The PM playbook is broken for AI. Here’s how to fix it.

In the evolving landscape of product management, AI-driven product development has shifted focus from traditional, deterministic processes to more adaptive, context-aware methodologies. AI tools offer enhanced capabilities for rapid prototyping and idea validation, yet they challenge existing frameworks like A/B testing and roadmapping, which were designed for predictable product behavior. Product managers (PMs) are now tasked with rethinking these processes to accommodate AI's probabilistic nature, which requires new approaches to prototyping, evaluation, and user experience. This includes leveraging tools like Replit for initial concept testing and adapting roadmaps for continuous model iteration. As AI products demand more personalized user interactions, PMs must focus on understanding these unique user journeys and balancing innovation with risk management. The article emphasizes the importance of asking critical questions throughout the product development stages to harness AI's potential effectively while acknowledging the inherent unpredictability and risks involved.