AI is powerful—but without structured processes and reliable data, it cannot deliver meaningful results.
In this episode, Adi Klevit interviews Bryan DeBois about how artificial intelligence is transforming manufacturing and industrial systems. Bryan explains why the excitement around generative AI often overlooks the unique realities of plant-floor operations, where errors can have serious consequences. In high-stakes environments like manufacturing, organizations must carefully choose the right type of AI for the job.
Adi and Bryan discuss the difference between generative AI, predictive models, and autonomous AI systems. Bryan shares how manufacturers can use predictive analytics to forecast product quality, optimize set points, and improve operational performance. These models are designed to solve operational problems rather than simply generate information.
The conversation also highlights a critical systems principle: AI only works when businesses have already documented their processes and established strong data collection practices. Bryan explains how companies capture expert knowledge through a process called "machine teaching," allowing AI systems to learn from experienced operators and apply that expertise to improve future performance.
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