August 30, 2024 Human-Centered AI Will Shape the Future of Operations

AI is more than a buzzword—it’s transforming industries and unlocking new levels of efficiency and sustainability through human-like AI.

By Jim Chappell, Global Head of AI at AVEVA

Artificial intelligence (AI) is often touted as a game-changer for businesses, but its impact is much more complex and multi-faceted. AI encompasses a broad range of technologies, including various types of machine learning such as predictive analytics, deep learning, reinforcement learning, and recently, generative AI powered by Large Language Models (LLMs)

Once AI technologies reach their full potential, they will surpass the internet in their capacity to revolutionize our lives, completely influencing the way we manage and optimize business operations. Entire plant systems will be reimagined, with AI-driven solutions autonomously optimizing, strategizing, and executing tasks around the clock. While this vision is not yet fully realized, we are already witnessing the culmination of decades of AI research, alongside the humanization of AI through LLMs.

Today, these AI-powered technologies are already helping organizations reduce fuel consumption, enhance carbon capture, lower emissions, and extend equipment lifecycles—all while improving sustainability, efficiency, and profitability.

How are they achieving this?

Around the globe, companies are already reaping significant sustainability and productivity gains with the application of industrial AI. For instance, U.S. energy giant Duke Energy saved over $250 million by implementing predictive models that identified issues early, reduced errors, delivered faster outcomes, and improved ROI.

This AI-powered software is enabling industrial businesses to accelerate engineering processes through predictive design and simulation, maintain peak production efficiency, offer decision-making support to connected workers, minimize waste for higher yields, and detect potential equipment failures before they happen.

Integrating AI Across the Network

Many people view AI primarily through the lens of Generative AI (GenAI), but GenAI is just one aspect of a broader AI landscape that has been evolving for decades. As the amount of available data continues to grow, the true potential of AI is becoming clearer, with new large language models (LLMs) and GenAI playing a crucial role in this evolution.

Today’s experts are refining these mathematical algorithms and interweaving various strands of knowledge to develop solutions, or parts of solutions, tailored to industrial businesses. I refer to this as “AI infusion” – embedding AI capabilities throughout all aspects of operations, including engineering design, simulation, maintenance, the value chain, and data management.

With the advent of expansive LLMs, we’re poised to witness a significant leap in operational efficiency, where even the most obscure data becomes accessible and intuitive for users. As AI becomes more human-centric, users will extract greater value from their data and, ultimately, from all their activities, including software interactions. This transformation may redefine the user experience with computers.

The next major advancement will come from integrating various AI types and applications, enabling workers to perform at their fullest potential. As technology evolves, we will witness a more seamless collaboration between humans and AI, driving maximum progress.

This evolution is already evident with the rise of digital twins—virtual models of physical objects, systems, or factories, created using data from Internet of Things (IoT) devices, advanced computing, and digital processes.

Moreover, by incorporating large language models (LLMs) into the network, AI technology can evolve from being a mere tool to acting as a collaborator or assistant.

For instance, by combining their company’s industrial data with a large language model (LLM), users could easily ask the system natural language questions with minimal setup. They might inquire about specific objectives, such as  “Why is my compressor performing worse now than last week?”

By integrating Generative AI (GenAI) and other AI types with LLMs, users gain access to specialized knowledge in areas they may not be familiar with, like maintenance manuals or 3D design drawings. Ultimately, this will enable workers and executives to perform their roles with greater efficiency and ease, while ensuring that operations run at optimal and sustainable levels. In other words, this is the vision for Industry 5.0. AI-driven solutions will conduct hybrid semantic searches, continuously identifying and linking relationships across various types of data.

We are swiftly transitioning from narrow AI to general AI, where software will possess capabilities that are increasingly human-like. As this trend continues, AI will become more goal-oriented, utilizing all available resources to achieve its objectives. However, AI is not meant to replace human intelligence but should be seen as an empowering partner. It is set to evolve from today’s supportive software into a collaborative tool that works alongside humans—workers who are seamlessly connected to technology through intuitive interfaces.

In the not-too-far future, a plant manager might simply ask, “Can you reduce my factory’s carbon footprint? I allow you to take any ethical and legal actions necessary.” This command could initiate a series of actions, such as adjusting set points, sending emails and checking assets for efficiency issues. While we’re not quite there yet, most of the foundational elements are in place, so stay tuned.

About the Author:
With over 30 years of experience in the industrial software sector, Jim Chappell is currently Global Vice President and Head of Artificial Intelligence (AI) & Advanced Analytics across AVEVA’s business units and products.  Prior to his current position, he led the Asset Performance Management (APM) suite of products and related engineering/analytics services for Schneider Electric.  He was also a founding partner and managing officer of InStep Software, a global leader in industrial AI-driven Predictive Analytics and Big Data software, which was acquired by Schneider Electric in 2014.

Jim holds a B.S. in Nuclear Engineering from Rensselaer Polytechnic Institute (RPI) in Troy, NY, a M.S. in Nuclear Engineering from the Naval Nuclear Power School in Orlando, FL, and a M.B.A. from Chaminade University in Honolulu, Hawaii.  In addition, he graduated from the Civil Engineer Corps Officer’s School (CECOS) in Port Hueneme, CA.  He also held a top-secret clearance while an officer in the U.S. Nuclear Navy.

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