Time-and-Motion Study of AI-Augmented Work

The time-and-motion study is Taylor's signature method: observe the worker, time each motion, classify productive from wasteful, eliminate the wasteful, redesign for efficiency. The method assumes work is observable, its components are measurable, the measurable components separate into value-producing and waste, and elimination of waste is always a gain. These assumptions held for physical labor, held with some strain for routine knowledge work, and fail catastrophically for AI-augmented knowledge work. Imagine conducting a time-and-motion study of a builder working with Claude: the pauses become waste, the lateral questions become workflow errors, the deletions become rework — the most expensive waste class — and the metrics record the inefficiency of the only moments that matter. The method captures the commodity (execution, which the machine handles) and misses the resource (judgment, which the human provides). It optimizes the cheap part and neglects the expensive part.