Lessons for AI from the manufacturing supply chain

The article argues that successful AI adoption should be managed like a manufacturing supply chain: start with a clearly valuable and feasible use case, ensure the “raw material” data is high quality and traceable, and use disciplined, repeatable processes to build and operate models. For CIOs and technology leaders, the message is that AI value will depend less on experimentation alone and more on strong governance, measurable business outcomes, and operational controls that reduce risk, bias, drift, and integration failures. IT organizations should treat AI delivery as an end-to-end production system, with versioned data, automated pipelines, testing, and monitoring built in from the start.