We all know that AI is the talk of the town in market research circles. A “word bingo” game at any recent industry conference would have seen AI as the outright winner. AI also dominated Greenbook’s 2024 Grit Report. but how are these many conversations translating to action for insights leaders? 

The tantalizing potential of AI in research and insights is undeniable: AI-powered tools can automate tedious tasks, crunch massive datasets quickly, and uncover patterns that human analysts might overlook. In theory, Machine Learning can liberate researchers to focus on higher-level strategic work and enable faster, more responsive research processes. 

However, AI’s hype is matched by chronic anxiety about its implications for the future of market research, including: 

• Job Security: Will AI render human researchers obsolete? 

• Output Credibility: Could biased AI models lead to skewed or misleading insights? 

• Research Rigor: Will overreliance on AI keep research standards in mind? 

• Insight Quality: Can we trust the outputs of AI? 

AI is here to stay. We’re already seeing how emerging technologies will reshape our industry in the years ahead, and we must change our mindset and adapt our practices accordingly. 

Babita asserts, “if you have an innovative mindset, you’ll be comfortable with the potential discomfort AI will bring.”  

Many industry leaders share the same view: organizations that treat AI as optional will eventually fall behind those who build it into how decisions get made. 

Whilst the AI revolution may be daunting, it’s also an exhilarating opportunity for researchers who are willing to embrace change and push the boundaries of possibility. 

McKinsey reports that although 55% of Chief Insights Officers have already adopted some AI technologies, only a minority have fully embedded AI in insights practices, and even fewer are leveraging it to create substantial business impact. 92% of insights leaders are planning to increase investment in AI by 2028, but with leaders struggling to apply the capabilities of technology, what does embracing AI look like in practice? 

AI is becoming increasingly useful in generating predictive insights from brands’ vast troves of data. CPG CMI teams are harnessing predictive analytics to answer business-critical questions such as: 

• Which new product or marketing ideas are most likely to resonate with consumers? 

• How might different consumer segments respond to an ad campaign or brand message? 

• What are the key drivers of brand performance and market share in each category? 

“We’ve got quite a few organizations that have amassed a great deal of data on a platform that is standardized at scale, and it’s good data,” Babita reveals. “It gives us the ability to overlay AI to generate new ideas. But what does this enable us to predict for certain categories and certain brands?” 

“This predictive use case for AI is incredibly powerful. Imagine being able to forecast consumer trends, anticipate market disruptions, or test new concepts before they even exist, all based on the patterns and insights hidden in your historical research data.” 

However, some early adopters on the buy-side urge caution.  “Context is often missing when you rely on AI for prediction, and results can often be ‘just weird’. A recent analysis suggested that ‘milk pizza’ might be appealing to our target market. Without specialist curation, AI can be misleading and undermine the role of consumer insight,” says Babita. 

How can leaders balance automated analytics output with the human intelligence needed to deliver valuable predictive insights? 

Global consumer goods leader Unilever leverages AI to predict changing consumer preferences and informs innovative products that meet future consumer needs. Unilever’s psycho-analysis tool measures app-based interactions to define customer sentiment around new products and brand interactions. Improved understanding of consumer behaviors and motivations helps Unilever anticipate their future needs and has already seen new product launch success rate increase by 30%. 

Gartner reports that 32% of leaders believe they have made incorrect decisions due to missing foundational insights. Democratizing data across any business is critical to aid common understanding across teams, informing faster decisions and better outcomes.  

In the past, research requests would require familiarity with the repository of past research studies. New technology-driven capabilities are enabling more intuitive searchability of research portals to offer users specific answers to their questions. 

AI search is fast, eliminating the time-consuming and labor-intensive process of trawling through multiple PowerPoint decks to find valuable data. In early pilots, AI search on research portals has already cut hours from research requests, with test users getting the specific answers they need in a fraction of the time.   

Leading knowledge management platform Strativo warns of the dangers of organizational memory loss. Gartner divulges that 47% of knowledge workers have trouble finding information that is relevant to their roles. Several studies show that organizational memory loss can affect job satisfaction, reduce efficiency and increase the cost of doing business.   

AI is already offering significant advances in data access and searchability, but crucially, it also maximizes the utility of costly insight generation. In the future, we are likely to see a more seamless way of working between AI and humans in knowledge management systems, as well as more cross functional integration to enhance efficiency.  

In the more traditional world of online consumer surveys and access panels, AI isn’t just a productivity tool; it has also become a frontline defense. As incentives have grown and survey links are shared more widely, sophisticated fraud attacks and ‘bad actors’ have followed, flooding consumer panels with low-quality or fabricated responses. 

The rise of these attacks makes combatting fraud even more vital for tomorrow’s insights leaders. AI-powered tools can spot patterns humans would miss (duplicate identities, bots, speeders, click farms) and flag or remove suspicious responses at scale, protecting business continuity and improving data quality. 

“Fraud isn’t going away,” advises Caroline. “It’s about how we stay one step ahead of the fraudsters. We need to make it so challenging for bad actors to get into our surveys that they just don’t bother.” 

By combining cutting-edge AI with decades of expertise in panel management, leading research firms are guaranteeing truthful, reliable data to inform critical decisions, even as fraudsters become savvier and more determined. 

Of course, getting to this level of AI sophistication requires a strong foundation of clean, consistent, and well-structured data. 

