Market research is undergoing a paradigm shift. Traditionally, generating consumer and market insights was a human-powered endeavor, reliant on expert analysts to interpret surveys and focus groups. Today, that model is being accelerated by artificial intelligence, enabling researchers to analyze data faster and uncover insights at greater scale. At a time when global market research revenues topped around $150 billion in 2025, the industry is again entering a period of rapid transformation driven by increasingly powerful AI models. Thanks to Large Language Models (LLMs), some forms of deep consumer research that once took weeks or months can be completed in near-real time and at a fraction of the cost.
Arnaud Frade
Enterprise GTM Lead, APAC
This AI revolution is fundamentally altering the value equation in research and analytics. If any company can deploy an AI model to crunch data and produce insights quickly, then competitive advantage increasingly depends on both advanced analytics and the quality of the underlying data. Analytics risks becoming a commodity, easily replicable by LLMs and automated tools. The proliferation of insights focused AI-powered startups are accelerating innovation and raising expectations across the market research industry. As a result, the source of differentiation is shifting upstream to the data itself. In an AI-enabled world, better input data yields better output insights. Organizations are discovering that the quality, at a respondent-level, granularity, and freshness of their consumer data are now the deciding factors in the quality of decisions made from those insights.
Crucially, “better data” in this context means data direct from consumers that is rich, unbiased, and relevant to the questions at hand. Whether it’s first-party data from your customer interactions, social media conversations reflecting authentic consumer voice, or large-scale survey panels with robust sampling, the integrity and representativeness of the input data drives the integrity of the insight. Even the most advanced AI models will mislead if fed flawed or unrepresentative data. On the flip side, organizations that excel at gathering high-quality consumer data can unlock outsized value from AI-powered research.
AI Disruption in Market Research: Faster Insights, Higher Expectations
The infusion of AI into market research is dramatically accelerating the insight generation process. Current AI models can already analyze massive data sets across reviews, social posts, sales data, and more in seconds, identifying patterns that humans might miss. This velocity and efficiency gains come with higher expectations from business stakeholders. If AI can generate insights on-demand, business units start to expect real-time consumer understanding. With this, companies are shifting from treating research as a periodic project to a continuous real-time process.
Furthermore, AI is enabling predictive and proactive insights. Rather than just reporting what happened, modern AI systems forecast trends and simulate outcomes. For instance, AI can comb through live consumer signals – reviews, social chatter, customer service calls – to detect emerging preferences or issues. It moves market research from lagging indicators to leading indicators of what’s coming. This shifts product development and strategy from reactive to proactive mode.
However, these advantages can only be fully realized if the input feeding these AI models is comprehensive and trustworthy. Faster insights are a double-edged sword: they can lead to rapid smart decisions or rapidly propagated errors, depending on the data foundation. This is why, in the AI era, organizations must double down on data quality.
Quality In, Quality Out: Why Better Input Data Matters More Than Ever
The principle of “garbage in, garbage out” has never been more apt. When analytics were manual, a skilled human could sometimes recognize and correct for bad data. In automated AI pipelines, the model will earnestly find patterns in whatever data it’s given – including noise or bias – and output confident but misleading insights. Data quality is therefore directly tied to insight quality. As generative AI becomes ubiquitous in analysis, companies that maintain superior data inputs will simply get better answers from the same algorithms.
This also applies to training data for LLMs themselves – as existing training sets age, continuously refreshed consumer data becomes increasingly valuable. These on-going consumer data points represent a welcomed solution to optimizing models and improving up-to-the-minute training.
Ultimately, investing in better data upfront pays off in more accurate, credible insights. AI’s pattern-finding prowess means it will amplify whatever signal (or noise) is present in the data. So business leaders must ask: Are we feeding our insight engines the best possible data? If not, that is where to focus investment – be it through improved survey sampling methodologies, social listening tools to capture real consumer voice, IoT sensors for real-time usage data, or data partnerships to enrich your view of the customer. The following case studies illustrate how organizations that prioritized direct consumer data quality have achieved breakthrough results.
Leading brands are already reaping the benefits of coupling AI with rich consumer datasets: Unilever’s Lipton ingested 36 million social media posts to predict the matcha tea trend early, fueling a new product launch that became the market’s top iced tea variant in 5 months. PepsiCo mined 157 million online beverage conversations (filtering out noise and spam) to design its Bubly sparkling water line, which exceeded $100 million in first-year sales. Mars fed an AI system (“Brahma”) with insights from 80,000 consumers across 11 countries, enabling up to 50 new product concepts per day and compressing innovation cycles from months to days. Procter & Gamble (P&G) leverages AI on billions of data points from sales and customer interactions to spot emerging needs, make faster decisions, and personalize marketing – demonstrating how better use of data translates to competitive edge.
The writing is on the wall: in the age of AI, the organizations that thrive will be those with the best data fuel for their powerful insights engines. In this context, AI is reshaping the research workflow, increasing speed and scale while elevating the importance of high-quality data and expert interpretation. Quality consumer data has become the lifeblood of competitive insights. Business leaders, especially at the board level, should view investments in data quality not as back-office issues, but as strategic imperatives for innovation and growth.
On the flip side, neglecting data quality can be perilous. AI will eagerly find patterns in statistical noise or echo chamber data and present them with confident charts, potentially leading executives astray. The cost of decisions based on bad insight can be immense. Thus, safeguarding the integrity of data inputs should be a governance priority. This includes championing data privacy and ethics (to ensure consumer data can be collected sustainably), and investing in data management and cleansing capabilities.
As AI becomes the norm in market research and analytics, researchers play an even more important role in designing studies, ensuring data quality, and translating insights into business strategy. The case for better input data from consumers is ultimately a case for better business outcomes. Companies that treat consumer data as a strategic asset, will ride the AI wave to deeper insights and smarter decision-making. For any business navigating the future, the message is clear: when AI comes to town, bring the best data you’ve got. The quality of your insights, and the quality of your decisions, depend on it.
Sources:
Use case – Unilever x Black Swan Data
Use case – PepsiCo x AI
https://www.pepsico.com/newsroom/stories/2021/artificial-intelligence-at-pepsico
Use case – AI for product research
Use case – Mars Brahma
Use case – Unilever x AI
Use case – P&G x AI
https://sloanreview.mit.edu/article/how-procter-gamble-uses-ai-to-unlock-new-insights-from-data/