Operational technology has been running the world's critical infrastructure for decades. Power plants, manufacturing lines, water treatment facilities, and refineries, all humming along on systems that prioritize uptime over upgrades. Now everyone wants to throw AI at it.
Great idea in theory. In practice, you're trying to teach algorithms about 30-year-old machinery that was never designed to generate data for analysis, let alone feed a machine learning model. The equipment works. The protocols are proprietary. The people who understand it best are skeptical of anything that might introduce risk.
But the opportunity is real. AI applied correctly to operational technology can predict failures, optimize performance, reduce energy consumption, and prevent the kind of catastrophic downtime that costs millions per hour. The key is doing it in a way that respects the constraints of OT environments while delivering measurable business value.
Here's how to actually do it.
AI trained on incomplete or inaccurate asset data will confidently tell you wrong things. It will recommend maintenance on equipment that was decommissioned two years ago. It will miss critical dependencies because the configuration items aren't linked. It will generate insights that sound impressive and lead nowhere.
The foundation of any AI strategy in OT is visibility. You need a complete, accurate inventory of your operational assets. Not just the major equipment, but the sensors, controllers, network devices, and supporting infrastructure. You need to know what's connected to what, what's critical to production, and what the actual operating parameters are.
This means building or refining your CMDB and aligning it to a structured framework like CSDM. It means integrating telemetry, historians, and asset management platforms. It means reconciling data across multiple sources and establishing a (wait for it) single source of truth.
Once your foundation is clean, AI becomes useful. Pattern recognition works when the patterns are real. Predictive models improve when they're trained on reliable data. Automation doesn't cascade into failures because the system understands context.
Your OT people speak PLC and ladder logic. Your IT people speak REST APIs and microservices. They work in different buildings, follow different protocols, and have fundamentally different priorities. IT optimizes for flexibility and feature velocity. OT optimizes for stability and safety. This divide has been acceptable for decades. It's not acceptable anymore.
The value of AI in operational environments comes from correlating data across both domains. When a sensor anomaly on the factory floor can be instantly cross-referenced with maintenance history, spare parts inventory, technician availability, and incident patterns from similar assets across other sites, you have actionable intelligence. When those systems remain isolated, you have expensive data lakes that no one trusts.
Connecting IT and OT requires a common data fabric. It's about creating an integration layer that can ingest operational telemetry, contextualize it with business data, and make it available to both human operators and AI models in a secure, governed way.
ServiceNow's Workflow Data Fabric does this by creating a unified data model that spans IT assets, OT equipment, facilities, HR, and business processes. When everything is connected through a common platform, you get end-to-end visibility. An anomaly triggers a workflow that includes full asset context, relevant documentation, parts availability, and recommended actions based on historical outcomes. That's where AI earns its paycheck.
Reactive maintenance is expensive, disruptive, and increasingly indefensible. When equipment fails unexpectedly, you're paying for emergency repairs, unplanned downtime, expedited shipping on parts, and all the downstream production impacts. You're also explaining to leadership why a predictable failure wasn't predicted.
Machine learning models excel at spotting patterns in time-series data that humans miss. Temperature drift, vibration changes, pressure fluctuations, and power consumption anomalies; these signals often appear days or weeks before a failure. The challenge is separating meaningful patterns from noise and doing it at scale across hundreds or thousands of assets.
This is where AI becomes genuinely valuable in OT environments. Train models on historical sensor data correlated with known failure events. Deploy them to monitor real-time telemetry. When anomalies are detected, trigger automated workflows for inspection, diagnostics, or parts procurement. Surface alerts to operators with full context and recommended actions.
Predictive maintenance isn't new, but AI makes it practical at scale. You can monitor entire fleets of assets continuously, adapt models based on new failure modes, and improve accuracy over time as you accumulate more operational data.
Pattern matching is not judgment. Context matters, and AI doesn't have it. When a temperature sensor spikes on a critical asset, a machine learning model might flag it as an imminent failure and recommend an immediate shutdown. A human operator who has worked with that equipment for 10 years knows it runs hot during certain production cycles, and the spike is normal. The AI sees an anomaly. The human sees Tuesday.
This doesn't mean AI isn't useful. It means you need to design systems where AI assists human decision-making rather than replacing it. Use models to surface signals that might otherwise be missed. Use natural language processing to summarize complex operational data. Use anomaly detection to prioritize what operators should look at first.
But keep humans in control of critical decisions. The best implementations of AI in OT environments create a collaboration between machine intelligence and human expertise. AI does the heavy lifting of monitoring, pattern recognition, and information synthesis. Humans provide judgment, context, and final approval on actions that affect safety or production.
In regulated industries like energy, chemicals, pharmaceuticals, and food production, compliance isn't optional, and it isn't periodic. It's a continuous operational requirement. Every control action, every parameter change, and every maintenance activity needs to be documented, traceable, and defensible.
The traditional approach treats compliance as an audit exercise. Teams scramble quarterly or annually to gather evidence, compile reports, and demonstrate adherence to regulations. It's labor-intensive, error-prone, and creates risk. If you can't quickly prove what happened on a specific asset at a specific time, you have a problem.
AI and automation make compliance continuous and embedded. Configure your systems to automatically log every operational event with full context. Use AI to monitor for compliance deviations in real time. Flag procedures that weren't followed. Surface gaps in documentation before they become findings. Everything is timestamped, traceable, and validated.
That's the difference between passing an audit and explaining why you don't have the evidence you need.
The technology industry loves to celebrate inputs. Models trained. Data points processed. Algorithms deployed. None of that matters if it doesn't improve business outcomes.
Tie every AI initiative in your OT environment to a measurable business impact. Reduced downtime translates to increased production capacity and revenue. Faster throughput means you can meet demand without capital investment in new equipment. Energy optimization cuts operational costs. Better safety performance reduces incidents, insurance premiums, and regulatory risk.
If you can't draw a clear line from an AI model to one of these outcomes, you're running a science project, not a business initiative. That's fine for research labs. It's not acceptable for operational technology that underpins critical production and infrastructure.
Track the mean time between failures and mean time to repair. Monitor overall equipment effectiveness. Calculate the financial impact of prevented downtime. Quantify energy savings. Report on safety improvements.
Make the business case for AI in operational technology based on results, not potential. When you can show that predictive maintenance prevented $3 million in lost production last quarter, you'll get budget for the next initiative. When you can only show that you processed more sensor data, you won't.
For organizations that depend on physical operations, like manufacturing, energy, transportation, utilities, and healthcare facilities, operational technology is where AI can deliver actual value instead of just interesting demos.
But success requires more than algorithms and ambition. You need a solid data infrastructure that provides accurate, complete visibility into your operational assets. You need connected workflows that break down silos between IT and OT. You need models that respect the constraints and risks of production environments. You need people who understand both the technology and the business.
That's not easy work. It requires investment in platforms that can unify operational data, governance frameworks that maintain quality, and organizational alignment between teams that haven't always worked together.