Industrial AI, Done Right: What Data Centers Can Learn from the Process and Power Industries
Data centers are building the infrastructure for AI. Who’s optimizing the infrastructure itself?
AI is used today to optimize almost everything: worker efficiency, process efficiency, energy efficiency. AI is everywhere. But what about where all of this actually comes from, the data center itself?
Data centers have become the physical backbone of AI infrastructure, yet the operational maturity behind that backbone has not kept pace with what it now supports. Many facilities already run tools that are strong on control and monitoring, or narrow AI tools focused on one system like thermal. Most facilities can already see that something changed. Few can trace why it changed across the systems that caused it. A temperature spike in the cooling cycle traces back to a power event three systems away, and only someone who has seen it before makes the connection in time. A cooling tower slowly losing efficiency to fouling shows up as a rising energy bill months before anyone traces it to the actual cause.
That deeper work, understanding why an anomaly or an efficiency loss is happening across interacting systems, still depends on a small number of people who have seen enough failures to recognize the next one, and there is rarely enough time to analyze fragmented data fast enough to act on it. According to Uptime Institute, that expertise is concentrated, not distributed, and the industry's own research says the gap is widening rather than closing.
The industry building AI infrastructure for artificial intelligence has rarely turned that same intelligence on itself. Not because the physics is unfamiliar.
A data center is like a process plant
A data center has no hazardous chemicals and no complex reactions, but the operational challenges are similar to a process plant: energy efficiency, asset reliability, uptime. And the assets are similar too. Chillers, cooling towers, air handling units, compressors, pumps, heat exchangers – all continuous processes.
Cooling alone makes the stakes concrete. The U.S. Department of Energy estimates cooling can account for up to 40% of data center energy usage overall, depending on facility type and efficiency.
Process industries have spent decades building the analytics maturity to answer these challenges. TrendMiner is used across power generation and close to half the chemical industry to do it: monitoring and reporting on equipment performance, catching drift before it becomes downtime, surfacing root cause across domains that used to be analyzed in isolation. The physics data centers run on has already been studied and optimized elsewhere.
Data centers are also increasingly operated in environments where change is constant rather than exceptional: live infrastructure changes that make anomaly detection or scenario validation necessary, fluctuating demand that requires forecasting and planning, and sustainability reporting under increasingly strict regulation. More than half of operators now track water use specifically, according to Uptime Institute’s 2026 survey, one more data stream competing for attention alongside energy and emissions.
The skill gap, and the trust gap it creates
Operational data is already collected in many data centers, much like in the process industry. But it is often fragmented, sitting across EPMS, DCIM, and other systems that were never built to talk to each other. And there are only a few people who can process large amounts of that data, and even fewer with real, deep expertise.
The data center industry faces a massive staffing shortage. Big players like AWS, Microsoft, and Google are already building their own training pipelines to cope according to the World Economic Forum.More than half of operators now report difficulty finding qualified candidates was found in the survey of Uptime Institute. The result is teams under constant uptime pressure, expected to know what to do and how to act quickly.
That shortage often pushes facilities toward AI to cover the gap. That AI is often a black box. It produces a verdict without showing the reasoning behind it. The real diagnostic work, tracing why an anomaly is happening across interacting systems, still depends on a small number of specialists who have seen enough failures to trace it themselves. Those specialists are rare, and rarely have the time to dig through all of it on top of everything else on their plate.
Left unaddressed, this has two consequences. The first is trust. A tool that hands back an answer with no reasoning attached gives staff nothing to check it against, and no way to trust it during an abnormal event when it matters most. This is already showing up in the data. Uptime Institute’s 2026 survey found that operator trust in AI’s operational benefits declined slightly this year, with confidence highest for the lower-risk applications, like sensor data analytics and predictive maintenance, that show their reasoning.
The second is skill decay. According to Uptime Institute, overreliance on AI and automation can weaken diagnostic judgment, situational awareness, and confidence during abnormal events, since staff supervising a black box have nothing to learn from except the answer itself. MIT Sloan economist Simon Johnson calls the alternative pro-worker AI. His own example is a data center technician, whose job already involves interpreting operational data and spotting early risk. AI that surfaces the reasoning behind a finding lets that technician build judgment. AI that just hands back an answer does not.
What data centers need is what the rare specialists need too: AI and analytics fast and easy enough to run across large, complex datasets, without a data science queue, transparent enough to build trust rather than erode it.
The TrendMiner Analytics Agent: Industrial AI, done right
TrendMiner already analyzes process data across close to half the chemical industry. The same analytics approach can now be applied to data centers. Data center facility teams look a lot like the process industry customers TrendMiner already serves: constant uptime pressure, and not enough time to dig through large amounts of data before deciding what to act on.
That is exactly the gap the TrendMiner Analytics Agent closes, working alongside the historian, DCIM, or BMS systems already in place rather than replacing them. A cross-domain anomaly, a compute job timing shift landing on top of cooling headroom that had been quietly shrinking for months, no longer requires the one specialist who has seen this pattern before. The agent lays out the correlation across systems directly, so any engineer can investigate it and reach the root cause, fast enough to matter under real operational pressure.
Why the TrendMiner Analytics Agent works this way:
- Consistent. The same question gets the same answer today and next week, so findings hold up when presented to the team.
- Explainable. No black box. Every assumption and step in the analysis is visible and can be checked or adjusted.
- Actionable. Insights come with a path forward, and can be turned into monitors and reports that keep working after the investigation ends.
This is not a narrow tool built for one problem. The same agent supports energy efficiency and cooling equipment optimization, heat exchanger fouling monitoring, and sustainability and mandatory KPI reporting, through the same time-series analysis, monitoring, and reporting used across all of it.
The model is not the advantage. Process data and domain experts are. The TrendMiner Analytics Agent gives data centers the way to put both to work, fast.
Curious what the TrendMiner Analytics Agent can do for your facility? Explore the platform or speak with us.
See it in action
TrendMiner’s analytics approach is proven across the process industries it already serves:
- Production increase through correlation analysis in a cooler of a combined-cycle plant: a 1% production increase.
- Optimizing operating conditions for a heat exchanger.
- Preventing pump resonance failure: saving up to $360k per equipment per day.
