Learn the product management mindset that Googlers are using to deeply adopt AI into their work, based on Stanford research.

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Why do some people become enthusiastic, consistent adopters of AI, while others give it a try and shrug? We collaborated with Stanford University researchers to find out.

Over the last 18 months, we took the researchers behind the curtain at Google to observe how Googlers were learning and using AI in their day-to-day work. The timing of the study allowed us to observe firsthand how the rapid pace of AI was fundamentally challenging and changing how we build, collaborate and lead.

The published study found that while most people were eager to find value in AI tools, many were stuck in what the researchers called "simple substitution": swapping existing tasks for AI alternatives. But many found the effort it took to learn the AI tool and get to a good result was often greater than the payoff. Crucially, the researchers found that successful adopters didn’t just focus on prompt engineering or its more recent sibling, context engineering. Instead, deep AI adopters completely changed how they approached AI — taking inspiration from product management.

No matter their role, proficient users of AI unknowingly applied the product manager playbook; they identified high-value opportunities, understood what various AI tools can do and found a fit between the two. They took the time to rethink and redesign their workflow rather than look for quick solutions. Because generative AI is like a Swiss Army knife — a general-purpose technology packed with dozens of functions — the product manager mindset helps you decide which tool to pull out for the job.

What does that really look like? The Stanford study identified five strategies for anyone to more deeply adopt AI:

1. Start with what’s blocking your work. Don’t start with the technology, start with the work. Identify the hurdles that, if cleared, would allow you to move faster, think more creatively or analyze more deeply. Pinpointing these blockers shows you exactly where an AI solution could provide the most help.
2. Choose the right tool, beyond a chatbot. Once you’ve spotted an opportunity, explore the right AI tool for the job. There are many available, and many are better suited to solve your problem than only a chatbot. Evaluate which tool could sustainably work, even if it means adjusting your usual flow.
3. Start small and experiment rapidly. Don’t aim to completely redesign your workflow at first. Focus on prototyping, testing and refining your ideas. Starting small helps discover a solution that actually works and avoids frustration or costly scale ups.
4. Think holistically across systems. Successful adoption requires moving past isolated, one-off tasks and embedding AI into your broader, everyday processes. Often, the biggest upside comes from bridging across datasets, stitching an AI workflow that reduces multiple manual tasks, or elevating your strategic thinking by pulling together various expertise areas as inputs.
5. Share your playbook. The final step is to document your wins so others can skip the trial and error and adapt them to their own work. Packaging your findings into repeatable templates (you can use AI to do this!) saves the next person from starting at zero and allows the entire team to benefit from the compounding productivity.

Googlers are always tinkering and trying new things to change how we work for the better. With this product management mindset, we think anyone can more deeply adopt AI to do the same. Check out the full Stanford study for more on what the researchers learned and get inspired by more examples of Googlers using AI in their daily work.