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A large paint company documents four ways to process a purchase order. In reality there are 11,973 different approaches. That gap between the documented process and the real one is the subject of this episode, and it explains why AI adoption is near universal while the share of companies reporting any profit impact has been flat at 37 percent for two years.

We look at where the difficulty actually sits. The process lives in workarounds, spreadsheets and people's heads, and is different at every site. The data is spread across roughly 900 applications of which a quarter are connected. Legacy systems were never designed to be called by a machine. And the law has started to way in: Air Canada was held liable for what its chatbot said, Workday can be sued as an employer's agent, Australian companies must disclose automated decisions from December 2026, and Commonwealth Bank reversed AI-attributed redundancies after call volumes went up rather than down.

We then cover what AI adds to the old problem. Language models are non-deterministic and cannot be replayed for audit. The best agents complete about 30 percent of realistic office tasks. Human oversight runs into limits Lisanne Bainbridge described in 1983, now measured in the field. And people feel faster while being measurably slower.

Finally, what works. The strongest predictor of financial return is not the model but whether the workflow was redesigned, which is the same lesson factories took forty years to learn from electrification. We walk through the method: mine the process, standardise the core, exclude the tail explicitly, run in shadow mode against a baseline, measure at the process level, and tier governance by consequence.

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