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.

As tools like Zapier become more powerful, clerical workers will increasingly take on tasks resembling basic scripting or macro creation. This shifts their skillset toward technical problem-solving, blurring the line between administrative work and development and creating a new class of worker.

Instead of selling software to traditional industries, a more defensible approach is to build vertically integrated companies. This involves acquiring or starting a business in a non-sexy industry (e.g., a law firm, hospital) and rebuilding its entire operational stack with AI at its core, something a pure software vendor cannot do.

Most companies are not Vanguard tech firms. Rather than pursuing speculative, high-failure-rate AI projects, small and medium-sized businesses will see a faster and more reliable ROI by using existing AI tools to automate tedious, routine internal processes.

The common fear of AI eliminating jobs is misguided. In practice, AI automates specific, often administrative, tasks within a role. This allows human workers to offload minutiae and focus on uniquely human skills like relationship building and strategic thinking, ultimately increasing their leverage and value.

Industry leaders from LinkedIn and Salesforce predict that AI will automate narrow, specialized tasks, fundamentally reshaping careers. The future workforce will favor 'professional generalists' who can move fluidly between projects and roles, replacing rigid departmental structures with dynamic 'work charts.'

Product management "range" is developed not by learning domain-specific facts, but by recognizing universal human behaviors that transcend industries—the desire for simplicity, convenience, or saving time. Working across different verticals hones this pattern-matching skill, which is more valuable than deep expertise in a world of accessible information.

Instead of focusing on foundational models, software engineers should target the creation of AI "agents." These are automated workflows designed to handle specific, repetitive business chores within departments like customer support, sales, or HR. This is where companies see immediate value and are willing to invest.

When transitioning to a new industry, your lack of domain knowledge is secondary. Focus on your "superpower": the proven, repeatable process you use to deliver results. Articulate your ability to launch, rally teams, and solve problems, as these core skills are universally valuable.