Why this matters now
Many industrial organizations are at an operational inflection point. A global, highly competitive marketplace demands greater efficiency and optimization, yet many of the most powerful analytical and AI-driven tools live in the cloud.
Operational technology (OT) environments have historically resisted cloud connectivity to ensure security, determinism, and protection of intellectual property. However, the accelerating adoption of AI requires new approaches to data access and decision support that do not compromise these core OT values.
As Sean Saul, vice president of the DeltaV™ platform at Emerson, and an expert with decades of experience in automation, explains in a recent article in Control magazine, edge technology provides a secure path forward:
“New edge areas will use technologies like Type 1 hypervisors to perform deterministic behaviors based on real-time data.”
Takeaway: Edge computing allows OT teams to enable AI-driven insight while preserving deterministic control and security.
TL;DR
- Industrial organizations need AI insights without compromising OT integrity.
- Cloud-only approaches conflict with real-time and security requirements.
- Edge computing enables secure data egress with contextual integrity.
- LLMs and AI copilots rely on real-time, high-quality operational data.
- Enterprise operations platforms unify edge, cloud, and control systems.
Enter the edge
Modern edge technologies provide a critical bridge between OT systems and advanced analytics. Solutions such as the DeltaV Edge Environment enable secure egress of control system data while preserving operational context.
This architecture ensures AI-driven applications receive a steady stream of trusted data without introducing cyber or operational risk.
As AI adoption increases, the edge becomes essential for supporting LLM-based tools that streamline analysis and advanced control. Saul highlights this shift:
“With more powerful edge computing aligned with real-time data, users can apply large language models (LLMs) to streamline analysis and advanced control.”
Tools such as DeltaV Virtual Advisor embedded into DeltaV Live and the Guardian Virtual Advisor depend on this architecture to provide safe, natural-language guidance to operators.
Takeaway: Edge computing supplies AI tools with real-time, trusted data while maintaining OT safeguards.
An industrial foundation
While edge tools deliver immediate value, the most advanced optimization requires an integrated foundation. Organizations are increasingly adopting an enterprise operations platform (EOP) strategy to unify data from the field through the edge and into the cloud.
Built on a software-defined architecture and unified data fabric, the EOP combines real-time control with industrial AI and zero trust cybersecurity to enable secure, scalable AI adoption.
Saul underscores the importance of industrial-grade AI outcomes:
“While mainstream consumers can use AI to get approximate answers, we need it to give industrial-grade answers without approximations or fabrications, and we think we’re succeeding. We believe some on-premises AI architectures are mature enough to serve in the process control space. By using specialized knowledgebases like DeltaV runtime data and customer-specific procedures, they can give users gen AI tools for operations, and configure control strategies in context within proven applications.”
Takeaway: An enterprise operations platform enables industrial-grade AI that is accurate, contextual, and trustworthy.
Building for the future
The future of industrial operations will be defined by AI-driven optimization, advanced automation, and increasing autonomy. Digital transformation laid the groundwork by creating the intelligent field.
Now, OT teams must modernize applications and architectures to move data safely beyond the control layer—empowering the innovators who will define efficiency, safety, and resilience in the years ahead.
Takeaway: Modernizing around edge and AI prepares OT teams for sustained, long-term operational excellence.
