The hardest question in enterprise AI right now isn't "does it work?" It's "how do you prove value beyond productivity?" Executives have heard the pitch - GenAI saves 30 minutes a day, automates reports. The ROI is real but soft. What does it actually change?
We got an answer during a supervised demo with Ameren, a Fortune 500 utility serving 2.4 million customers across Missouri and Illinois. It wasn't about time savings. It was about finding four transformers with dangerous dissolved gas readings that had gone unnoticed for months using existing systems.
The Problem: Fleet-Wide Visibility That Takes a Full Day (So It Rarely Happens)
Ameren's reliability engineers are responsible for thousands of assets across a grid serving 2.4 million customers. They're deeply skilled at what they do but they're also stretched thin.
Transformer dissolved gas analysis (DGA) data exists in their systems. The expertise to interpret it sits on the team. But synthesizing that data into fleet-wide insights requires manually stitching together exports from multiple systems, a process that takes roughly eight hours when done comprehensively. And reliability engineers have full workdays: responding to outages, managing scheduled maintenance, handling compliance requirements, supporting capital planning. Proactive fleet-wide DGA sweeps aren't skipped because they lack value, they're deprioritized because the team is busy doing the other critical work that keeps the grid running.
The result is a gap that no one wants but everyone understands: comprehensive analysis happens periodically rather than continuously. Issues surface reactively when they escalate, not proactively when they're still manageable.
Ameren's team recognized this trade-off and wanted to break it. They partnered with Hitachi Energy, whose APM Health software already housed their asset data, and brought in Bolo AI to add a natural language query layer that could collapse eight hours of manual synthesis into minutes.
The Demo: Day Zero to Insight
We configured data connections in under two weeks. Then came the supervised demo on Ameren's actual fleet data on test environment.
A substation maintenance engineer asked a simple natural language query: show me transformers with acetylene spikes across my fleet for the last 2 years.
Within minutes, the system surfaced results. Four transformers were flagged with readings that warranted immediate attention.
One of them stopped the room.
The Ameren team's reaction was immediate: "If that value is real, that transformer should not be in service."
They called the engineer responsible for that asset on the spot. An emergency oil sample was scheduled that day to investigate further.
When we asked how they would have found this issue without the system, their answer was direct: "Almost impossible."
What Happened Next
The demo was in January 2026. Within a day, Ameren's team had reviewed the full list and taken action.
Three additional transformers beyond the initial flag required follow-up. Two had already been addressed with updated samples and additional testing, one needed a new sample collected. The rest showed only trace acetylene levels: normal operations.
The system didn't just surface a problem. It gave the team a prioritized view of their entire fleet, separating genuine risks from background noise, in minutes instead of days.
Why This Matters: Real ROI Beyond Productivity
The 240x speed improvement is real - what took eight hours now takes two minutes. But that metric undersells what actually changed.
Before: Fleet-wide DGA analysis was a periodic, resource-intensive project. Issues surfaced reactively.
After: The same analysis runs on-demand, enabling proactive monitoring at a cadence that was previously impossible.
The value isn't "we saved eight hours." The value is:
- Risk identification: Critical transformer issues surfaced that existing tools and processes missed
- Operational change: Reactive monitoring became proactive fleet management
- Decision speed: From data to emergency action in the same meeting
This is what real GenAI ROI looks like in industrial operations. Not incremental productivity gains but fundamental changes in what's operationally possible.
The Technical Foundation
Bolo AI began working with Hitachi Energy in 2025 to integrate generative AI capabilities directly into their APM Health software. The goal: help Hitachi's utility customers unlock insights trapped in their asset data without requiring new dashboards, data migrations, or workflow changes.
The result is APM Smart Query, a Bolo AI-powered capability that enables natural language queries against complex operational databases. Engineers ask questions in plain English. The system translates those queries into the appropriate database calls, synthesizes results across sources, and returns answers in context.
The integration is built around Bolo AI's context and semantic layer, which bridges customer data and our AI agents. This allows accurate reasoning over sophisticated industrial schemas - in Hitachi's case, databases with 23 tables, up to 33 columns per table, and tens of thousands of records per asset type. In testing, the system achieved a 93% pass rate on user acceptance test cases covering simple search, anomaly detection, comparative analysis, and recommendation support.
Ameren's deployment represents customer validation of this work. The two-week timeline from kickoff to working demo wasn't a one-off sprint. It reflects an architecture designed for rapid deployment on top of existing systems.
What's Next
The supervised demo validated the value. Now Ameren is moving to hands-on deployment, giving their reliability engineers direct access to query their fleet data on demand.
Beyond acetylene detection, the team is expanding into additional workflows, including SF6 reporting for compliance. That process previously required manual assembly from multiple systems and engineers. With APM Smart Query, it becomes another natural language prompt away from a complete report.
For utilities and industrial operators evaluating GenAI, Ameren's experience offers a clear proof point: the technology can deliver measurable operational value, not someday, but in weeks, on production data, with real consequences for how teams manage critical infrastructure.
