To successfully automate complex workflows with AI, product teams must go beyond traditional discovery. A "forward-deployed PM" works on-site with customers, directly observing workflows and tweaking AI parameters like context windows and embeddings in real-time to achieve flawless automation.

Many teams wrongly focus on the latest models and frameworks. True improvement comes from classic product development: talking to users, preparing better data, optimizing workflows, and writing better prompts.

Product managers should leverage AI to get 80% of the way on tasks like competitive analysis, but must apply their own intellect for the final 20%. Fully abdicating responsibility to AI can lead to factual errors and hallucinations that, if used to build a product, result in costly rework and strategic missteps.

High productivity isn't about using AI for everything. It's a disciplined workflow: breaking a task into sub-problems, using an LLM for high-leverage parts like scaffolding and tests, and reserving human focus for the core implementation. This avoids the sunk cost of forcing AI on unsuitable tasks.

The process of guiding an AI agent to a successful outcome mirrors traditional management. The key skills are not just technical, but involve specifying clear goals, providing context, breaking down tasks, and giving constructive feedback. Effective AI users must think like effective managers.

It's a common misconception that advancing AI reduces the need for human input. In reality, the probabilistic nature of AI demands increased human interaction and tighter collaboration among product, design, and engineering teams to align goals and navigate uncertainty.

Don't view AI tools as just software; treat them like junior team members. Apply management principles: 'hire' the right model for the job (People), define how it should work through structured prompts (Process), and give it a clear, narrow goal (Purpose). This mental model maximizes their effectiveness.

Beyond just using AI tools, the fundamental process of product management is evolving. For every new initiative, PMs must now consider the appropriate level of AI, automation, or customization. This question is now as critical as "what problem are we solving?" and addresses rising customer expectations for adaptive products.

The most effective application of AI isn't a visible chatbot feature. It's an invisible layer that intelligently removes friction from existing user workflows. Instead of creating new work for users (like prompt engineering), AI should simplify experiences, like automatically surfacing a 'pay bill' link without the user ever consciously 'using AI.'