The Humans Behind The Automation

Earlier this week, we talked about inference getting cheaper. Today is the other half of the story: AI may be getting cheaper to run, but it is not getting simpler to install inside a real company. OpenAI and Anthropic are both moving deeper into enterprise AI services. The strategic lesson is not the deal structure. It is the admission: the hard part is no longer only the model. The hard part is understanding how work actually happens inside companies. In this episode, Stephen Forte explains why the best AI deployments start with workflow archaeology: interviewing the people doing the work, mapping repeated task patterns across teams, finding where humans act as middleware between machines, and building agents around shared work instead of individual job titles. Key takeaways: Do not start with, “What agent should we build?” Start with, “What work is actually happening?” The unit of analysis is not the employee. It is the task pattern. Many companies have seven people doing the same 20 percent of work in different departments. Measure agents by output: transactions handled, files normalized, exceptions routed, cycle time reduced, and human review required. AI adoption is a migration, not a rip-and-replace transformation. The future is not one bot per employee. It is a new operating system for the business, assembled from the real work people already do.