Intelligence at Work: The Alert That Knows the Next Step

Intelligence at Work is a six-part series exploring how GE Vernova embeds decades of operational expertise into key decision workflows, evolving our software from a system of record into a system of intelligence.

The information presented is intended to highlight capabilities available today and provide an outline of general product direction, and it should not be relied on in making a purchasing decision. The information on the roadmap is for information purposes only and may not be incorporated into any contract and is not a commitment, promise, or legal obligation to deliver any material, code, or functionality. The development, release, and timing of any features or functionality described for our products remains at our sole discretion.

A high-priority alert fires on a boiler feed pump. A junior reliability engineer, just two years into his career, sees it in his queue. He knows something is developing, but he is not sure what to do about it. His mentor is on vacation for the next week. He has the prediction. What he needs is the prescription.

That gap between "something is wrong" and "here's what to check first" is where most plants lose time. And it is where the knowledge problem lives. The senior engineer who has seen this pattern before is unavailable. The technician who remembers what fixed it last time transferred to another site. The reliability lead who knows which manual section is relevant and which one is outdated retired in March. Expertise is scattered across people and not available when an alert requires someone to act.

SmartSignal® predictive maintenance software has been delivering the prediction side of this for years across a wide array of industries, from power generation and oil and gas to aviation and manufacturing. The platform monitors equipment from any manufacturer, making it OEM-agnostic by design. Machine learning digital twins learn what normal looks like for each individual asset, flag deviations early, and give operations teams lead time before failures develop. That capability is proven and deployed. But the alert itself always stopped short of one thing. It told you something was coming without telling you what to do about it.

Predictive analytics solved the detection problem. Then the question became what solves the response problem? What’s the prescription to address the concern?

The prescriptive layer is where generative AI will earn its place in the workflow. Not as a chatbot or a novelty. As the mechanism that connects a SmartSignal alert to the accumulated knowledge required to act on it. When an alert fires, the prescriptive layer will interpret the diagnostic context and generate specific, prioritized recommendations drawn from four sources:

Built over decades of diagnosing and resolving real failures on real equipment, GE Vernova's Industrial Managed Services (IMS) case library is an invaluable resource, along with a customer's own internal documentation and institutional knowledge. The system draws from both, depending on what is available and what has been configured. The GenAI employed here does not invent answers. It retrieves, synthesizes, and prioritizes from a knowledge base that already contains the expertise.

Proprietary prompts and guardrails ensure recommendations are consistent, specific, and traceable. Every suggestion links back to its source, whether that is a manufacturer's manual, a closed case from three years ago, or a documented best practice from a senior engineer who captured her reasoning before she left.

Back to the engineer and the alarming boiler feed pump. The engineer reviews the suggestions against what he knows about the asset and the operating context. Some recommendations apply. Others don't. He selects the actions that make sense for this specific situation and creates a formal case.

The system doesn't replace his judgment. It gives him a structured starting point so he's not beginning from zero. No phone call to his mentor on vacation. No waiting for someone with more experience to weigh in before he can move forward. The knowledge that once lived in one person's head is now surfaced as a reference for every engineer at every level, on every shift.

The system adapts. Because the knowledge base is built on specific failure modes, not generic troubleshooting, the recommendations change entirely based on the asset and the diagnostic pattern. A turbine inlet filter fouling alert on the same site generates a completely different set of actions. Environmental assessment, scheduled maintenance updates to increase filter inspection and cleaning frequency, differential pressure instrumentation calibration, and evaluation of other possible failure modes. Different asset, different failure modes, different guidance.

That is the difference between "digital" and "intelligent" operations. Asset-specific guidance eliminates irrelevant diagnostic steps and lets the engineer focus on the actions most likely to resolve the issue.

This is where the compounding advantage lives. When a case closes, the engineer provides feedback. Thumbs up or thumbs down on each recommendation. Notes on what resolved the issue and what did not apply.

That feedback flows back into the knowledge base. The next time a similar alert fires on a similar asset, the recommendations are sharper because they are informed by what worked last time. The case library grows with real outcomes from real resolutions. A plant running this for two years gets more contextual, more specific guidance than one that started last month, because its knowledge base has two years of closed cases teaching the system what matters.

The system runs on Amazon Bedrock with four layers of protection built into every interaction. Sensitive information filters, content filters, denied topic blocking, and contextual grounding checks that prevent hallucination. Cross-tenancy safeguards ensure one customer's data never appears in another's recommendations, and guardrails apply during both prompt creation and response generation.

The recommendations are auditable and traceable to source documents. They do not fabricate information. This is generative AI operating within boundaries, not generating freely.

SmartSignal is generally available today for predictive failure modeling and diagnostic alerts. Industrial Managed Services (IMS) is available today for prescriptive recommendations. Our product roadmap for the prescriptive layer within SmartSignal is currently in early customer validation, focusing on the following areas:

The goal is not to replace the experienced engineer. It is to ensure their knowledge is available to every team member, on every shift, at every plant, even long after they have moved on.

We have already explored how adaptive neural networks optimize combustion continuously. This post demonstrates what happens when machine learning prediction gains a generative AI layer built on those same decades of accumulated expertise.

Moving forward, the series will continue to map the strategic roadmap for our platform, including visual intelligence, automated turnaround planning, fleet-wide strategy, and conversational data access. Next, we look at what happens when AI closes the loop between inspection findings and defect elimination across an entire asset class.