Generative AI for Market Research: Opportunities and Risks

AI and market research.

"Great power comes with great responsibility," they say. That remark, made popular by the Spider-Man series, is well known even among non-Marvel fans. Even though the sentiment was initially used to describe superhuman strength, speed, agility, and resilience, it's still useful to bear in mind when attempting to understand the growth of generative AI.

Although the technology itself isn't new, the introduction of ChatGPT gave it around 100 million individuals in the span of only two months, giving many the impression that they had gained a superpower. But like with all superpowers, how you utilize them is what counts. The same applies to generative AI. There is the possibility of greatness, goodness, and evil.

Now is a crucial time for the top businesses in the world to decide how they will apply this technology. Consumers are unclear about how to prioritize their expenditure due to ongoing economic uncertainties and rising prices.

Generative AI can provide marketers an advantage in the fight for consumers' attention by taking into account both of these aspects. However, they must adopt a balanced viewpoint, recognizing both the opportunities and the hazards, and tackling both with an open mind.

What Generative AI means for insights work

The market research sector is no stranger to change; during the past several decades, the tools and processes accessible to specialists in consumer insights have advanced significantly.

We now have no way of knowing the scope or pace of the changes that increasingly available generative AI will bring. However, there are some foundations that must be established in order for decision-makers to rapidly choose how to react when new information becomes available.

It all boils down to asking the proper questions in the end.

What opportunities are there?

Currently, increased productivity is the main opportunity provided by generative AI. It may significantly quicken the procedures of coming up with concepts, facts, and written texts like the initial versions of emails, reports, or articles. It frees up more time to work on activities that need considerable human skill by increasing efficiency in these areas.

Faster time to insight

We believe that information summarization has a lot of potential for insights work in particular. For instance, the Stravito platform already uses generative AI to automatically summarize each unique market research study, eliminating the requirement for each report's original description to be manually written.

With the capacity to condense massive amounts of data into digestible chunks to swiftly respond to business concerns, we also see opportunity to further this use case. This may take the form of asking a query in the search box and receiving a brief response based on the company's own knowledge base, for instance.

Democratization insights through improved self-service

Additionally, generative AI may make it simpler for all corporate stakeholders to have access to insights without constantly involving an insights manager directly. Accessibility issues might be resolved by generative AI, which could benefit businesses wishing to more thoroughly incorporate customer input into daily operations.

Asking the incorrect questions, for example, is one of the frequent worries related to market research access by all stakeholders. In this use scenario, generative artificial intelligence may assist corporate stakeholders without research backgrounds to ask better questions by presenting them with pertinent queries connected to their search.

Tailored communication to internal and external audiences

Generative AI also offers the chance to customize messages for both internal and external audiences.

There are several potential uses in the area of insights. By making it simpler to customize insights messaging to various business stakeholders within the firm, it might enable knowledge sharing to have a greater impact. In order to speed up the research process and reduce back and forth, it might also be used to adapt briefs to research agencies.

What dangers exist?

Although generative AI may be a useful tool for insights teams, there are a number of hazards that businesses should be aware of before implementing it.

Quick dependence

Prompt dependence is one major concern. Because generative AI is statistical rather than analytical, it functions by anticipating the information that will be most likely to be said next. Even if you give it the incorrect instruction, you're still likely to receive a very strong response.


The ability of generative AI to combine accurate knowledge with false information makes things more difficult. This may be humorous in low stakes settings. However, the inputs for every choice need to be reliable when dealing with million dollar business judgments.

In addition, there are a lot of complicated concerns regarding customer behavior. Deeper inquiries into human values or emotions frequently call for a more nuanced viewpoint. For example, a query like "How did millennials living in the US respond to our most recent concept test?" would provide a clear-cut response. Key facts could be missed while attempting to synthesize big collections of research reports since not all questions have a single correct response.


Lack of openness about algorithm training is another important danger to be aware of. For instance, even when it can, ChatGPT may not always be able to provide the sources of its information since such sources may be difficult to confirm or may not even be real.

Additionally, since generative and other AI algorithms are trained by people and existing data, they may be prejudiced. This may produce objectionable responses such as those that are sexist, racist, or both. This would be an example of generative artificial intelligence (AI) making work less productive for firms aiming to confront biases in their decision-making and improve the world for customers.


ChatGPT may be used to create reports, meeting agendas, and emails, among other things. However, entering the essential information to produce those messages can put confidential business data at danger.

In reality, 1.6 million knowledge workers from a variety of sectors were the subject of an investigation by the security company Cyberhaven that revealed 5.6% had used ChatGPT at least once at work and 2.3% had entered sensitive corporate information into ChatGPT.

Employee use of ChatGPT at work has been prohibited by organizations including JP Morgan, Verizon, Accenture, and Amazon due to security concerns. And most recently, when looking into privacy issues, Italy became the first Western nation to outlaw ChatGPT, grabbing the attention of privacy regulators in other European nations.

The risks of entering data into a tool like ChatGPT must be understood by insights teams and anyone working with confidential research and insights. It's also critical to stay informed about both your company's internal data security policies and the policies of service providers like OpenAI.

We firmly believe that in order to effectively understand consumers in the future, sophisticated technology and human skills will still be required. Even the most advanced technology will be worthless if no one wants to utilize it.

Therefore, rather than merely implementing technology for its own sake, businesses should place an emphasis on responsible experimentation, finding the appropriate challenges to tackle with the right tools. Great power entails enormous responsibility. Brands need to decide how they will use it right now.

Yasmin Anderson

AI Catalog's chief editor

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