Why do some people become enthusiastic, consistent users of artificial intelligence, while others give it a chance and shrug it off? We collaborated with researchers from Stanford University to find out.
Over the past 18 months, we took the researchers behind the scenes at Google to observe how Googlers learned and used AI in their daily 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 with what the researchers called “simple substitution”: swapping out existing tasks with AI alternatives. But many found that the effort it took to learn the AI ​​tool and achieve a good result often outweighed the payoff. Crucially, the researchers found that successful adopters did not focus solely on prompt engineering or its newer sibling, context engineering. Instead, the deep AI adopters completely changed how they approached AI – with inspiration from product management.
Regardless of their role, skilled AI users unconsciously applied the product manager’s playbook; they identified high-value opportunities, understood what different AI tools can do, and found a fit between the two. They took the time to rethink and redesign their workflow rather than looking for quick fixes. Because generative AI is like a Swiss army knife—a general-purpose technology packed with dozens of features—the product manager’s mindset helps you decide which tool to pull out for the job.
What does it actually look like? The Stanford study identified five strategies anyone can use for deeper adoption of AI:
- Start with what is blocking your work. Don’t start with the technology, start with the work. Identify the obstacles that, if removed, would allow you to move faster, think more creatively, or analyze more deeply. By locating these blockers, you show exactly where an AI solution can provide the most help.
- Choose the right tool beyond a chatbot. Once you spot an opportunity, explore the right AI tool for the job. There are many available and many are better suited to solving your problem than just one chatbot. Consider which tool can work sustainably, even if it means adjusting your usual flow.
- Start small and experiment quickly. Don’t aim to completely redesign your workflow at first. Focus on prototyping, testing and refining your ideas. Starting small helps to find a solution that actually works and avoids frustration or expensive scale-ups.
- Think holistically across systems. Successful adoption requires you to move beyond isolated, one-off tasks and embed AI into your broader everyday processes. Often, the greatest benefit comes from bridging data sets, stitching together an AI workflow that reduces multiple manual tasks, or elevating your strategic thinking by bringing together different areas of expertise as inputs.
- Share your playbook. The final step is to document your gains so others can skip the trial and error and adapt them to their own work. Wrapping your results into repeatable templates (you can use AI to do this!) saves the next person from starting from scratch and allows the whole team to benefit from compound productivity.
Googlers are always tinkering and trying new things to change how we work for the better. With this mindset for product management, we believe everyone can take AI deeper to do the same. See the full Stanford study for more on what the researchers learned and be inspired by more examples of Googlers using AI in their daily work.