Large retailers are moving toward having effectively the same massive product catalogs via marketplaces. As selection becomes commoditized and ceases to be a differentiator, retailers will be forced to compete on the next level: deeply personalized service and unique customer experiences.
When a leader at Google was denied engineers for a $3B product, the constraint became a gift. It forced ruthless prioritization and required building such strong internal excitement for the project that it naturally attracted talent over time, rather than just being assigned it.
In businesses with tight 5-8% margins, like retail, AI-driven efficiencies in areas like customer support aren't just incremental. They become extraordinarily powerful levers for profitability and scaling, fundamentally altering the cost structure of the business.
The proliferation of AI development tools points to a future of billions of hyper-specialized applications. This could end the concept of a single, consistent user experience, creating a reality where every digital product is uniquely customized for each individual user.
For large, traditional companies, the most critical first step in AI adoption isn't building tools, but fostering deep understanding. Provide teams sandboxed access to AI models and company data, allowing them to build intuition about capabilities before crafting strategy.
To convince executives at traditional companies of AI's potential, abstract presentations fail. Instead, provide tangible, immersive experiences. A ride in a Waymo car, for instance, serves as a powerful product demo that makes the future feel concrete and inevitable, opening minds in a way slideshows cannot.
Frame moonshot projects like Google's Waymo not as singular bets, but as platforms for innovation. Even if the primary goal fails, the project should be structured to spin off valuable 'side effects'—advances in component technologies like AI, mapping, or hardware that benefit the core business.