Signal vs. Noise: Navigating Operational Technology in the Age of AI

Spend a few minutes scrolling online, and you'll notice something interesting: everyone seems to be an expert in Operational Technology (OT). There are detailed posts about OT cybersecurity strategies, long articles explaining industrial AI, and confident predictions about the future of manufacturing. Many of these pieces look thoughtful and credible. Some are even technically correct. But increasingly, a quiet question sits in the back of many leaders' minds: how do you know what to trust anymore? 

That question came up in an interesting way during this year's S4x26 Conference. One of the recurring themes across several sessions was the idea of connection. On the surface, that might not seem like a new concept. Over the past decade, OT environments have steadily become more connected to enterprise systems through digital transformation initiatives. Those connections have enabled better visibility, centralized monitoring, and remote support across industrial environments. What stood out at the conference, however, was the sense that we are about to enter a very different phase of connectivity. And the primary driver behind it is not networking infrastructure or modernization programs. It is data and AI. 

For years, organizations have debated whether connecting operational systems more deeply into enterprise and cloud ecosystems was worth the risk. Security concerns, operational disruptions, and the complexity of legacy industrial systems often slowed those initiatives. That hesitation made sense. Connecting critical operational systems has real consequences. But as AI capabilities mature, the value of operational data continues to increase. Predictive maintenance models need equipment data. Production optimization tools need real-time process inputs. Quality analytics and energy optimization platforms rely on continuous operational visibility. The more data these systems receive, the more valuable they become. 

OT Connectivity is Entering a New Phase

The expectation discussed at S4x26 was that within the next two to three years, the business case for accessing OT data may become so strong that the C-suite will struggle to say no. When the operational and financial value becomes clear, the conversation changes. Instead of debating whether to connect systems, leadership begins asking how quickly it can be done. 

That means we should expect to see a continued explosion of connections into OT environments. AI platforms, analytics engines, enterprise dashboards, and third-party tools will increasingly request access to plant-floor information. But the word "connect" surfaced in another context during the conference that may be even more important. While OT environments are becoming increasingly technologically connected, the information ecosystem around OT is becoming increasingly chaotic. 

AI's Growing Appetite for Operational Data 

AI has dramatically increased the amount of content being produced online. Articles, industry commentary, research summaries, and opinion pieces can now be generated in seconds. Much of it appears polished and authoritative. Much of it even contains elements of truth. The problem is that it often lacks context. Developing the curiosity to question assumptions and explore new ideas will become just as important as the technology itself in an AI-driven world. Large language models can produce content that is mostly correct, but operational environments depend heavily on nuance and experience. A recommendation that sounds reasonable in theory may fail when applied to a real facility with legacy systems, safety constraints, and production pressures. As a result, leaders are facing a new challenge: how can we filter the signal from the noise? 

In many cases, the most reliable filter may not be technology at all. It may be your network: trusted advisors, partners who are implementing solutions in real facilities, peers you meet face-to-face at conferences, practitioners who are solving operational problems every day. These relationships form what could be described as a circle of trust. When new trends, technologies, or claims emerge, validating them through that network becomes critical. If multiple trusted sources are seeing the same thing in the field, the insight is far more likely to hold up. If a claim appears once from an unfamiliar source, it may deserve additional scrutiny. 

With Great Expertise Comes Great Responsibility 

There is also a responsibility that comes with expertise. The information being shared today will inevitably feed back into the AI systems generating tomorrow's content. If inaccurate or incomplete ideas spread widely, those models will learn from them and amplify them. That creates a feedback loop where misinformation becomes harder to detect. 

The OT world is about to become more connected than ever before. Data will move faster, AI systems will influence more decisions, and operational visibility will expand dramatically. But alongside those technical connections, we will need stronger human connections to help interpret what is real. Because in an AI-amplified world, clarity increasingly comes down to one simple question: who do you trust? 

Author Introduction: David Smit, OT Architect

Dave Smit is an Operational Technology (OT) Architect at Interstates who specializes in networking and cybersecurity for industrial and manufacturing environments. He works closely with customers to design secure, resilient OT systems and help organizations identify practical and innovative technology solutions. His work often focuses on building strong data and network foundations that support digital transformation, advanced analytics, and AI while maintaining operational reliability. 

Outside of work, Dave enjoys cycling, technology projects, and spending time with his family. Thank you for your hard work and expertise, David!