictive models can forecast outcomes—such as the success of an ad campaign—without requiring traditional surveys. At the same time, AI-powered tools are increasingly being used to analyse social media for valuable market insights.

The Corporate Divide: AI Adoption in Brands and Public Sector

Newer brands tend to be more open and agile in adopting AI for consumer insights. Unlike older brands with well-established research practices, they favour in-house AI experimentation over traditional market research agency processes, which can be slower and less flexible.

Larger corporations, however, often take a more cautious approach to AI adoption due to concerns around ethics, accountability, and responsible decision-making. The public sector, in particular, is among the most resistant to AI implementation.

More broadly, AI is seen as a tool to enhance internal capabilities and improve efficiency. However, AI-generated outputs are closely scrutinized, especially in sensitive industries like finance and medicine, where risk assessment is crucial. Vendors using AI must undergo rigorous reassessment to ensure compliance and maintain trust.

Clients generally perceive more risks in AI, leading to cautious adoption, particularly in highly regulated sectors like finance and healthcare, where data breaches carry significant consequences. As a result, some companies opt to bring AI in-house, such as by deploying internal versions of ChatGPT. Structured policies are essential to ensure ethical AI use and regulatory compliance.

AI adoption is more common in areas like desk research and competitor intelligence, as these functions are often handled internally. Media companies, in particular, leverage AI to gain deeper insights from their vast customer data. AI helps analyse viewing habits and genre preferences to better understand audience behaviour and personalize recommendations. However, since this approach is not an exact science, some companies take a "test-and-learn" approach to gauge audience reactions. Challenges arise when applying AI-driven insights across different countries and cultures.

Additional hurdles exist in understanding light users or mixed-profile users due to noisy or insufficient data. For example, shared accounts can distort insights and lead to poor recommendations for occasional users.

AI is frequently used alongside primary research to gain a more comprehensive understanding of customer journeys and identify areas of dissatisfaction. Marketing teams also integrate AI into the innovation process, media targeting, real-time creative optimization, and segment-specific messaging.

Conclusions: AI in Consumer Insights

Arguably, consumer insight is adopting AI faster and more enthusiastically than most other industries. This is driven by the broader range of benefits that AI brings to the sector, with solutions being offered by a plethora of specialist vendors.

As partly an 'academic pursuit,' consumer insight is both able and eager to explore the benefits and limitations of AI to optimize its commercial applications.

However, the research has highlighted a range of pitfalls in its applications to consumer research, ranging from in-built biases, hallucinations, to researchers becoming too dependent on the technology, which results in a decline in critical thinking and creativity.  These factors will be explored in more detail in future articles from GMO Research & AI.