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Operationalizing AI and machine learning in mining holds immense potential for the industry with emerging use cases promising to optimize process, increase automation, and drive up operational efficiency.

AI and machine learning are already driving advancements in areas like asset health monitoring. As models continue to get smarter, they will be able to deliver even earlier warnings about changes to asset and process health. Thus, organizations will not only be able to prevent failure but also prevent asset degradation and extend operating life.

Generative AI could support the data modeling and analysis underpinning asset health monitoring solutions and therefore speed up deployment and time to value.

Process optimization is another immediate area where mining organizations can extract value from AI and machine learning. For example, leveraging a combination of data-driven process modeling, AI, and advanced optimization techniques can continuously improve production rate, yield, energy efficiency, and overall operating margins for a wide variety of industrial processes.

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