Generative AI makes product management inroads

One of the biggest challenges facing product managers is navigating the endless stream of data they rely on to develop new products and services. As a result, they are always looking for innovative ways to streamline and improve their processes. It’s no wonder then product managers are quickly warming to the use of the generative AI, an artificial intelligence-based technology that can generate a wide variety of content including text, images, and audio, to help them develop better ideas for products and services, better understand customer needs, and speed prototype generation, according to Forrester.

One of the key benefits of using generative AI for product managers is a more effective product strategy. With generative AI, product managers can engage with large data sets to identify and summarize top trends in a more complete and accurate manner. When predictive analytics are added to the mix, product managers can automate many of the tasks involved in knowledge management, which leads to a more effective formulation of product strategy.

“Product offerings tend to be technical in nature, which is why we are seeing product managers warm to generative AI,” says Sam Somashekar, principal analyst, product management research services for Forrester. “Product managers can get into a rut and not leverage data and trends from other industries when it comes to product development. Generative AI helps them comb through data to identify trends and starting points [for products] based on the data it is presented.”

A big benefit of generative AI for product development teams is that it allows them to develop prototypes in hours as opposed to days. Speedier prototype development means more opportunities for customer feedback, faster time to market, and improved market fit. “Generative AI can help product development teams put together workable prototypes when they wouldn’t have the time otherwise,” Somashekar says.

Generative AI algorithms can also make prototype development more efficient, reducing manufactured part costs between 6% and 20%, part weight between 10% to 50%, and development time between 30% and 50%, without affecting the interface between the part and the larger assembly, the report says.

With improving products and services and bringing product to market that create a point of differentiation for their company’s brand cited by product managers as high priorities the next 12 months, according to Forrester’s 2023 Product Management Survey, generative AI can help product managers meet these objectives. The reason, according to Forrester, is that generative AI provides a fuller, more accurate picture of current and impending customer needs from reams of data.

“Generative AI can determine how a product can be more aligned to customer needs,” Somashekar says. “It can reveal customer behavior patterns, what path a customer takes to purchase, how they purchase, what worked during the sales cycle and what didn’t.”

In addition to more efficient prototype development and creating products better suited to customers’ needs, generative AI can also help product managers identify product trends from a wide variety of sources, including such competitive intelligence as competitive product documentation, industry whitepapers, and customer feedback. Hence, generative AI can significantly streamline the time it takes to review these sources of information.

“Generative AI can be trained to pull data that supports the starting point for product traits,” says Somashekar. “Pulling together all the data needed for product development creates a more detailed product development strategy and go-to-market plan that considers market trends, customer behavior, and competitive actions. If generative AI does not have access to the right data, however, it cannot be trained to properly summarize that data.”

In addition to aiding product development and tailoring products to customers’ needs, generative AI can also help product managers stay on top of future product needs in the marketplace when fed data from non-traditional sources such as social media posts, search engine requests, blogs, and online articles. “Generative AI not only helps enhance product development and marketing future product strategies, but it also helps product managers plan for the future,” Somashekar says.

Despite the potency of generative AI in the product development process, product managers need to be aware there are risks involved with using the technology. Those risks include the creation and distribution of harmful content, copyright violations, disclosure of sensitive information, and the amplification of existing biases. In the case of the latter one common bias is around outdated stereotypes. “For example, a tool may return images and descriptions of a middle-aged white man in a suit when asked to generate a persona for a CFO in a bank” the report says.

Such bias occurs from a lack of transparency in data used to train large language models, which prevents a clear understanding of the biases. “Ensure that any generative AI tools you use are trained on diverse and representative data sets and monitor their performance to check for any developing biases,” the report says. “Push for policies that guide users — who should be represented by diverse leaders and subject-matter experts — on the type of data they can feed into generative AI applications and what they should avoid. If your organization is struggling to get enough of the right data to train generative AI models, invest in synthetic data to address the gap.”

While some product managers may have qualms about the use of generative AI due to its short track record when it comes quality and ability to deliver personalization at scale, they should not be dismissive of the technology.

“Generative AI doesn’t have to be perfect for product managers to use it,” Somashekar says. “Generative AI questions the status quo and challenges product managers to evolve and move forward in new ways.”

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