StewAI Blog · The Economics of AI · 16 min read
The Assembly Line for Thought
Industrial automation did not succeed by giving craftsmen a better tool. It redesigned the work. AI is stuck for the same reason manufacturing was: chat augments a task, agents improvise a craftsman, and neither builds an assembly line. Recipes do.
Frequently asked questions
Why do most enterprise AI pilots show no P&L impact?
MIT's Project NANDA found roughly 95 percent of enterprise generative-AI pilots produced no measurable P&L return, not because models fail at tasks but because the work around the tasks was never redesigned. McKinsey found AI high performers nearly three times as likely to have fundamentally redesigned workflows.
What is wrong with agentic AI as an architecture?
An agent defines its workflow ad hoc on every run, so the workflow is not inspectable before execution, not reusable, not composable, and not statistically controllable. It replaces one craftsman with another instead of building an assembly line.
How do you trust AI making judgment calls at scale?
The way manufacturing solved it a century ago: statistical process control. Typed outputs at every step, sampled scoring against ground truth, control limits, and automated escalation on drift. That only works when the process is structured; a chat transcript offers nothing to measure.
Read the full article on StewAI