Alternatives to real respondents are definitely gaining traction amongst research buyers. Greenbook’s 20 Grit Report survey data suggests that twice as many buy-side researchers are looking at non-sample options compared to 2022. ‘Synthetic sample’, which is AI-generated consumer responses, is arguably the most controversial aspect of AI applications right now.  

At Delineate, we believe that synthetic data can be useful in tightly-controlled scenarios, for example, exploring edge cases or stress-testing scenarios, but it does not remove the need for high-quality, real consumer signal. It’s an extension of your data, not a replacement for it. 

The inevitable prominence of AI raises existential questions about the future of research careers. Will intelligent machines displace human insight professionals? 

The consensus from my conversations is that research professionals will continue to play a vital role in generating insight. But CMI teams will need to adapt to work alongside emerging technologies. In the next few years, AI will change how we commission, analyze, curate, and share insights. Leaders are already thinking about the shape of their teams and the skills and training needed to make the most of AI-powered insight. 

Many senior insight leaders say the goal isn’t to be an expert in every new technology, but to promote a culture of innovation where teams are encouraged to experiment, learn, and share what works. 

Broadly, Gen Z employees tend to be more native to AI tools and algorithms, but often have less applied category and business experience. Many more experienced team members have the opposite profile: deep consumer and commercial knowledge, but less fluency with new tools. Both groups need ongoing training, not just on how AI works, but on how to combine algorithms with domain expertise. And in reality, champions can come from any age or background. Curiosity and openness to new ways of working matter far more than the year on someone’s CV. 

Rather than being replaced by AI, researchers and insight professionals are quickly adding ‘prompt engineering’ to their toolkits, using AI as co-pilots to marry human and machine intelligence. As specialists, we are becoming more adept at asking the right questions of our AI tools and interpreting technology-driven outputs through a nuanced human lens. This shift will require researchers to upskill in areas like data science, analytics, and storytelling, where AI cannot deliver contextual, engaging translation. 

The fundamental human skills of critical thinking, creativity, and empathy will be increasingly harnessed in the career of the future researcher to turn automated analysis into relevant recommendations. 

“We fundamentally believe our sector is a people business,” confirms Caroline. “We source information from people, we sell information to people. We are about reflecting the truly diverse world that we have the accountability and the privilege to serve.”  

Insights teams are increasingly expected to do more with less, provide more definitive answers, and support faster decision-making across their entire business. Rapid and effective AI deployment is a means to an end: a dynamic tool for augmenting and scaling human expertise, not a substitute for insight specialists. 

Leonard Murphy, Chief Advisor for Insights and Development at Greenbook, makes a similar point: as AI automates more of the research process, process skills on their own matter less. What increasingly sets insight teams apart are the so-called soft skills (intuition, creativity, context framing, influencing, communication, and sense-making), which become the non-disruptive foundation for thriving in an AI-driven world. 

The real value of AI-powered analytics will come from a close partnership between human and machine. The most effective researchers of tomorrow will continually upskill and adapt to apply their own consumer understanding to automated analysis and translate complex insights into actionable business recommendations. 

The potential of AI is uncontested, but most insights teams have yet to fully safeguard against the looming risks and resulting business damage of emerging technologies. 

Biased data will always deliver biased results. Inaccurate insights inform flawed decision-making and even discriminatory outcomes if left unchecked. The USC Information Sciences Institute recently found that over a third – 38% – of data used by AI contained bias. Gender and racial bias are routinely common in consumer data sets, presenting not only a risk of discriminating against large groups of consumers, but the danger that diverse consumers will be under-served or even ignored in product development recommendations. 

As an industry, we’ll need robust processes to audit AI models for bias, ensure diverse and representative training data, and maintain human oversight of machine-generated insights.  Researchers and brands will rightly demand to know the “why” behind the “what”: transparency and interpretability in AI research must also be prioritized. Transparency in AI involves developing systems that are explainable, accountable and available for human interrogation. 

What does the future hold for AI in market research?  

“Growth never comes from comfort zones,” shares Caroline.  “Researchers will need to lean into the discomfort, ask tough questions, and experiment fearlessly to realize the full potential of AI.” 

Researchers are already experimenting with automated technologies to speed up and scale up insights through: 

• Automated data collection 

• Machine-powered analysis 

• Knowledge management 

• Synthetic data collection 

• Predictive modeling and insight generation.  

 As technology advances at breakneck speed, the possibilities of AI in research will only expand, and the researchers of today are balancing machine efficiency and human expertise to anticipate tomorrow’s decisions. 

FMCG giant Nestlé is leveraging AI for innovative market research and future product development. Nestlé’s GenAI platform gathers data on consumer preferences and conducts rapid market trend analysis across restaurant items, home cooking habits, social media and a variety of online sources to accelerate product innovation. Nestlé uses AI to analyze vast amounts of data in short time frames and supports automated analysis with strategic human knowledge, to identify emerging consumer needs and validate new product concepts for the most successful market launch. 

The successful integration of AI will require a careful unity between machine efficiency and human expertise, between speed and quality, and between innovation and responsibility. If we can become revolutionary innovators to meet the demands of the AI-driven world and the consumer needs of tomorrow, our industry’s brightest days and better opportunities to drive business-critical decision-making lie in wait for us. 

Delineate help the world’s largest consumer brands to conduct faster research and deliver more accurate consumer insights that influence business-critical decisions. The Coca-Cola Company, Ancestry.com and Nomad Foods employ our brand tracking and campaign evaluation technologies to mobilize the data-driven insights that create a competitive advantage.  

Discover how Delineate can transform your insights capabilities to meet the consumer needs of tomorrow.