To learn AI agent development, avoid large, complex projects. Instead, build a small personal agent (e.g., a daily briefing tool) to master the core, transferable skills of context, retrieval, tool use, and permissions—the true foundation of valuable corporate AI systems.

The key for go-to-market leaders to stay relevant is hands-on experience with AI. Instead of delegating, leaders should personally select an AI tool, ingest data, and go through the iterative training process. This firsthand knowledge is a rare and highly valuable skill.

Product leaders must personally engage with AI development. Direct experience reveals unique, non-human failure modes. Unlike a human developer who learns from mistakes, an AI can cheerfully and repeatedly make the same error—a critical insight for managing AI projects and team workflow.

Simply buying an AI tool is insufficient for understanding its potential or deriving value. Leaders feeling behind in AI must actively participate in the deployment process—training the model, handling errors, and iterating daily. Passive ownership and delegation yield zero learning.

Instead of passively learning about AI, executives should actively deploy a simple agentic product. This hands-on experience of training and QA provides far more valuable, practical knowledge than any course or subscription, putting you ahead of 90% of peers.

While senior leaders are trained to delegate execution, AI is an exception. Direct, hands-on use is non-negotiable for leadership. It demystifies the technology, reveals its counterintuitive flaws, and builds the empathy required to understand team challenges. Leaders who remain hands-off will be unable to guide strategy effectively.

Passively reading consultant decks is insufficient for grasping AI's potential. True understanding comes from active experimentation. Firms and their portfolio companies should "get their hands dirty" by building their own AI agents and co-pilots to discover the art of the possible and apply it directly to their own operations.

Leading an AI transformation requires more than just delegation. Leaders must personally engage by building their own compounding AI 'stack'—a collection of skills, context files, and workflows. This hands-on experience is essential for developing intuition, understanding the technology's potential, and leading from the front.

The PM role will expand beyond leveraging off-the-shelf AI. They will be responsible for creating and training specialized AI agents. This involves instilling agents with deep, company-specific knowledge of business models, customers, and strategy, just as they would onboard a new human team member.