A key product management discipline is identifying an initiative’s target customer, value proposition, and strategic business value. Business value from data science initiatives often involves improved decision-making capabilities, increased productivity, and sustained competitive advantages. The data science product, including the product’s data visualizations, predictive models, and LLMs, are part of the solution.
“AI is the ‘how’ and not the product, so if using AI doesn’t solve a customer problem, you shouldn’t do it,” says Ibrahim Bashir, VP of product management at Amplitude. “If an AI-driven feature doesn’t positively impact a key business metric, such as time-to-value or retention, it shouldn’t be a priority.”
Karl Mattson, CISO at Noname Security, says that leading product managers first consider the end state of the user or customer experience and work backward to build the product. He says, “For data science initiatives, the end goal is informing quality decisions. We truly have to understand the nature of the decisions to be made on our data product and not be obsessed first over the technical how.”
