Beyond the buzz: Navigating the brave new world of generative AI
July 14, 2023
While appetite for generative AI is on an upward trajectory, the practicalities of adoption are far less clear. That's why investing in custom B2B research could give you the edge over your competitors.
Regardless of how you feel about it, generative AI is here to stay. When we asked C-level executives about their perceptions of and plans for the technology, the majority told us incorporating it into their businesses is high on their lists of priorities.
That said, while appetite for generative AI is on an unstoppable upward trajectory — businesses want to use it, and fear they’ll fall behind if they don’t — the practicalities of adoption are far less clear.
Very few businesses, regardless of size, have a well-thought-out generative AI strategy, or feel they know how to use AI well.
And that’s why investing in custom B2B research could give you the edge over your competitors.
Generative AI’s real superpower (isn’t what you think)
As with any other disruptive technology — remember e-readers, or digital music downloads? — the biggest challenge of generative AI is figuring out where it fits into your processes, so you can use it to your best advantage.
Unsurprisingly, increased efficiency is the most commonly touted business benefit of the technology. In a survey of brand marketers we ran in conjunction with the Wall Street Journal, for instance, 64% of participants said they expect generative AI will help them cut costs and boost revenue.
And while some participants believe it will lead to lower headcount and smaller budgets, this is not the primary objective. If anything, 48% of participants told us they’ll be spending the money they’ll free up by using generative AI on other activities.
But while efficiency is undoubtedly a compelling benefit, it’s a very small part of what generative AI can bring to the table.
Even better, the technology can parse through huge amounts of data — including data that’s unstructured and, so, extremely costly and labor-intensive to access using conventional methods — and identify patterns humans would miss in a matter of seconds. As a result, your business can gain access to deeper, more accurate insights you can use to improve your business not just internally, but also externally.
“Generative AI,” says NewtonX’s CEO Sascha Eder. “can enhance your product or service significantly. For example, instead of giving your clients raw data, you can save them time and position yourself as a more valuable partner by running intelligence and giving them the most important insights.”
“Let’s take the example of ratings data,” continues R/GA’s Senior Vice President of Applied Intelligence Christian Kugel.
“If people have a bad experience, they tend to complain about it in a ratings form — as well as good experiences when people are pleased and delighted. How do you really understand the layers that might exist beyond the superficial one, and do that in a way that can scale? Using AI to help power the analysis makes the process much more manageable and straightforward.”
Maarten Lagae, Landor & Fitch’s Executive Director of Insights & Analytics, Americas, agrees: “With AI and natural language processing, it’s much easier to start making sense at scale of what people are saying in open-ended, unstructured data.”
Of course, this is the case with every new technology. Case in point, between 1997 and 2000, when eCommerce went mainstream, online fraud spiked from 100 cases a year to 11,000 cases a year. But, over time, people adapt. In eCommerce, for instance, merchants, payment services providers, and other stakeholders now use increasingly sophisticated technologies to lower — and, in some cases, even eliminate — the risk of fraud.
“I’ve no doubt this will also happen with generative AI,” says Sascha Eder. “But, for the C-suite to feel sufficiently prepared to address the risks and deploy the technology with confidence, we need to cut through the hype.”
Unfortunately, as things stand most of the information about how to use AI seems to come from hyperbole-filled, rocket emoji-packed social media posts and carousels. These one-size-fits all prompts and hacks are, at best, short-term solutions. And, at worst, they could backfire spectacularly. Just ask the lawyer who got into hot water for filing a brief that was full of fake precedents which ChatGPT made up.
To truly unlock generative AI’s potential, you should be relying on data-backed insights. And this is where custom research can be invaluable.
Cutting through the noise: how to make the most of generative AI with custom research
The biggest benefit of custom research is that it gives you clarity: an understanding of how you could benefit from generative AI, what your customers think about it, and what your competitors are doing.
This empowers you to use the technology intentionally, in a way that actually helps move your business forward regardless of the direction the markets — and the economy — are taking, instead of doing it just because everyone else is.
“It’s really important to not automate for the sake of automating,” explains Sascha Eder. “Success isn’t measured by how many processes you’ve managed to incorporate ChatGPT into. What matters is whether AI adds any value. So you need to ask yourself: why do you want to use it? What purpose will it serve?’
