Reinventing process control for an AI-driven future

Process manufacturing is in a state of rapid change. Driven by advancing technology, evolving business demands, and shifting workforce dynamics, organizations are reimagining how they operate. The path forward centers on more intelligent, autonomous systems that optimize operational excellence and efficiency.

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Modern process manufacturers are now leveraging AI, machine learning, advanced process control, state-based control, and other powerful software solutions to improve operations, provide continuous and intelligent decision support, and capture institutional knowledge as repeatable procedures.

In fact, according to a Manufacturing Leadership Council report, nearly 39% of survey respondents are piloting AI projects, while another 23% have passed the pilot stage and are implementing projects operationally. Operational technology is increasingly adopting IT-centric technologies.

A software-defined shift

Today, the greatest challenge for organizations is not whether to adopt advanced technologies, but rather how to deploy them. Applications—including AI, ML, and APC—often require computational resources that exceed the capabilities of traditional OT controllers.

However, the alternative is IT servers, which have generally been considered ill-suited for the deterministic, low-latency, and high-availability requirements that OT environments demand.

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A new technology, the software-defined controller, is shifting this paradigm. Modern software-defined controllers can run the real-time, deterministic workloads necessary for critical operations on IT hardware with dramatically increased processing power, unlocking the flexibility and availability to drive more autonomous, intelligent operations.

Power and performance

AI, ML, APC, and other innovative automation software tools require significant processing power to perform their tasks. Traditional physical controllers typically lack the high-performance processors, graphical processing units, and memory bandwidth necessary to run these complex workloads.

In addition, most physical controllers are already at capacity, fully consumed by basic process control system functions and I/O communications.

As a result, many organizations looking to implement advanced and autonomous control software are frequently forced to do so outside of their control system, either in the cloud or on separate PC-based workstations. These configurations create additional challenges that can hamper operational efficiency.

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Cloud and network reliance presents potential latency limitations or connectivity issues that can reduce availability, especially during network outages. Moreover, such solutions typically require complex engineering to navigate firewalls and other network infrastructures to ensure reliable communication.

In contrast, software-defined controllers combine proven control software with server-based computing platforms. Because of their ability to use high-performance hardware, a single pair of servers can handle the control requirements of most large-scale facilities.

Flexible deployment

As AI rapidly transforms industrial operations, more autonomous operation will increasingly become a baseline for participation in the global market. Software-defined control will empower operations teams to implement these technologies quickly, reliably, and with faster return on investment.

Traditional controller implementation is time-consuming and costly. Engineers must examine the physical footprint and existing cabinets, perform complex engineering for allocating the I/O, and procure all the necessary physical hardware.

In contrast, with a software-defined solution, the system requires less wiring, less hardware, and a smaller physical footprint than a traditional setup. Once the infrastructure is in place, teams can provision new controllers in minutes as operational needs evolve.

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Beyond deployment efficiency, software-defined controllers streamline the testing and validation process. Many process manufacturing teams currently rely on simulation environments that approximate their control systems for testing purposes.

Software-defined controllers fundamentally change this approach because the simulation environment becomes the actual production controller. Teams can use these platforms as test beds to develop and validate new operations and control strategies, then transition the same fully configured controller directly to live operations, eliminating the traditional gap between testing and deploying into production.

Improved availability

For OT teams, availability is of the utmost importance. A controller cannot be used for mission-critical operations if it is subject to failures or performance issues that will cause unplanned downtime.

Modern software-defined controllers provide the same deterministic performance available from traditional controllers. The most advanced solutions are purpose-built to prioritize control software at the hardware level, ensuring their OT workloads execute reliably—without the risk that system functions might compete with control algorithms for resources.

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Software-defined controllers also enable resilience that physical hardware cannot match. Traditional controllers require redundant pairs to share the same backplane, creating a single point of failure.

Software-defined controllers break this constraint by enabling redundant instances to be deployed on separate servers and distributed across different locations, and even separate buildings. This geographic distribution dramatically improves uptime and disaster recovery capabilities, while maintaining seamless failover.

A seamlessly integrated solution

Leading software-defined control solutions are designed to coexist with traditional controllers, not replace them entirely. In harsh operational environments, such as those with extreme temperatures, heavy vibration, or other conditions where IT hardware would be unsuitable, physical controllers remain the practical choice.

This hybrid approach allows organizations to leverage software-defined control where it excels, while maintaining proven hardware solutions where environmental demands require it.

The hybrid approach only succeeds if it simplifies rather than complicates operations. Forward-thinking organizations are standardizing enterprise operations platforms where physical and software-defined controllers share a unified engineering environment.

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Engineers use identical tools, workflows, and programming interfaces whether configuring physical hardware or virtual systems.

This consistency eliminates custom integration work, enables seamless data exchange across all controllers, and empowers engineering teams and operators to move fluidly between platforms without retraining.

Innovation in IT/OT convergence

Software-defined control is enabling more autonomous operation and increased operational efficiency across process manufacturing. Leading organizations are already gaining experience-building confidence by deploying it at the industrial edge for utilities management, blending operations, batch processes, small lab systems, testing and validation systems, and beyond.

In doing so, they unlock new competencies, empowering operators with more automated workflows, increased mobility and decision support, and greater visibility into the health and performance of their processes.

The foundation of next-generation automation is here, unlocking the tools to define competitive advantage in process manufacturing for years to come.