HBR: To drive AI adoption, build your team's product management skills

What the article is about

Published in Harvard Business Review on February 3, 2026, this piece argues that most organizational AI training programs focus too narrowly on prompt engineering while missing the capability that actually determines whether AI use takes root: product management discipline. The authors are Amanda Pratt, a PhD candidate at Stanford’s Management Science and Engineering program and a 2025 graduate fellow at the Stanford Institute for Human-Centered AI, and Melissa Valentine, an associate professor at the same institute and senior fellow at HAI.

Context

The article is grounded in research on AI adoption at Google, which gives its argument empirical weight rather than positioning it as opinion. The authors observed that teams who successfully embedded AI into their workflows were not necessarily the best at writing prompts — they were teams that had learned to apply structured product thinking to their work.

Key argument

Pratt and Valentine propose a four-step framework for teams adopting AI: define a valuable problem, evaluate candidate solutions, experiment rapidly with real inputs, and integrate successful practices durably into day-to-day work. The argument is that knowing what to solve matters more than knowing how to phrase a query. Prompt skills are necessary but not sufficient; without the discipline to identify where AI can produce meaningful output, teams produce shallow or short-lived AI experiments that don’t persist after the initial excitement.

Product managers are already trained in this mode of working — problem discovery, solution evaluation, iteration, and systematic integration are core PM activities. The article’s practical implication is that organizations investing in AI adoption should spread this way of thinking across functions, not just rely on technical training programs.

Who it is useful for

The article is addressed to business leaders, HR and L&D managers, and senior team leads who are designing AI adoption programs. It is also directly relevant to product managers who want to make a case for broader PM influence within their organizations, since it provides research-backed framing for why product thinking is the critical variable in AI adoption — not tool access or prompt skill alone.

The four-step framework is simple enough to use as a facilitation structure for a team workshop, and the Google research context gives it credibility with executives who are skeptical of management-speak around AI.