Inspection
Work Planning
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The application of computational techniques that enable computer systems to perform tasks requiring human intelligence and to improve performance through data-driven learning.
Artificial Intelligence (AI) is the broader field concerned with developing systems capable of performing tasks that normally require human intelligence, including reasoning, pattern recognition, language understanding and decision support. Machine Learning (ML) is a subset of AI that enables systems to learn from historical data and improve predictions or decisions without being explicitly programmed for every scenario.
In industrial environments, AI and ML enable predictive maintenance, anomaly detection, intelligent document processing, digital engineering automation and advanced decision support. Properly applied, these technologies improve productivity, asset reliability and engineering efficiency while allowing experts to focus on higher-value activities.
AI is the umbrella discipline that includes Machine Learning, Deep Learning and Generative AI. Within industrial asset management it increasingly complements Digital Twins, Asset Information Management and predictive analytics rather than replacing engineering expertise.
AI should be viewed as a decision-support technology rather than a replacement for engineering judgement. Engineering, safety and regulatory decisions continue to require appropriate human oversight.
Predictive maintenance; Asset Performance Management; Inspection planning; Computer vision; Document digitization; Digital twins; Engineering knowledge management; Industrial analytics.
Using ML to predict equipment failures.; Automatically extracting asset tags from engineering drawings.; Detecting process anomalies from historian data.; AI-assisted engineering document classification.; Large Language Models supporting engineering knowledge retrieval.
ISO/IEC 22989; ISO/IEC 23053
VisualAIM connects glossary concepts to the asset records, inspection histories, and workflows they describe.
