Intelligence at Work: When AI Connects What Your Organization Already Knows

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 remain at our sole discretion.

There is a moment in every plant's life when the last person who truly understood a system walks out the door. Sometimes it is a retirement. Sometimes it is a transfer. Sometimes it is a Tuesday, and nobody notices until three months later when something goes wrong and the person who would have caught it is gone.

This is not a workforce problem. It is a knowledge problem.

GE Vernova’s software and digital transformation solutions support a broad range of industrial operations. In power generation, operators manage increasingly complex thermal, renewable, nuclear, and grid and transmission assets where reliability, efficiency, and emissions performance must be balanced continuously. In downstream, midstream, and upstream oil and gas, mining, and other energy-intensive industries, teams face many of the same challenges: maintaining complex equipment, identifying risk early, and preventing costly failures in high-consequence environments. And across other high-risk industries, including aviation and transportation, operational reliability and performance are equally critical.

Across these sectors, the pattern repeats. Critical operational knowledge lives in experienced people. The engineer who recognizes drift before the alarm fires. The inspector who spots a failure pattern before it becomes an outage. The planner who knows where to look first when time is short.

The question facing every operator today is whether that expertise leaves with them or stays embedded in the systems they built their careers around.

This series is about what happens when it stays.

A combustion tuning engineer at a combined-cycle plant adjusts turbine parameters by feel. She knows that a three-degree drop in ambient temperature at 2:00 AM changes the optimal fuel-air ratio, and she knows it before the emissions monitor registers the drift. Fifteen years of pattern recognition encoded nowhere except her intuition.

Three hundred miles away, an integrity engineer at a Gulf Coast refinery reviews drone inspection images from a pipe rack. He has seen enough corrosion under insulation to know that when one elbow in a particular pipe class shows damage, the other forty installed by the same contractor during the same campaign are probably developing the same problem. But that pattern lives in his head, not in the system, and when he is not the one reviewing the images, it gets missed.

Meanwhile, an airline maintenance engineer notices a vibration signature that technically falls within tolerance but has changed subtly from prior inspections. Experience tells her the pattern matters. Left alone, it could become tomorrow’s operational disruption. That pattern recognition, too, often lives in people more than systems.

Different industries. Different assets. Different failure modes. But the underlying challenge is identical. Critical operational knowledge is trapped in individuals rather than embedded in workflows. When those individuals are unavailable, whether at 3:00 AM on a night shift or permanently after retirement, the organization operates with less intelligence than it possesses.

GE Vernova is building something that did not exist before. Not a dashboard. Not a data lake. A system of intelligence that captures expertise, applies it continuously, and compounds it over time as more decisions flow through it.

That means Autonomous Tuning and CERius carbon emissions management software work in parallel. Both systems can be installed simultaneously, no integration required, working in tandem to solve for emissions reduction. Whether you sit in operations or sustainability, the tools will be there to help you manage your emissions.

Under the hood, neural networks trained on physics and operational history adjust combustion parameters every few seconds, finding the optimal point where acoustic limits, emissions limits, and load limits press against each other. Not once. Continuously. The turbine at 2:00 AM runs at the same level of optimization as the turbine at 2:00 PM when the senior engineer is watching.

And CERius tracks the emissions consequence of every operational decision in near-real time, turning compliance from a quarterly scramble into a continuous, audit-ready intelligence layer. Cleaner and cheaper. The regulatory case and the economic case pointing in the same direction.

In downstream operations, that means computer vision models that do not simply flag anomalies but classify them, connect them to population-level patterns, and trigger defect elimination workflows. A single finding on one pipe becomes a fleet-wide insight when AI identifies that the same failure mode is developing across an entire asset class.

And across industries, our predictive maintenance software SmartSignal’s machine learning digital twins predict failures before they develop, while a new prescriptive analytics layer combined with a GenAI translates those predictions into specific, prioritized actions. 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. Prescriptive analytics will tell him what to check first, drawing from manuals, case histories, and the accumulated knowledge of engineers who have resolved similar problems before.

And beneath all of it, a natural language interface lets operators query their own asset data as simply as asking a question. No tag-mapping expertise required, no SQL, no hunting through folder structures from twenty years ago. The system understands what they mean and returns what they need.

These capabilities are built for the people who run these systems. They are the real people — reliability and integrity engineers, planners, plant managers, and other operations specialists — whose decisions determine whether operations run safely, efficiently, and profitably. The intelligence GE Vernova is building exists to make those decisions better, faster, and more informed — not to replace judgment, but to ensure critical decisions are never made without the full picture.

Each post in this series examines one layer of this system of intelligence. The capabilities explored are AI-enabled and available exclusively on our SaaS platform, where the full value of this system of intelligence can be realized.

We begin with the gap between the control room and the quarterly report, when the finance department sees costs mysteriously rise and operations sees conditions within normal bound, yet there is drift and no one is connecting the dots.

Then we move to the alert that knows the next step, where predictive analytics gains a prescriptive layer.

From there, we explore how inspection findings become insights for prioritized actions that tell you which assets need attention now and which can wait, how hundreds of pages of unstructured reports help with turnaround decisions in seconds, how local maintenance playbooks become global fleet strategies, and how a natural language interface turns complex industrial data into a simple conversation between the operator and the system.

Six posts. One thread connecting all of them: the shift from systems that record what happened to systems that know what to do next.

The expertise does not have to walk out the door. It can stay, embedded in the system, available on every shift, at every plant, compounding with every decision that flows through it.

That is what Intelligence at Work means. Let us show you how.