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While the steel industry may not be synonymous with AI adoption, steel manufacturers are not different from any other business in ensuring efficiency in their output and throughput. There are almost certainly opportunities for AI implementation across an industry worth hundreds of billions of dollars that currently lags behind in adopting AI technology.

Almost no other sector is traditionally slower to technological adoption than manufacturing, in no small part due to the countless challenges of relying on data accrued from physical environments. Yet across industries, manufacturing business leaders are finding that data is finally “waking up” to the nuances and fundamentals of their business operations.

This article has been sponsored by SambaNova Systems and was written, edited, and published in alignment with Emerj’s sponsored content guidelines.

The hypothesis is simple:

Equipment breakdowns or downtime is extremely expensive (imagine a train broken down on isolated tracks, hundreds of miles from the nearest depot)
Heavy equipment (engines, wind turbines, manufacturing machines) produce various streams of data (heat, vibration, time-series, etc)
Machine learning could be used to detect "failure patterns" in that data, helping businesses to maintain equipment health more effectively

Insurers are looking to leverage all of the digital customer data that is now available to them, including one new data source that some of the largest insurance enterprises claim are actively collecting: real-time data streams from the Internet of Things (IoT).