In large enterprises with legacy systems, AI-generated "vibe code" is not ready for direct production deployment. Treat it as a "first draft" for exploration and testing. A successful transition to production requires implementing stage gates and checks and balances, rather than a direct, one-step process from the AI tool.
As AI tools automate coding and prototyping, the product manager's core function is no longer detailed specification writing. Instead, their value multiplies in judging, facilitating, and making the right strategic decisions quickly. The emphasis moves from the 'how' of building to the 'what' and 'why,' making decision-making the critical skill.
To get meaningful competitive analysis from an AI, first provide your business and product strategy. Then, have the AI define the competitive set. Only after you agree with the landscape should you define specific comparison criteria. This iterative, context-first approach yields much better results than asking for a feature comparison directly.
Instead of monitoring private AI chats to ensure best practices, leaders should focus on providing the right inputs. Create centralized, AI-ready artifacts like customer research, business strategy, and outcome documents. This ensures teams connect their AI-accelerated work to the correct context, allowing leaders to monitor outcomes, not activity.
To get approval for an alternative to a corporate-mandated AI tool like Microsoft Co-pilot, build a business case based on efficiency. Demonstrate with side-by-side output comparisons how the preferred tool yields better results faster. Frame the default tool not just as inferior, but as an impediment that makes your team slower.