AI Is Coming to the Control Room—But Are We Ready?
Over the past year, I've witnessed a major shift.
AI is no longer just a buzzword or a boardroom initiative. It's making its way to the operator console, where real decisions happen. We're seeing LLMs nudging maintenance actions, analytics engines suggesting setpoint changes, and cloud-connected AI proposing "next best moves" for real-time processes.
And here's the uncomfortable truth:
AI is already influencing OT operations, often without the governance or architecture to handle it.
A Familiar Pattern, Accelerated
This isn't the first time I've seen a cultural and technical shift of this kind.
Years ago, we faced a similar challenge when OT data began flowing to the business layer. The initial resistance wasn't technical; it was human. Concern over "being connected" slowed adoption until a generational shift occurred. Once those comfortable with smartphones, wearables, and cloud-connected tools entered leadership, connectivity was no longer optional it was expected.
The same thing is happening now. But faster.
This time, we have two drivers:
- AI is already part of everyday life—from phones to emails to task automation.
- There’s corporate pressure to deploy AI at scale, framed as operational excellence, workforce optimization, or digital transformation.
Whether OT experts resist or not, the AI wave is coming, and cost savings will be the ultimate accelerant.
We expect the Autonomous Operations Maturity Model to evolve rapidly, not just for efficiency, but also because financial pressure will demand it.
But AI Needs Access—And That Means Exposure
Here’s where it gets real. To function, AI needs data. And in OT, data lives deep in Level 1 and 2—on PLCs, HMIs, DCS servers.
So ask yourself:
- How is AI accessing that data?
- Through what channels?
- With what controls?
- Who’s watching what goes in and out?
We’re already seeing architectures where AI systems are connected to core production networks without clear segmentation, governance, or rollback paths.
If we've learned anything from over 2 decades in control systems, it's this: no tool is safe unless the architecture is built to fail safely.
Autonomy Without Governance Is a Risk Multiplier
Autonomous operation sounds impressive—but we know what that really means: allowing systems to act with minimal human oversight.
In modern pharmaceuticals or automotive industries, this works because processes are standardized and predictable. But in power, oil & gas, or utilities?
- Conditions change by the minute
- Site-specific knowledge drives decisions
- Human judgment isn't optional—it's essential
Yet AI is being piloted without defining basic governance questions:
- Who approves an AI recommendation?
- What happens when the AI gets it wrong?
- Who owns accountability?
We’re not saying “don’t adopt AI.” We’re saying: engineer the system to handle AI like any other operator, with oversight, boundaries, and contingency.
What Needs to Happen Now
Here's what we're helping clients build into their Enterprise OT Cybersecurity Programs to prepare for this AI shift:
✅ AI-Aware Architecture
- Treat AI engines like third parties—not trusted insiders.
- Isolate AI data flows from control-critical systems.
✅ Secure Data Supply Chains
- Don’t just export data—protect, contextualize, and monitor every step.
✅ Governance That Reaches the Plant Floor
- Embed AI roles into SOPs, PHAs, and HAZOPs.
- Include AI in your incident response and root cause workflows.
✅ Cross-Functional Readiness
- OT, IT, data science, and security must collaborate, not compete.
Bottom Line: We've Been Here Before—But This Time, It's Faster
Just as we saw with the rise of connected operations, AI adoption in OT will transition from being resisted to being required. The only question is whether you'll build the foundation before or after it's already influencing decisions.
Let's not let AI outpace the controls we've spent decades building.
📩 Ready to assess your AI integration architecture and OT cyber posture? Let's talk.