Artificial intelligence and new architectures: Navigating the IT/OT convergence

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Bringing diverse elements together to create something new – convergence – has advanced human development throughout history. Astronomers, mathematicians, and cartographers collaborated on accurate celestial navigation techniques crucial for early maritime exploration. Ancient civilizations combined engineering, mathematics, and architectural skills to create underground aqueducts and irrigation systems, which allowed us to thrive in semi-arid regions. And, now, artificial intelligence will help drive convergence to new levels.

Today, convergence is happening between IT (information technology) and OT (operational technology) systems. IT systems traditionally deal with data access, storage, analysis, and processing, often in virtual or cloud-based environments. OT systems manage industrial operations and control physical devices like machinery, sensors, and other equipment, often with embedded remote terminal units (RTUs) or programmable logic controllers (PLCs).

IT and OT integration enables businesses to gather real-time data from physical operations, analyze it for insights, and then employ these insights to:

• Optimize processes
• Enhance efficiency
• Make informed decisions
• Utilize predictive maintenance
• Enable remote monitoring
• Automate industrial processes

How the digital world now affects the physical world is mind-blowing and we are only beginning to learn how to use it.

Artificial intelligence and the data-rich deluge

The IT-OT convergence thrives on data which also lies at the heart of the ongoing digital transformation. Industrial data is radically increasing in value and volume, with the analytics market experiencing an annual growth rate of 15%. On-premise (edge analytics) yearly growth rate is forecasted to grow by nearly 25% by 2028.

While machine learning, a subset of artificial intelligence, is ideal for edge analytics (an emerging technology closer to the data source and expected to ease the load on cloud servers), the machine learning system must be trained to recognize patterns and learn over time.

Artificial intelligence, on the other hand, moves beyond pattern recognition to become a sophisticated decision-making tool. It integrates hierarchical data models to predict system behaviors, optimize costs, and forecast demand. Artificial intelligence (AI) engines discern patterns autonomously to understand not just what is happening now but foresee what could happen and make real-time decisions based on intricate, layered data.

Companies like Schneider Electric and AVEVA are making steady progress in this area.

As companies install next-generation, open standard, smart RTUs at remote sites, these devices offer better edge capabilities and provide a dedicated platform for advanced edge services. SCADAPack 470i and 474i RTUs, for instance, provide a secondary Linux processor to augment the primary traditional control logic processor.

This second processor allows for the secure use of open-source solutions including Node-RED, Python, and other open-source applications to build advanced solutions. Those solutions can include tech like machine learning and advance analytics. Both can simplify the design of remote telemetry networks, concentrating data from remote sites into smaller, easily communicated packages. So, you get all the relevant data you need without the heavy demand on the communication network.

The cybersecurity imperative: IT is in the driver’s seat

The growth of edge analytics and AI-powered IT-OT convergence has not been without challenges. It is now driven by the pressing need for enhanced cybersecurity.

This is particularly relevant for OT systems designed to remotely operate critical infrastructures like water supply and gas pipelines, where a lack of security can leave these systems vulnerable. The 2021 Colonial Pipeline ransomware attack and the Florida water pump station cyberattack highlighted the vulnerability of this critical infrastructure. The attacks disrupted services and raised serious concerns about the susceptibility of vital infrastructures to cyber-attacks, leading to new regulations in the industry.

The need to secure these systems often leads organizational heads to their IT departments, which traditionally manage security matters. This has accelerated IT-OT convergence, mainly as there is a global need to secure critical infrastructure as more businesses connect their OT systems with IT infrastructure. Businesses are responding with solutions that fit the IT space more than the industrial automation or the operation space. In turn, it accelerates IT secure technology adoption inside of RTUs such as the 470i and 474i

Cyberattacks exist in the digital space, but they now have very real effects on the physical world.

The future beckons

As we navigate this intricate digital maze, three crucial challenges become known, guiding our journey into the heart of IT-OT convergence:

• Autonomous operations: Most industries are aiming for complete autonomy by 2030 – affecting decision-making processes and physical tasks. This change from industrial automation to autonomy will reshape our infrastructure and industrial plants. Imagine an AI system that can leverage a deep understanding of variables and autonomously orchestrate operations.

• Shifting control to the Edge: There is a greater acceptance of remote operations and edge computing for control, optimization, and data-shaping. Edge computing allows for data aggregation and enhances the value of systems like AVEVA PI. Such solutions can transform data into narratives that provide deeper operational insights, fast analysis, and expanded visibility of assets. This allows companies to operate more efficiently and sustainably.

• Generational change: The accelerating retirement of the baby boomer generation signals a need for rapid knowledge transition. Digital natives – the generations that have grown up immersed with computing technology – are taking over. With the lineage of the past, the pace of innovation, and the need for new skills, traditional barriers are falling. New systems driven by edge analytics and AI will continue to remove barriers to innovative approaches.

Learn more about artificial intelligence

The way we structured systems five years ago is completely different today and will be much different five years from now. AI, machine learning, and edge analytics are integral to today’s industries. These technologies are poised to fundamentally reshape system architectures, expand computational capabilities, and drive innovation.

As cybersecurity improves and human-machine collaboration deepens, it will undoubtedly lead to breakthroughs in other fields. Adapting to this evolving landscape will be key as these advancements become essential to our industries and infrastructures. To learn more about SCADAPack 470i and 474i and its profound impact on operational resiliency and workforce efficiency, visit the SCADAPack website. Discover how AVEVA PI has been built specifically to overcome the challenges of AI in industrial environments.