The most critical skill in the AI era is no longer narrow specialization but versatile business acumen. As AI handles specialized tasks, human value shifts to orchestrating multiple AI agents across functions. This requires a holistic understanding of the entire business 'symphony' to guide the agents effectively.
Previously, leaders carefully weighed the ROI of pursuing new features. With AI, building and testing ideas is so rapid that the strategic focus must shift. The greater risk is not a failed experiment, but failing to experiment at all. Organizations should measure the opportunity cost of not embracing AI-driven speed.
It's a mistake to make 'using AI' the strategy itself. Fundamental business drivers like customer lifetime value (LTV), retention, and engagement remain unchanged. AI is a powerful new method for influencing these timeless metrics, but it is not a replacement for a sound business strategy focused on customer value.
Many leaders test AI with simple, surface-level experiments. But modern AI is so advanced that these small tests create a false sense of understanding. According to Braze CPO Kevin Wang, genuine value is only revealed when AI is applied to complex, multi-team business problems and real-world workloads.
The initial rush to adopt AI resulted in superficial features like text rephrasing tools. That era is over. The next, more valuable phase of AI product development requires creatively embedding AI's reasoning capabilities into core product workflows, moving beyond simple generative tasks to create genuine, contextual automation.
Since AI capabilities are novel, users often struggle with adoption. Rather than using traditional templates or tutorials, a more effective method is to build an AI agent or operator that guides users through the process. This approach uses the AI to teach the user how to leverage AI's potential within the product's specific context.