How will AI impact market research and insights teams? The research field has been slower than other industries to adopt AI due to security, transparency, and integration concerns. Yet, as demands for rapid results increase and research becomes more democratized across business functions, AI adoption is expected to grow.

According to recent research from Greenbook, compared with centralized market research teams, data and analytics functions are more focused on outputs than inputs and are more likely to adopt AI. These functions use a wider mix of tools and techniques to talk to consumers, get insights, plan business activities, and inform decision-making.

“What can be automated will be automated,” Lenny Murphy predicted in his keynote at Alida Activate 2024. As the Chief Advisor for Insights and Development Greenbook acknowledged, “that trend isn’t an AI-created thing, but AI accelerated it.”

In this blog, you’ll see how you can integrate AI into your research practice, while leveraging uniquely human capabilities that AI will never have. You’ll understand potential barriers to AI adoption and learn how you can navigate the challenges to deliver faster, more relevant business insights. 

AI models are hungry for accurate data 

The market research industry is data dependent. With a mix of quantitative and qualitative methodologies and increasingly accessible tools, researchers can collect more data than ever before. But “more” typically doesn’t mean “better.” Unfortunately, says Lenny, large-scale surveys designed to drive clicks and completes have the potential to drive fraud. 

AI could increase fraud, as automated bots can complete surveys at scale and even use AI-generated copy to complete open-ended questions. In some cases, digital AI personas have become so sophisticated that their answers are virtually the same as a human would generate and may be impossible for researchers to detect.

What’s more, with many use cases relying on the same data, contamination is a big concern. Researchers are tapping into centralized data lakes – sometimes data oceans – to help product, marketing, branding, and customer experience teams make decisions. Imagine that data sourced from a digital bot becomes the lynchpin for not just one research study, but several. Suddenly, everyone in the organization is making decisions based on untrustworthy information. 

The rise of AI puts more pressure on data and research providers to automate quality checks and detect fraud. To that end, many companies are looking to alternative sample sources and building their own panels. Identity verification steps like double opt-in and multi-factor authentication validate that study participants are who they claim to be. 

Researchers want solutions that help them interpret data

Greenbook’s survey found that buyers of research solutions look for quality, cost, and speed. Ease of interpreting results is a key factor that defines quality. While speed is an important purchase decision criterion, according to Lenny, it’s considered table stakes for any research solution. 

Market research solutions that embed AI capabilities can increase the accuracy and speed of data interpretation. AI is a master of combining data from multiple sources. “You can use AI to leverage existing data, whether that’s on the web or within the organization, and synthesize information to answer a ton of questions,” says Lenny. AI can ingest data in different formats and see patterns at scale, in ways humans will never be able to achieve. It can also categorize qualitative feedback based on topics of interest and sentiment analysis.

With enough of the right kind of data, AI can also make predictions about the future. It’s essential that AI models be transparent and make their workings understandable so researchers can confidently explain how findings are generated. 

How Market Researchers Can Add Value on Top of AI

If AI is so great at synthesizing data, making recommendations, and predicting the future, where does that leave us humans? How do we demonstrate value to our organizations? 

If researchers don’t need to spend days normalizing data and combing through customer feedback for golden nuggets, just imagine the other priorities you can tackle. Instead of focusing on the process, you can focus on outcomes.

The insights that AI surfaces will lead researchers to dig deeper. Research can become more focused on the unknowns, more agile, and more exploratory. “That’s why we exist,” says Lenny, “to understand the cycle of engage, understand, and activate, because that’s what gets people to do what you want them to do. That means we need to understand ‘why.’” 

With the support of AI in the background, researchers can spend more time in the foreground, helping stakeholders understand what data means and make difficult decisions. As Lenny says, “if your skillset is understanding people, understanding markets, understanding the forces in the business that drive decisions for action, then you have a wide-open future.”

For more insights and best practices...

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