AI-Driven Work Order and Asset Management Systems in Facility Operations Using Natural Language Processing

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The AI-driven system improves work order processing time by 45%, enhancing operational efficiency.
Evaluation of over 10,000 maintenance logs revealed a remarkable 91% classification accuracy of the NLP model.
This framework employs a fine-tuned BERT-based model to process maintenance requests and track asset lifecycles.
The findings suggest that integrating AI in management systems can transform facility operations and promote data-driven maintenance.

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This analysis demonstrates that AI and natural language processing improve maintenance request handling and asset efficiency.

Musa et al. (2025) studied this question.

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