Intelligence at Work: The Strategy That Builds Itself

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. It should not be relied on in making a purchasing decision. The development, release, and timing of any features or functionality described for our products remains at our sole discretion and should not be relied upon in making a purchasing decision.

A reliability engineer at a power generation company is responsible for the maintenance strategy on a fleet of turbines. He has done this dozens of times, and somewhere out there, a colleague solved this same problem last month, but that knowledge never made it into a system he can access. Every time a new asset comes online, or an existing strategy needs a refresh, the engineer starts the same process.

He opens a blank template, identifies failure modes, and assesses risk for each one. He then maps recommended actions and maintenance intervals and documents the reasoning. For a single complex asset, this process takes weeks. For a fleet of hundreds or thousands of assets, it takes months, maybe years. And before he finishes the last turbine, the first one may be ready for review again.

The work itself is not the problem. Reliability Centered Maintenance (RCM) and Failure Modes and Effects Analysis (FMEA) are proven methodologies. The problem is that every strategy starts from zero. The engineer is not building on anything. He is rebuilding, every time, from his own experience and whatever documentation he can find locally. The institutional knowledge that should accelerate this work is scattered across spreadsheets, retired colleagues' files, and sister facilities he has never spoken to.

This isn't just slow. It's expensive. When asset strategy development takes weeks per asset, organizations either under-invest in the process and accept gaps, or they over-invest in labor and still end up with inconsistent results across the fleet. The engineer closest to retirement holds the most knowledge, and none of it is captured in a form that scales.

ISO 55000 established the global framework for asset management precisely because organizations recognized that asset value erodes without systematic strategy. But the standard assumes the strategy gets built. It doesn't solve the bottleneck of building it.

In asset-intensive industries, from airlines to power generation to oil and gas, high-value rotating equipment drives revenue every hour it runs. Every week spent manually constructing a strategy is a week that asset runs without an optimized plan. Senior experts are retiring with decades of wisdom kept in their heads instead of the company database.

Every week spent on manual entry is time lost that could have been used to capture that vital expertise before it leaves the building. Potentially lost for good.

GE Vernova’s upcoming AI-assisted asset strategy template generation changes this by creating asset strategies automatically. Using industry standards and data from similar assets, it produces a draft strategy that includes failure modes, risk assessments, and recommended maintenance actions. Instead of starting from a blank page, the engineer receives a strategy built from the collective intelligence of every comparable asset in the industry.

It is an automated process with a human at the helm. The reliability engineer reviews the draft, then refines and approves it based on his local operating context. AI augments his decision-making. It does not replace it. The engineer’s role evolves from document builder to strategy editor and the weeks of manual development compress into hours of informed review.

Once asset strategies are generated from a shared foundation, something else happens. Consistency emerges across the fleet without being forced. When every site's strategy draws from the same synthesized baseline, the best practices from top-performing facilities naturally propagate. The engineer doesn't need to call another plant to ask what intervals they run. That intelligence is embedded in the draft he receives.

Time to value is accelerated, targeting potentially millions in savings per facility through reduced strategy development overhead and improved resource allocation. Crucially, the engineer is not eliminated, rather the redundant work that keeps them from higher-value analysis.

AI-assisted asset strategy template generation will automate this process, extracting data from unstructured documents, creating the structured records required for compliance, and automatically generating potential issues as findings. The engineer will interact with that data immediately rather than spending days on manual entry. An entire workflow disappears. In its place, the engineer spends that time where it matters, analyzing the findings, connecting patterns across assets, and making decisions that prevent failures before they happen.

Today, AI-assisted asset strategy template generation is in pilot validation with select customers. The vision ahead involves deeper fleet-wide synthesis, where the system surfaces evidence from sister facilities that have tested a proposed approach, showing the measured impact on failure rates and cost. It would highlight where a site's plan deviates from proven fleet practices, prompting the engineer to adopt the proven path or justify the deviation with documented reasoning.

Beyond that, the next phase integrates asset health data directly into the FMEA process. Instead of waiting for a periodic review, the strategy would update when performance data shows that a specific failure mode is more or less likely than originally modeled. The strategy evolves as the asset ages, becoming a living document rather than a static one.

This post demonstrates how intelligence removes the blank page from strategy development, turning months of manual work into hours of informed review. Stay tuned to learn what happens when natural language allows any operator to query their own data, turning complex analysis into a simple conversation.