Why AI alone can’t answer your market research questions

Even if an AI model is trained not to lie, flawed data still does.  

If you’re collecting data with outdated research methods like surveys or incentives, AI won’t change anything about its accuracy. It might not be intentional, but prompting an AI, even an in-house one, with your flawed data won’t uncover new, meaningful insights. The data is the data – asking AI to parse it won’t make it better quality. 

Skipping surveys entirely and going straight to an AI with the idea that it will tell you what it knows about consumer preferences will only get you more flawed data, since it will crawl pre-existing surveys. 

An over-reliance on AI in decoding market research data not only reduces the resonance of future campaigns, but it creates a false sense of accuracy. Taking what AI says to be objectively true can lead to a ripple effect of false data, informing go-to-market decisions that aren’t based in reality – taking chunks out of your bottom line over time. 

Understanding the limitations of AI for market research

AI can be an extremely helpful tool, especially in the field of market research. 

It’s not so much that the AI is flawed, but the idea that AI can create more enriched, accurate data out of flatter points informed by bias and uncontrolled environments that isn’t correct – at least for now.  

Treating AI like an oracle that can answer all of your questions is a slippery slope. AI can only draw upon what already exists, including data collected by and from humans.

Weak points in AI that can negatively influence your data: 

• Struggles with nuance. Understanding complex business goals, culture, and brand voice specifics are a key reason to keep humans at the forefront of your research. 

• Analyzing without bias. AI was developed by humans, and humans act upon bias whether they’re aware of it or not.  

• Mitigating reasoning. AI can overthink and draw conclusions that are false based on information that technically supports them based on conjecture or conflating evidence.  

AI is just as, if not more susceptible to misinterpretation as we are, since it’s trained to look for connections and missing context at every opportunity, even where there might not be any need. It often takes human insight and prompting to get an AI to understand the information that’s been given to it in a way that makes sense or meets the goal of the user.   

Research World reports that even when AI appears to create something new with data, it’s often reported that the cause isn’t a semblance of sentience but hallucination. If you continually operate off of flawed data, for example, data that is under representative of a demographic, there’s potential for negative stereotypes to be reinforced or for key insights to be glazed over.  

The right way to use AI for market research 

Understanding your positionality in conducting your research is key, and it’s by virtue not something that AI can do for you. 

Lived experience and a deep understanding of the work is essential to asking truly helpful questions that yield truly helpful data points. 

Human researchers are essential. AI should be used by them to do what it does best, which is scaling, automating the mundane, and speeding up basic processes. When you surrender control to AI and don’t audit, your insights can only echo a flat interpretation of already imperfect data and in the worst case, affirm harmful biases. It’s part of ethical research to conduct it with a critical lens – one that can detect nuance, context, and interpret the “why”. 

Large language models like Chat GPT can only echo pre-existing sentiments. Without lived experience and positionality, there isn’t a point of view to which insights can be made richer by.  

There’s a common misconception that AI can’t be biased, and it’s a dangerous one to believe. The researcher is just as important as the research, and most open AI tools have been trained imperfectly. Decoding data requires more than multitudes; it requires a lived experience from which to interpret multitudes of data. 

The way forward isn’t to reject AI completely, of course. Winning brands will treat it as a consulting tool and will conduct research with disruptive researchers that won’t try to dress up flawed data with an AI’s imitation of what an actual researcher can conclude.  

In the wild testing gleans real insights right where your consumers live, getting you actionable data that you can make better go-to-market decisions with.