Integrating AI into your decisionmaking processes is an invaluable way to reduce downtime across maintenance operations. For example, maintenance personnel can use AI-powered analytics to determine why the system is suggesting reducing the order for certain parts or why a machine not scheduled for maintenance needs repair within the next week.
However, even when the data shows what is the best actionable step to take next, decision makers might still be hesitant to act on insights that were put together by an algorithm. When it comes to strategic and proactive maintenance, speed is of the essence.
XAI is essential to decision intelligence. It uses specialized models and algorithms to provide clear explanations and reasoning for how it came to a certain conclusion. This enables maintenance teams to build trust in AI recommendations and speed up analytical and interpretation practices so that companies can get to more accurate answers faster.
When maintenance analysts can trust their AI to help them make the right proactive decisions, they’re able to optimize pre-existing maintenance activities while also anticipating future maintenance needs in the most efficient ways possible.
