Integrating AI P&ID Extraction with Asset Management Systems

Engineers spend the majority of their time searching for information within technical documents such as P&IDs. This inefficiency is now being solved with artificial intelligence.

What Is AI-Based P&ID Extraction?

Piping and Instrumentation Diagrams (P&IDs) are technical blueprints that detail the relationships between equipment, piping, valves, and instrumentation in industrial facilities. Traditionally locked in static formats such as PDFs or scanned drawings, these documents have been a barrier to true digitalization.

AI-based P&ID extraction uses computer vision, natural language processing (NLP), and deep learning to convert these documents into data that is structured and readable by machines. Once extracted, this information becomes interoperable with CMMS platforms and EAM solutions such as IFS and HxGN EAM.

Why It Matters for Asset Management

Manual transcription of P&ID data into asset management systems is time-consuming and error-prone. AI eliminates these inefficiencies, which ultimately enables quicker migrations to digital twins and better decision-making through accurate system-of-record integration.

By automating the extraction of equipment tags, loop diagrams, and process flow, AI bridges the gap between engineering design and maintenance execution. This supports critical functions such as FLOC creation, PM planning, and asset hierarchy management in tools including Oracle EAM or SAP PM.

Tech Stack Behind AI P&ID Extraction

The AI workflow typically includes:

• Optical Character Recognition (OCR) to read and digitize text from scanned images
• Symbol Recognition using trained models that identify common engineering symbols across standards
• Relationship Mapping to connect equipment, instruments, and their functions across the diagram

Modern AI platforms also integrate with EAM systems, which ensures extracted data flows directly into asset registries. This enables accurate PM triggers and enhances compliance with ISO 14224.

The Road Ahead: Smarter Plants

As more facilities adopt digital twins, the role of AI in transforming static P&IDs into living models will expand. After being combined with real-time sensor data, the extracted diagrams become dynamic, informing predictive maintenance and operational decision-making.

In fact, the global market for AI in industrial applications is expected to reach $1.8 billion by 2032, which underscores the urgency for organizations to modernize how they handle engineering documentation.

Final Thoughts

AI-powered P&ID extraction is no longer just an innovation, it’s becoming industry standard. Whether you are looking to migrate to a modern EAM system or enable predictive maintenance, integrating AI into your asset data workflows is a strategic step. By unlocking siloed P&ID data, companies can enhance asset visibility and build the foundation for intelligent operations.