Manufacturers operate some of the most complex machinery on the planet – from CNC machines and industrial robots to gas turbines with over 20,000 components.
Keeping these assets running smoothly is mission-critical, yet maintenance teams are often buried under vague alerts, scattered documentation and time-consuming root cause analysis.
Much of this challenge comes down to context. More than 80% of enterprise data is unstructured and even when organizations adopt retrieval-augmented generation (RAG), they often discover that retrieval alone doesn’t fully solve the problem. RAG can surface relevant information, but it does not interpret it. For predictive maintenance to be truly effective, retrieval needs a GenAI layer that can turn information into understanding.
This is the role that the SAS Retrieval Agent Manager (RAM) is designed to play: serving as the GenAI layer that transforms RAG from an advanced search capability into an operational decision-support system for maintenance.
Why does predictive maintenance need GenAI?
Machine learning has already revolutionized predictive maintenance by detecting anomalies in real time. Anomaly detection models can flag issues earlier and more reliably than traditional threshold-based systems. But detection alone doesn’t close the loop.
An alert without context forces technicians into manual investigation mode: searching documentation, comparing past incidents and piecing together fragmented clues. Fixing this means greater reliability, reduced downtime, improved sustainability and optimized life cycle costs for machinery and assets.
RAG helps by grounding AI in enterprise-trusted knowledge. But RAG by itself still behaves like a sophisticated retrieval system. It answers what information exists. What maintenance teams need is help interpreting that information right now.
That’s where a GenAI layer becomes essential.
IDC recommends considering technology partners capable of transferring internal data into large language models (LLMs) to maintain security and avoid loss when creating a private, secure GenAI environment. SAS can help you do just that to help get the most out of predictive maintenance.
SAS RAM is designed to provide that layer by combining:
- RAG to retrieve trusted, relevant content.
- LLMs to interpret and synthesize that content.
- Agent-based orchestration to turn insight into action.
It doesn’t replace your existing ML systems; it supercharges them but turning detection into understanding.
From alert to resolution: adding reasoning to retrieval
Consider a typical scenario:
- Anomaly detected: ML flags a combustion instability in the chamber.
- Context retrieved: RAG surfaces manuals, logs and service bulletins related to that condition.
- GenAI applied: A reasoning layer interprets what those sources collectively suggest.
- Technician empowered: The agent presents probable causes, recommended actions and source citations in a user-friendly format.
This is the shift SAS RAM is built around. Instead of handing technicians a stack of documents, it provides structured, contextual guidance grounded in enterprise knowledge. Retrieval becomes reasoning. Alerts become explanations.
How do you scale agentic AI in manufacturing?
The architecture naturally follows a progression:
- Start with machine learning: Most manufacturers already have anomaly detection in place.
- Add RAG: Integrate unstructured data – PDFs, manuals, notes – into a searchable knowledge system.
- Add GenAI: Turn retrieved content into contextual understanding.
- Deploy agents: Operationalize reasoning and response.
- Scale: Reuse agents across machines, sites and teams.
SAS RAM is designed around this pattern, making it possible to scale context-aware, agent-driven AI across the enterprise.
What are some real-world impacts?
Our research shows SAS RAM delivers:
- 50% reduction in research time
- 40% increase in content accuracy
- 35% boost in customer satisfaction
- Faster root cause analysis
- Reduced downtime
- Improved technician productivity
- Greater confidence in maintenance decisions
For maintaining your critical equipment and processes, SAS RAM helps you move from reactive to proactive – turning maintenance into a strategic advantage.
