How edge intelligence, lifecycle separation lead to smarter, more resilient industrial operations

Today’s process industries navigate a dynamic landscape shaped by interconnected assets, stringent sustainability targets and rapidly advancing digital technologies. Supply chain disruptions continue to expose operational vulnerabilities, while customers demand unprecedented transparency and agility. To stay competitive, plant owners must not only optimize energy efficiency but also adapt swiftly to evolving market demands, navigate increasingly complex data environments and meet rising expectations for transparency.

These converging pressures are redefining how industrial control systems must evolve. Modern plants operate thousands of field devices generating continuous data streams while regulatory demands become more stringent. At the same time, expectations for real-time optimization and predictive capabilities are growing. For plant operators, the challenge is no longer choosing between innovation and reliability, but in achieving both. Distributed control systems (DCS) remain the cornerstone of industrial operations, providing the foundation for safety-critical functions and deterministic execution. For decades they have reliably managed complex process variables, enforced safety interlocks and ensured regulatory compliance. Their strength lies in delivering consistent, predictable performance that keeps plants running safely and efficiently. As operational demands expand, there is a growing opportunity to extend these trusted capabilities without compromising their core mission.

The key insight driving change is that different operational functions have fundamentally different requirements. Safety-critical control demands stability, extensive testing and minimal change. In contrast, advanced analytics, optimization and predictive capabilities benefit from rapid iteration and continuous improvement. Attempting to unify these divergent needs within a single system, however, often leads to compromises that do not fully serve either requirement.

The solution lies in a complementary architecture. By preserving the DCS’s role as the reliable control layer and introducing a parallel digital layer for advanced analytics, machine learning and optimization, each environment can do what it does best. This separation of concerns enables innovation without sacrificing reliability, while maintaining secure, integrated communication between the two.  

The best of both worlds – combining stability and flexibility

One of the enduring challenges in industrial automation is lifecycle management. Control systems require extensive certification, rigorous testing and carefully managed updates to ensure safety and reliability, while digital applications thrive on rapid iteration to incorporate new insights and capabilities. By separating these layers, yet keeping them connected, plants can innovate at digital speeds without compromising stability in core control functions. The question then is where should these advanced digital capabilities be deployed?

The key lies in edge computing, but not in its traditional role of basic data collection and processing. To truly enable separation of concerns, edge systems are evolving into intelligent platforms capable of learning, adapting and making autonomous decisions. This transformation gives rise to edge intelligence.

At its core, edge intelligence brings smart capabilities directly to equipment and systems at the plant level, enabling immediate decisions without relying on remote systems. At a more advanced level, it connects local intelligence with artificial intelligence (AI) and cloud computing, forming a distributed network where systems learn from each other and benefit from shared knowledge

By placing processing and decision-making power at the source of operations, edge intelligence enhances control automation without disrupting operations. It enables rapid, localized responses which are critical in systems where latency can delay action. The true value emerges when edge intelligence operates as part of an integrated ecosystem. With this integration, local systems continuously learn from operational patterns while feeding insights to cloud-based analytics. Machine learning models developed centrally are deployed to edge nodes where they adapt to local conditions. Problems detected at one site can trigger fleet-wide analysis to prevent similar issues elsewhere. The result is an intelligent network where immediate local responses and enterprise-wide analysis reinforce each other.

Opening new possibilities for digital growth

This connected approach transforms how plants manage the exponential growth of IoT devices and digital twins. Continuous data streams from devices that could otherwise overwhelm centralized systems are filtered and processed locally at the edge, reducing bandwidth demands and providing actionable insights. Digital twins benefit from distributed processing, enabling real-time synchronization between physical assets and virtual replicas without network bottlenecks.            

Within all this, modular automation provides the technical foundation that enables this approach. Modern DCS are increasingly modular, enabling individual process units, safety systems and optimization functions to be updated independently, without disrupting core operations.

The containerized modules function as secure, self-contained environments with defined interfaces, allowing automated orchestration based on performance and security requirements. This approach moves beyond  perimeter-based security toward zero-trust principles where each component must prove its identity and authorization.. This modularity aligns with separation of concerns, targeted upgrades to digital capabilities while leaving the proven core control unchanged.

Industry initiatives such as the Open Process Automation Forum (OPAF) and NAMUR guidelines have long recognized the need for such modularity and interoperability. These standards emerged from the realization that legacy systems often hinder innovation and increase obsolescence risks. By promoting open interfaces and vendor-neutral integration, they enable the flexible, future-ready automation architectures that today’s dynamic environments demand. The evolution from Industry 4.0 to Industry 5.0 reflects a deeper understanding. While Industry 4.0 emphasized connectivity and data collection, Industry 5.0 focuses on human-centric operations, sustainability and resilience. This focus is supported by preserving human expertise in control operations while augmenting it with intelligent digital layers. Operators retain their process knowledge and safety focus while gaining predictive insights and optimization recommendations.

This extended automation model delivers measurable value for plant owners:

• Energy optimization algorithms at the edge reduce operating costs and support environmental compliance.
• Predictive maintenance extends asset life and minimizes unplanned downtime directly impacting profitability. 
• Enhanced traceability and compliance reporting reduce regulatory risk and support sustainability goals demanded by stakeholders.

The AI and machine learning capabilities driving these benefits require flexible data access, rapid feedback loops and continuous learning. Edge processing delivers this by enabling real-time pattern recognition and local optimization while maintaining the deterministic behavior essential for process control. Meanwhile, cloud resources support complex modeling and cross-site learning without disrupting local operations.

Power to the people – empowering the next generation workforce

As experienced operators retire, the need to transfer decades of process knowledge becomes urgent. The next generation expects digital-native tools and intuitive interfaces. Extended automation capabilities meet this expectation by transforming roles:

• New operators engage with systems through advanced visualization, predictive analytics and automated diagnostics.
• Experienced staff focus on strategic decisions and exception handling, supported by continuous digital assistance.

Rather than replacing expertise, these tools amplify human capabilities and accelerate learning in complex environments.

Extended possibilities without compromise

This new automation paradigm marks a structural shift toward adaptable, resilient systems designed for long-term industrial use. By leveraging edge intelligence and lifecycle separating life cycle concerns, plant owners can drive innovation without sacrificing operational continuity. The result is a powerful combination of energy efficiency, transparency and reliability that preserves the proven core controls that ensure safe, reliable operations. For plant owners navigating an increasingly complex landscape, this approach turns the traditional trade-off between innovation and stability into a strategic advantage.