Home Artificial Intelligence Generative AI for Market Research: Opportunities and Risks

Generative AI for Market Research: Opportunities and Risks

Generative AI for Market Research: Opportunities and Risks

“With great power comes great responsibility.” You don’t must be a Marvel buff to acknowledge that quote, popularized by the Spider-Man franchise.  And while the sentiment was originally in reference to superhuman speed, strength, agility, and resilience, it’s a helpful one to bear in mind when making sense of the rise of generative AI.

While the technology itself isn’t latest, the launch of ChatGPT put it into the hands of 100 million people within the span of just 2 months, something that for a lot of felt like gaining a superpower. But like all superpowers, what matters is what you employ them for. Generative AI is not any different. There’s the potential for nice, for good, and for evil.

The world’s biggest brands now stand at a critical juncture to make your mind up how they are going to use this technology.  At the identical time, economic uncertainty and rising inflation have endured — leaving consumers unsure of prioritize spending.

Considering each aspects, Generative AI will help give brands a leg up within the battle for consumer attention. Nevertheless, they should take a balanced perspective – seeing the probabilities but additionally seeing the risks, and approaching each with an open mind.

What Generative AI means for insights work

The market research industry is not any stranger to alter – the tools and methodologies available to consumer insights professionals have evolved rapidly over the past few many years.

At this stage, the extent and speed of the changes that increasingly accessible generative AI will bring are something we are able to only speculate on. But there are particular foundations to have in place that can help decision makers determine respond quickly as more information becomes available.

Ultimately, all of it comes back to asking the best questions.

What are the opportunities?

Currently, the first opportunity offered by generative AI is enhanced productivity. It may well drastically speed up the processes of generating ideas, information, and written texts, just like the first drafts of emails, reports, or articles. By creating efficiency in these areas, it allows for more time to be spent on tasks that require significant human expertise.

Faster time to insight

For insights work specifically, one area we see a whole lot of potential in is summarization of data. For instance, the Stravito platform has already been using generative AI to create auto-summaries of individual market research reports, removing the necessity to manually write an original description for every report.

We also see potential to develop this use case further with the power to summarize large volumes of data to reply business questions quickly, in a straightforward to devour format. For instance, this might appear like typing a matter into the search bar and getting a succinct answer based on the corporate’s internal knowledge base.

For brands, this may mean having the ability to answer easy questions more quickly, and it could also help maintain a whole lot of the bottom work when digging into more complex problems.

Insights democratization through higher self-service

Generative AI could also make it easier for all business stakeholders to access insights while not having to directly involve an insights manager every time. By removing barriers to access, generative AI could help support organizations who wish to more deeply integrate consumer insights into their each day operations.

It could also help to alleviate common concerns related to all stakeholders accessing market research, like asking the flawed questions. On this use case, generative AI will help business stakeholders without research backgrounds to ask higher questions by prompting them with relevant questions related to their search query.

Tailored communication to internal and external audiences

One other opportunity that comes with generative AI is the power to tailor communication to each internal and external audiences.

In an insights context, there are several potential applications.  It could help make knowledge sharing more impactful by making it easier to personalize insights communications to numerous business stakeholders throughout the organization. It may be used to tailor briefs to research agencies as a option to streamline the research process and minimize the backwards and forwards involved.

What are the risks?

Generative AI may be an efficient tool for insights teams, but it surely also poses various risks that organizations should pay attention to before implementation.

Prompt dependency

One fundamental risk is prompt dependency. Generative AI is statistical, not analytical, so it really works by predicting the probably piece of data to say next. For those who give it the flawed prompt, you’re still prone to get a highly convincing answer.


What becomes even trickier is the way in which that generative AI can mix correct information with misinformation. In low stakes situations, this may be amusing. But in situations where million dollar business decisions are being made, the inputs for every decision must be trustworthy.

Moreover, many questions surrounding consumer behavior are complex. While a matter like “How did millennials living within the US reply to our most up-to-date concept test?” might generate a clear-cut answer, deeper questions on human values or emotions often require a more nuanced perspective. Not all questions have a single right answer, and when aiming to synthesize large sets of research reports, key details could fall between the cracks.


One other key risk to listen to is an absence of transparency regarding how algorithms are trained. For instance, ChatGPT cannot all the time let you know where it got its answers from, and even when it will possibly, those sources may be not possible to confirm and even actually exist.

And since AI algorithms, generative or otherwise, are trained by humans and existing information, they may be biased. This may result in answers that are racist, sexist, or otherwise offensive. For organizations seeking to challenge biases of their decision making and create a greater world for consumers, this may be an instance of generative AI making work less productive.


A few of the common use cases for ChatGPT are using it to generate emails, meeting agendas, or reports. But putting within the crucial details to generate those texts could also be putting sensitive company information in danger.

Actually, an evaluation conducted by security firm Cyberhaven found that of 1.6 million knowledge employees across industries, 5.6% had tried ChatGPT a minimum of once at work, and a couple of.3% had put confidential company data into ChatGPT.

Firms like JP Morgan, Verizon, Accenture and Amazon have banned staff from using ChatGPT at work over security concerns. And only recently, Italy became the primary Western country to ban ChatGPT while investigating privacy concerns, drawing attention from privacy regulators in other European countries.

For insights teams or anyone working with proprietary research and insights, it’s essential to pay attention to the risks related to inputting information right into a tool like ChatGPT, and to remain up-to-date on each your organization’s internal data security policies and the policies of providers like OpenAI.

It’s our firm belief that the longer term of consumer understanding will still must mix human expertise with powerful technology. Essentially the most powerful technology on the earth might be useless if nobody actually wants to make use of it.

Due to this fact the main target for brands ought to be on responsible experimentation, to seek out the best problems to unravel with the best tools, and never to easily implement technology for the sake of it. With great power comes great responsibility. Now’s the time for brands to make your mind up how they are going to use it.



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