AI-powered productivity from industrial edge to enterprise

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AI-powered productivity from industrial edge to enterprise​

The full value of AI in process manufacturing has yet to be realized. The next step will require a hybrid architecture that orchestrates AI deployments across cloud, edge, and core control system computing environments based on use cases, performance requirements, and security policies.

Process control and AI insights​

• Cloud-centric approach hinders operational technology (OT) adoption.
• OT architecture is evolving, and the future relies on an orchestrated hybrid environment.
• Process control facilities are embracing the AI journey.

Artificial intelligence (AI) has dominated the technology world in just over three years. It is becoming increasingly difficult to find process manufacturing professionals who do not use generative AI tools, whether for document creation, research, or general Q&A-style interactions. As a result, many teams are pursuing opportunities to use advanced AI techniques in operational technology (OT) environments to increase productivity in manufacturing operations.

The common force behind today's most transformative AI tools, and the foundation that gives them their impressive capabilities, is cloud computing. The processing power and rapid scalability available in cloud environments are key factors that enable AI tools to be universally accessible and increasingly informative.

Cloud-centric approach hinders OT adoption​

The tight coupling between cloud computing and AI has slowed the pace of adoption for OT use cases, as OT teams are often reluctant to connect systems to the cloud. Cloud connectivity can be affected by network constraints that impact performance, and in some jurisdictions, data governance and regulatory requirements can limit adoption. These are critical issues for organizations focused on security, availability, and competitive advantage. However, even if OT teams were more willing to connect control systems to the cloud, in most cases, latency would still be too high for real-time, mission-critical operations.
These challenges can be overcome. OT technologies are evolving, and significant AI tools are already available, with more on the way. Forward-thinking organizations are using higher-performance computing capabilities closer to their core processes through edge hardware platforms.
Today, this strategy primarily manifests itself through the deployment of edge solutions physically isolated from core control system components. In the coming years, the OT technology stack will be further enhanced with the emergence of software-defined architectures that work with enterprise operations platforms to provide an integrated approach to AI workload orchestration.

Evolution of OT architecture​

One of the keys to successfully deploying AI in OT infrastructure is the ability to seamlessly move cloud-based workloads to an on-premises environment. Today, automation solution providers are equipping edge platforms with AI accelerators, allowing models similar to those deployed in the cloud to run on local systems. The goal is to provide solutions that can run on-premises, ensuring the necessary security and latency for OT environments, while also offering reasoning capabilities and generating verifiable natural language responses without any fabrication.
These local models are quickly proving that the industrial edge is the next frontier for OT-driven productivity gains, with a strong foundational data base containing clear context critical for success. Edge environments can seamlessly connect AI use cases with the rich data model and real-time operational data uniquely provided by distributed control systems. Most importantly, this processing can be performed quickly and securely at the edge, allowing results to be more directly tied to critical real-time goals.

Figure 1: From the intelligent field to the edge and the cloud, AI technologies will continue to enhance operational excellence and unlock autonomous operations. All figures copyright: Emerson
The capabilities of on-premises AI for OT environments are not limited to edge deployments. As automation solution providers develop software-defined control solutions, AI models requiring the lowest latency will be able to reside even closer to core process dynamics, eventually running in the same virtualized environment as other control system functions. With extremely low cycle times, these software-defined systems will help operators determine the best action in complex scenarios by capturing and embedding information using AI and making it available on demand. These systems will significantly reduce the time from analysis to problem resolution by automating operator-guided multi-step workflows (Figure 1).

The future relies on an orchestrated hybrid environment​

The effective deployment of AI in OT architectures relies on a flexible technology foundation aligned with each process's operational philosophy and unique performance requirements. Not every AI solution needs to reside on the same computing platforms as core control functions. Even with the most advanced software-defined control systems available and AI workloads running in the same environment as core control functions, many AI applications will still be better suited for edge or cloud environments.
For example, technologies likely to run in the software-defined control layer are real-time, precise, low-latency applications such as advanced process control, quality control, and other solutions that need to deliver results in seconds or milliseconds to be safe and effective.
In an edge environment, AI solutions that support reliability, sustainability, and other operational excellence outcomes will leverage powerful hardware-based AI accelerators to deliver results that can be provided with slightly longer cycle times.
Scenario and planning tools, performance engineering software, and enterprise virtual advisors for the fleet of OT systems are likely to continue to reside in cloud environments where they can scale seamlessly and deliver non-time-sensitive results.
As these computing environments continue to interconnect, OT teams will need an orchestration architecture to coordinate AI workloads across cloud, edge, and core control systems and ensure optimal performance across the automation stack. The most effective solutions will not be those built piecemeal and connected through complex, custom-designed interfaces, but rather those designed to integrate seamlessly.

Figure 2: Enterprise operations platforms will enable the effortless integration of AI and automation technologies for enterprise optimization.
As technologies continue to evolve, the need for orchestration is perhaps the most important element to consider. By the nature of technological advancement and project deployment, OT teams will implement AI solutions incrementally. Fortunately, embedded AI agents and other enterprise operations platform technologies simplify the connection between data sources and consumers through standard protocols, bringing contextualized insights together into a cohesive whole through an industrial data fabric for ease of use and connectivity (Figure 2).
Today's best automation technologies are part of integrated platforms designed to work together.

Embracing the AI journey​

The coming years will radically redefine OT environments worldwide. AI use cases will continue to proliferate, and organizations with a clear vision and the right partners will gain a significant competitive advantage. While not all solutions that will make up the entire OT AI technology suite are available today, the tools needed to build the foundation for AI and embark on this journey are available, and many organizations are incorporating them into the scope of their future projects. There has never been a better time to start laying the groundwork for the future of automation.