“With great power comes great responsibility.” You don’t should 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 consider when making sense of the rise of generative AI.
While the technology itself isn’t recent, 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 isn’t any different. There may be the potential for excellent, for good, and for evil.
The world’s biggest brands now stand at a critical juncture to determine how they’ll use this technology. At the identical time, economic uncertainty and rising inflation have continued — leaving consumers unsure of learn how to prioritize spending.
Considering each aspects, Generative AI might help give brands a leg up within the battle for consumer attention. Nevertheless, they should take a balanced perspective – seeing the chances but additionally seeing the risks, and approaching each with an open mind.
What Generative AI means for insights work
The market research industry isn’t 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 may help decision makers determine learn how to respond quickly as more information becomes available.
Ultimately, all of it comes back to asking the precise questions.
What are the opportunities?
Currently, the first opportunity offered by generative AI is enhanced productivity. It may possibly 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 variety 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 eat format. For instance, this might appear like typing an issue into the search bar and getting a succinct answer based on the corporate’s internal knowledge base.
For brands, this is able to mean with the ability to answer easy questions more quickly, and it could also help deal with a variety 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 with no need 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 every day operations.
It could also help to alleviate common concerns related to all stakeholders accessing market research, like asking the mistaken questions. On this use case, generative AI might 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 varied business stakeholders throughout the organization. It may be used to tailor briefs to research agencies as a method to streamline the research process and minimize the backwards and forwards involved.
What are the risks?
Generative AI might be an efficient tool for insights teams, however it also poses various risks that organizations should concentrate on before implementation.
Prompt dependency
One fundamental risk is prompt dependency. Generative AI is statistical, not analytical, so it really works by predicting the almost certainly piece of data to say next. In the event you give it the mistaken prompt, you’re still more likely to get a highly convincing answer.
Trust
What becomes even trickier is the way in which that generative AI can mix correct information with misinformation. In low stakes situations, this might be amusing. But in situations where million dollar business decisions are being made, the inputs for every decision have to be trustworthy.
Moreover, many questions surrounding consumer behavior are complex. While an issue 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.
Transparency
One other key risk to concentrate to is an absence of transparency regarding how algorithms are trained. For instance, ChatGPT cannot all the time inform you where it got its answers from, and even when it may, those sources may be unattainable to confirm and even actually exist.
And since AI algorithms, generative or otherwise, are trained by humans and existing information, they might be biased. This could result in answers which are racist, sexist, or otherwise offensive. For organizations trying to challenge biases of their decision making and create a greater world for consumers, this is able to be an instance of generative AI making work less productive.
Security
A number 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.
The truth is, an evaluation conducted by security firm Cyberhaven found that of 1.6 million knowledge employees across industries, 5.6% had tried ChatGPT not less than 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 in the near past, 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 concentrate on 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 long run of consumer understanding will still have to mix human expertise with powerful technology. Probably the most powerful technology on this planet can be useless if nobody actually wants to make use of it.
Subsequently the main focus for brands ought to be on responsible experimentation, to search out the precise problems to resolve with the precise 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 determine how they’ll use it.