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.

The most significant productivity gains come from applying AI to every stage of development, including research, planning, product marketing, and status updates. Limiting AI to just code generation misses the larger opportunity to automate the entire engineering process.

Enterprises struggle to get value from AI due to a lack of iterative, data-science expertise. The winning model for AI companies isn't just selling APIs, but embedding "forward deployment" teams of engineers and scientists to co-create solutions, closing the gap between prototype and production value.

Beyond booking meetings for high-value deals, AI agents can be empowered to handle the full sales cycle for lower-priced products. They can answer questions, provide discount codes, and conduct follow-up, creating a significant, automated revenue stream with no human sales involvement.

Companies are replacing traditional, siloed sales assembly lines with a centralized "GTM Engineer." This technical role uses AI and automation tools to build revenue systems, absorbing the manual research and prospecting work previously done by individual reps. This allows for rapid learning and scaling of creative ideas across the entire team.

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.

Instead of traditional IT roles focused on software, an AI Ops person focuses on identifying and automating workflows. They work with teams to eliminate busy work and return hundreds of hours, shifting employees from performing tasks to directing AI.

Flexport is upskilling its non-technical staff through a 90-day "AI boot camp." By giving domain experts one day a week to learn low-code AI tools, the company empowers them to automate their own repetitive tasks, turning them into "lightweight engineers" who are closest to the problems.

This emerging role applies engineering and AI to GTM functions, building agents to automate tasks like lead qualification and personalized outreach. This dramatically increases efficiency, allowing one person, with an AI agent, to do the work of ten.