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The work of product managers centers on understanding what consumers want, creating items that meet those needs, and ultimately driving sales for their companies.
To help, many are embracing generative artificial intelligence to sift through troves of data, analyze it, and extract insights that can help them develop products more quickly and with stronger appeal.
"At a high level, it's taking big data on consumers — demographics, their values — and trying to match them with product designs," Alex Burnap, an assistant professor of marketing at the Yale School of Management, told Business Insider.
Burnap said AI can also be used to predict which product features and designs will be most attractive and are likely to generate profits. Burnap's research includes developing machine-learning algorithms that can understand customer needs and translate them into product features. He's also been a consultant for startups and major companies, including General Motors.
Several companies, including consumer-packaged-goods brands like PepsiCo and Kraft Heinz and automakers such as Toyota, are already using AI to support product development.
The technology's use in product development, Burnap said, will advance as it creates efficiencies in product managers' tasks and helps them develop products that boost business.
BI spoke with Burnap about what companies need to know about AI to understand consumers and inform product design.
The following has been edited for clarity and length.
How can companies use AI to understand consumers' needs and wants and design products accordingly?
There's been a lot of research into how to identify customer needs — or missing needs or market opportunities — to come up with new products that we didn't catch before. The early work looks very successful.
For example, using a tailored version of ChatGPT to search through all Amazon reviews in a particular product category to search for reviews that say, "Hey, this product was great, but it could have been blah, blah, blah."
It's absolutely being used to elicit customer needs, and I think we'll keep seeing work in that area.
How can companies use this information to inform product design and development?
The product-design life cycle starts with the initial idea of a product people might want. It goes through different design stages, engineering, and manufacturing until it's launched, and then you're marketing it with ads.
In almost every one of those stages, there's some role for generative-AI models. One is the very early stage of figuring out what people really want. That will continue to make advances.
These models will continue to help designers augment product development. Full automation could be challenging because you still need a lot of intrinsic human creativity, at least in the very early design process.
Yale School of Management
One of the advances we'll see initially for successful product design is on the back end, the marketing side: coming up with ads telling people what's out there and personalizing ads to the exact tastes of the user.
Personalization is the big difference with a lot of models. The current paradigm of serving ads is very scalable. Think of companies like Google AdWords; they're very good at taking ads created by publishers and targeting the right customer. We'll start to see advertisers not just target existing ads but generate ads personalized to you.
What advice do you have for corporate leaders considering using AI for product development?
First, consider if you actually need generative AI. In the product-design world, we say, is this a technology push or an actual need that AI can help you solve?
Companies need to understand the actual customer needs. If product managers are using ChatGPT, is it helping them understand customer needs that have been missed? A lot of questions are not super clear unless you pilot-test them.
Then, think through whether to use your own engineers and data scientists to come up with AI models or existing services, like ChatGPT. Weigh the pros, cons, and limitations of each.
Companies also must establish best practices and consider the risks and regulatory constraints around using AI, depending on the industry. And are those risks worth the benefits that you think it will bring?
