This lunch and learn is an opportunity to share how similar teams are preparing.
Lunch & Learn:
AI Agents for Operations & Maintenance
AI is advancing at a rapid pace, however most tools on the market are meant for knowledge workers supporting back office operations. This session is meant for teams that are responsible for managing complex industrial assets whose decisions have daily impact on safety, reliability, and risk.
What Are AI Agents and Why Copilot Doesn't Cut It in Operations & Maintenence
Understanding the fundamental difference between chatbots, copilots, and true AI agents can be confusing. While chatbots respond to simple queries and copilots assist within single applications, AI agents autonomously reason across multiple systems to solve complex operational challenges.
Reasoning Engine
Breaks complex operational questions into sub-tasks and develops multi-step solution pathways
Tool Integration
Queries enterprise systems, reads technical manuals, runs calculations across platforms
Cross-System Data Access
Simultaneously searches CMMS, historians, APM platforms, and technical documents
Contextual Memory
Understands your specific equipment history and operational patterns, not generic knowledge
The Context Layer: What Makes Agents Actually Work
Raw data without context produces generic answers that don't help operations teams. The context layer transforms disconnected data into actionable intelligence by understanding how your assets, systems, and operational knowledge interconnect.
What a Context Layer Actually Is
Data Connectors
Plugs directly into existing CMMS, APM platforms, and historians without ripping out and replacing your current systems
Industrial Ontology
Understanding of how assets, work orders, and maintenance records relate to each other
Semantic Mapping
Translates between how systems store data and how engineers actually think about problems
Source-Backed Reasoning
Every answer includes full traceability back to the original data sources for verification
Context Layer vs. Other Approaches
Knowledge Graphs
Powerful but rigid architecture requiring months to build and ongoing maintenance by specialized data engineers
Data Lakes
Centralizes information but doesn't add the contextual layer needed to make data actionable for operations
Enterprise Search
Finds relevant documents but can't reason across multiple systems or synthesize answers from different sources
Real Example: A Fortune 500 Utility
The Challenge
A major utility serving 2.4 million customers needed to identify at-risk transformers across thousands of grid assets. Critical dissolved gas analysis (DGA) data was buried across multiple disconnected systems with no way to query the entire fleet simultaneously.
The Question
"Show me transformers with acetylene spikes across my fleet for the last 2 years."
How the Context Layer Processed This Query
The AI agent simultaneously searched historian data, maintenance records, and technical specifications to identify patterns that existing monitoring tools had missed for months.
At-Risk Transformers Found
Critical assets that existing tools missed for months, now flagged for immediate attention
Faster Than Manual
What took engineers days of cross-referencing systems now completed in minutes
Time to First Insight
Actionable results delivered immediately after system connection, no training period required
Key Takeaways + Questions
AI Agents ≠Chatbots
Operations and maintenance needs purpose-built tools that reason across systems, not generic assistants designed for office work
Context is Everything
Without a context layer, AI gives generic answers. With it, AI delivers YOUR answers based on YOUR systems and YOUR operational history
Are you ready for agentic operations?