By: Ethan Rogers

Artificial intelligence is increasingly influencing the research space, offering potential for efficiency, insights, and innovation. However, amid all the excitement, there is still a misconception in the market that simply integrating AI will automatically provide a competitive advantage. What some businesses overlook is the reality that AI is a tool—not a strategy—and without proprietary data, companies may find themselves blending into the competition.

Katrina Starnes, founder of market research company The Rise Group, cautions that businesses failing to understand AI’s limitations may face challenges. “AI is only as effective as the data it processes,” she explains. “If businesses rely predominantly on publicly available information, they are essentially working with the same data as their competitors. That alone doesn’t provide a significant advantage; it may lead to standardization.”

One of the significant challenges in AI-driven research is poor-quality data. “If AI is fed flawed, outdated, or biased data, it will likely produce similarly flawed, outdated, or biased insights,” Starnes notes. This issue, known as ‘garbage in, garbage out,’ has been an ongoing challenge since the early days of machine learning.

For instance, many companies today are using AI for market research, survey analysis, and strategic planning. But if the AI is working with incomplete or unreliable data sources, businesses may end up making decisions based on incorrect or incomplete information.

And the issue isn’t just theoretical; it’s happening in real-world scenarios. In the U.S., public data that has been used for years can quickly become unreliable due to regulatory changes, privacy issues, or shifts in the political landscape. “If AI depends on publicly available data, and that data becomes unreliable or inaccessible, the insights AI generates will reflect these changes,” Starnes explains.

As the demand for innovation increases, many businesses mistakenly believe that adopting AI will instantly set them apart. In reality, the widespread use of AI could lead to the opposite effect—industry-wide similarity.

“Everyone is using AI to create marketing strategies, customer insights, and growth plans. If you’re drawing from the same data sources and using the same algorithms as your competitors, it can become difficult to distinguish your brand. It might lead to uniformity,” Starnes explains.

True competitive advantage doesn’t come directly from AI itself, but from the unique data it is trained on. Companies that invest in proprietary research, customer insights, and unique datasets can utilize AI in ways their competitors may not be able to. “It’s about using AI wisely, not as a shortcut, but as a tool that enhances the value of high-quality, exclusive data,” she advises.

Another risk that is often overlooked is how AI can affect human decision-making. “There’s growing concern that as people rely more on AI, they might lose their ability to think critically,” Starnes warns. “It’s akin to using a calculator without understanding the underlying math. If you don’t fully understand how AI reaches its conclusions, you could miss errors that are difficult to spot.”

This reliance on AI has already led to some missteps, from companies rolling out AI-generated campaigns with factual inaccuracies to organizations making flawed financial decisions based on misinterpreted data. AI, for all its strengths, cannot provide context or assess the reliability of information.

Rather than rushing to implement AI as a one-size-fits-all solution, businesses should consider a more thoughtful approach. “Sometimes, you need to slow down to move faster,” Starnes advises. “For AI to be a true advantage, it requires a solid foundation: clean, well-maintained, and proprietary data.” This involves conducting original research and collecting first-party data. To ensure data quality and governance within the organization, teams need comprehensive technology training that allows them to assess AI-generated insights critically.

Starnes also highlights another hidden risk: data leakage. “If your team is uploading sensitive data into open AI models, you could unintentionally be sharing that information back into the system, which competitors could access.” Companies must be cautious about how they integrate AI and ensure that their competitive intelligence is protected.

“If you want to lead in your industry, don’t just use AI because it’s widely available,” Starnes concludes. “Use it in combination with human intelligence, and with the right safeguards in place. Invest in your own data. Stay sharp. And always verify the source.”

Published by Stephanie M.