Tools Create Capacity, Workflows Create Value (robertgreiner.com)

Organizations repeatedly report the same paradox: AI and automation tools create large amounts of capacity—engineers feel 40% faster with coding assistants, labs triple pipetting throughput, analysts reclaim hours—but delivery dates, deal flow, and project timelines barely budge. Historical and theoretical lessons explain why: Paul David’s electrification “productivity paradox,” Toyota’s emphasis on standardized workflows rather than robots, and Fred Brooks’ coordination limits show that speeding a single step doesn’t speed the whole system. Work flows at the pace of the slowest constraint, so unchanneled slack converts into higher standards, more analysis, or broader scope instead of faster outcomes. For AI/ML teams this means the race isn’t just for better models but for better workflow integration. Value appears only when organizations explicitly choose how to use new capacity—speed, quality, cost, or scope—and then redesign processes, incentives, and metrics to enforce that choice. Three forces block this: incentive inertia (old metrics drive old behavior), hidden coordination costs (handoffs and reviews dominate), and workflow lock‑in (tacit routines resist change). Actionable fixes: find the real bottleneck, redesign end‑to‑end workflows to assume new capacity, and align KPIs to the intended outcome. Most orgs already sit on 30–40% latent capacity; the tool is potential energy—workflows make it kinetic.