Artificial intelligence is moving closer to the core of industrial operations, but realizing its value requires more than adding AI applications to existing systems. An ARC Advisory Group article by Bob Gill, Emerson Advances Automation in the Age of AI, examines Emerson’s automation strategy and highlights the digital foundations, engineering models, operational data, and human expertise needed to make industrial AI useful in mission-critical environments.
Why It Matters
For senior leaders in the process industries, the question is shifting from whether AI has potential to how to deploy it responsibly and at scale. Peter Zornio, Chief Technology Officer at Emerson, described an approach that combines expanded sensing, lifecycle digital twins, a comprehensive data fabric, and AI-enabled automation. The goal is to create an operational foundation where AI can support modernization, optimization, decision-making, and increasingly autonomous operations.
Key Takeaways
- Industrial AI depends on a strong digital foundation and access to contextualized operational data.
- AI-powered tools can reduce engineering effort and risk when modernizing aging control systems.
- Hybrid digital twins combine physics-based engineering models with AI and data-driven methods to support real-time optimization and decision support.
- AI operations advisors can bring together operational records, documentation, alarm data, and digital twins to help preserve and apply organizational knowledge.
- The journey toward autonomous operations is evolutionary, with AI augmenting human expertise rather than simply removing operators.
Build the Foundation Before Scaling AI
The article points to data as a central requirement. Industrial organizations often maintain separate systems for control, maintenance, reliability, environmental performance, and operations. AspenTech Inmation provides a unified data fabric that provides common access, context, and governance across these sources.
That foundation can extend to lifecycle digital twins. By combining first-principles engineering models with AI, these models can support online optimization, operator guidance, predictive analysis, and what-if scenarios while remaining grounded in process behavior.
Focus AI on the Difficult Operating Problems
A particularly important management insight is that the next challenge is not simply automating steady-state operation. It is helping people manage abnormal situations such as equipment failures, changing feedstocks, weather events, and process upsets. AI systems can increasingly help operators identify, diagnose, and respond to these events.
The longer-term direction is smarter operations in which AI and human expertise work together to improve performance, reliability, sustainability, and workforce effectiveness.
Explore Industrial AI
Visit the Industrial AI section on Emerson.com to explore solutions designed to deliver smarter, safer, and more sustainable solutions for online, mission-critical operations.