With custom research, you can identify which use cases might make the biggest difference to your business’ operations, and also how you can use it to better serve your customers.
On a recent project, for instance, we helped a large, well-known technology company learn more about its B2B customers’ expectations and attitudes towards AI-powered customer service chatbots.
Where state-of-the-market reports and white papers about industry trends are often broad — they have to appeal to a wide audience, after all — custom research enabled our client to gain an in-depth understanding of real customers’ current pain points and which capabilities they valued most in a chatbot.
These insights informed future development, allowing the company to direct its resources on the features that will be most impactful to their customers, instead of making educated guesses based on data that might not even be relevant to their industry.
In another project, we collaborated with a Big Four consultancy to help one of its clients understand how it could use AI to productize proprietary content and data assets.
Custom research enabled them to understand the mechanics of using generative AI to create these products. More importantly, they could validate whether there’s a market for such products and what customers would value most in them before investing the time, money, and effort on development.
Crucially, custom research enables you to be proactive. “Clients who commission research on generative AI,” observes NewtonX VP of Strategic Insights and Analytics Patiwat Panurach, “do so because they want to be thought leaders and, in turn, market leaders.
“Research connects the dots. If you understand AI’s strengths and limitations, and how that fits with your customers’ needs, you can position yourself as a strategic advisor instead of risking being shut out of the conversation.”
Some questions you could ask survey participants that would help you gain more clarity over your customers’ needs — or even identify market gaps — include:
How have you used AI over the past 12 months? And what are the three key use cases you’ve used it for?
How important has AI become to your workflow?
What do you like best about AI, and what do you wish AI could do better?
What are your three biggest concerns about AI, in order of priority?
Have you put risk-management procedures or a code of AI conduct in place in your organization?
“Asking the right questions,” continues Panurach, “will give you the insights you need to meet your customers or internal stakeholders where they are, instead of trying to second-guess how generative AI might fit into the equation.”
Knowledge is power
By 2032, the enterprise market for generative AI is expected to be worth $20.9 billion. Which is to say more and more businesses will be incorporating the technology into their workflows. But as tempting as it is to jump on the generative AI bandwagon, the reality is that this is a long game, not a short-term fix.
“We need to address the perception that, if companies aren’t using generative AI, then they’re behind,” says NewtonX’s Head of Engineering Kristoph Matthews. “The reality is that generative AI is just one tool in an organization’s toolbox, not a silver bullet.”
Sascha Eder agrees. “There are a lot of ways in which you can apply LLMs (large language models) and generative AI that have nothing to do with ChatGPT. For example, at NewtonX we’re leveraging natural language processing to identify audiences and guide them through surveys.”
The upshot is that you’re unlikely to get value from generative AI if your approach involves relying on reports that, at best, provide a broad assessment of your market. Or trying prompts from one-size-fits-all cheat sheets created by people who don’t understand the nuances of your business and seeing what sticks. The better strategy is to start by understanding your stakeholders’ pain points, and then use those insights to inform how you implement AI.
The good news is that, according to our research, nobody has worked this out yet. “Our data shows that people are still trying to figure out generative AI,” says Patiwat Panurach. “2023 is about trying out its various use cases. It’s in 2024 that things are going to get more serious.”
With most organizations still in the experimentation stage, there’s plenty of time to gather the insights you need to inform your way forward, so you’re prepared when competition heats up. As counterintuitive as it sounds, the best way not to get left behind in the generative AI arms race isn’t to plow on. First, you need to do your homework.
As noted by Helen Bentley, EY Seren Partner of Digital Strategy, Innovation & Experience: “Generative AI is an exciting new frontier offering limitless possibilities for innovation. As leaders, it is our responsibility not only to be creative, but also to think about acting ethically, designing reasonable regulations, considering data privacy, and ensuring use cases are architected to be inclusive of a diverse range of ideas and approaches. This will ensure the technology is helping us build a better working world in the longer term, not just serving short term commercial goals.”
“Ultimately,” concludes Eder, “it’s not about whether you use AI, but how thoughtfully you’ve integrated it. Even if openly available tools have democratized access to these technologies, it’s important to be deliberate with how you’re doing it.”
Want in-depth, accurate data on how you can use generative AI in your business to the best possible advantage?
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