In real-world industrial operations, performance can shift for many reasons – equipment wear, process changes, environmental factors, or just the way different components interact over time.

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Engineers often identify these shifts only after they have already affected reliability or performance. Anticipating and preparing for every scenario is difficult, especially when systems are dynamic and interconnected.

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AI has emerged as a powerful tool to detect patterns, forecast outcomes, and support operational decisions but only when it is applied in a form that engineers can trust.