For years, the customer journey started with a Google search. That paradigm is now shifting as users begin their discovery process by having conversations with LLMs. This fundamentally changes product design and go-to-market strategy, as the primary interface is no longer a company's website.
Leaders often misunderstand AI's probabilistic nature, thinking it's a flaw that will be "fixed." Drawing parallels to chaos theory, the slight non-determinism is an intentional feature that enables creativity and requires building systems with guardrails and human oversight, not seeking perfect predictability.
In previous tech waves, proprietary technology was a key differentiator. Now, with powerful AI models widely available, the advantage shifts to deeply understanding customer problems. The question "Should we even build this?" is more critical to creating a moat than the technology itself.
While the current AI era shares similarities with the birth of the internet, the key difference is the sheer velocity of change. During the dot-com era, companies had more time to adapt. Today, the acceleration is so intense that companies that wait on the sidelines risk becoming obsolete.
Many ideas from the dot-com bust, like Webvan (online grocery delivery), failed not because the concept was wrong, but because the supporting technology was immature. Today, these same concepts, like Instacart, are thriving as the necessary infrastructure is now in place.
Faced with an "AI mandate," many companies try to force-fit AI onto their current offerings, leading to failure. The correct first step is a fundamental assessment: is this problem even a good candidate for AI, or does the entire product need to be reimagined from the ground up?
True AI leadership requires moving beyond superficial use, like treating LLMs as a better Google. To avoid being left behind, leaders must get their hands dirty with the underlying technology. This deeper understanding is what enables them to identify real business opportunities and drive meaningful adoption.
Executives without technical understanding may make impossible requests, like asking why a database can't function like Excel. A product leader who has "gotten their hands dirty" can act as a credible "wall," translating technical complexities and protecting their team's focus and morale.