Author: Andrew Foster, IOTech Chief Product Officer
Automation.com recently published an article highlighting eight AI trends shaping industrial operations in 2026. The list includes autonomous systems, predictive maintenance, cybersecurity, workforce transformation and other technologies that are rapidly moving from pilot projects into everyday operations.
Reading it reinforced something we’ve been seeing across the industrial sector.
The conversation has shifted from “Should we use AI?” to “How do we successfully deploy AI?”
That’s an important step forward.
But I think many organizations are overlooking an equally important question: Is your industrial data ready for AI?
Industrial AI Depends on Trusted Operational Data
Artificial intelligence is only as effective as the data behind it. In industrial environments, that means operational technology (OT) data, information generated by PLCs, SCADA systems, sensors, controllers, building automation systems, historians and other connected equipment.
AI applications rely on this data to identify patterns, predict failures, optimize operations and support real-time decision-making.
If the data is incomplete, inconsistent or delayed, AI can only deliver limited value. The challenge often isn’t the AI model. It’s the quality and accessibility of the operational data feeding it.
Legacy Industrial Systems Create Data Challenges
Most manufacturers, utilities, energy providers and commercial facilities haven’t built their operations from scratch. Instead, they’ve added new technologies over many years.
The result is often a mix of:
- Legacy PLCs
- SCADA systems
- Proprietary protocols
- Modern IIoT devices
- Equipment from multiple vendors
- Cloud-based applications
Each system performs an important function, but they don’t always communicate effectively with one another. That creates data silos, which make it difficult to provide AI applications with a complete, real-time picture of what’s happening across an operation.
Why Data Interoperability Matters
As industrial AI adoption accelerates, data interoperability is becoming a strategic priority.
Organizations need a way to connect diverse OT systems, normalize data from different sources and make that information available for analytics, automation and AI applications.
Without that foundation, organizations often spend more time collecting and preparing data than acting on it. Preparing industrial data for AI is quickly becoming just as important as selecting the right AI platform.
Why Edge Computing Is Becoming More Important
This is one reason edge computing continues to gain momentum. Edge computing platforms help connect legacy operational technology with modern industrial AI applications by collecting, normalizing and processing data closer to where equipment and assets actually operate.
Rather than replacing existing infrastructure, edge platforms help organizations extend the value of what they already have while supporting faster operational insight and decision-making.
That capability is becoming increasingly valuable across manufacturing, renewable energy, utilities, transportation and smart buildings.
As these environments become more connected and distributed, the volume of operational data continues to grow. And so does the need to process it efficiently.
Preparing Infrastructure for Industrial AI
The organizations that will gain the greatest value from industrial AI won’t necessarily be the ones that deploy AI first. They’ll be the ones that prepare their infrastructure first. That means building an operational data foundation that is connected, interoperable and capable of delivering trusted information in real time.
Industrial AI is creating tremendous opportunities for efficiency, resiliency and operational improvement. But AI doesn’t begin with algorithms. It begins with data. And increasingly, that data starts at the edge.