Industrial AI is a subset of the broader field of artificial intelligence (AI) and refers to the application of AI technologies in industrial settings to augment the workforce in pursuit of growth, profitability, more sustainable products and production processes, and operational excellence. Industrial AI leverages machine learning, deep learning, neural networks, and other approaches. Though some of these techniques have been used for decades to build AI systems using data from various sources within an industrial environment, advancements in computing and analytics have vastly improved their capability.
Industrial organizations that have already begun leveraging AI are seeing transformational improvements, not simply in operational use cases from a systems viewpoint but also from a People and Process perspective, across internal cultures and external ecosystems. AI is becoming central to how industrial companies can address and react to a host of disruptive market conditions.
Foremost among these is the issue of energy transition and industrial sustainability (ETIS), consistently cited by industrial executives as one of their top two business challenges. To improve, decision-makers need a strategic approach to understand the value of AI in meeting ETIS challenges, particularly from an operational perspective. This ARC Strategy Report builds upon ETIS-specific use cases identified in ARC's Industrial AI Impact Assessment Model. This model offers a structured approach to assessing and planning for AI impacts in product design, supply chain, production, sales and service, and workforce management.
Looking at the desirability of such transformation, it is important for leaders to recognize the role AI can play to achieve ETIS transitional improvement and, more importantly, transformational changes.
Overcoming the “Impossible”
A host of developments have aligned to make sustainability issues, particularly the push to decarbonize and reduce industrial emissions, an increasingly core part of measuring industrial business performance. Multiple governmental and scientific entities have pointed out the irrefutable degradation of earth’s climate systems and demonstrated via data the link to emissions and society’s heavy reliance on carbon-based fuels.
Collectively, countries are attempting to better understand industrial carbon footprints with the goal of reducing them in the short and long term. The financial community is also rethinking investment patterns, as it views climate disruption as a risk to the global financial system. This result has been the enactment of regulations, legislations, and an array of reporting frameworks that is as dizzying as it is uneven, in terms of requirements.
To improve while complying with these requirements, industrial companies must change many internal and external aspects of their business too. Because of the breadth and complexity of change required, and the timeline across which it must occur, the decarbonization shift often appears inconceivable. Yet history has provided examples of how seemingly impossible change can occur through technological innovation. AI is now poised to take its place as an innovation lynchpin when applied to sustainability.
If leveraged correctly, AI will be a key element of the solutions needed for ETIS success. Achieving the long-term decarbonization and sustainability goals set by many non-standardized and disparate organizations will require major R&D, design, operational, and other breakthroughs beyond what we can currently imagine.
Major energy infrastructure improvements, like major technological advancements in AI, require massive investments in new manufacturing capabilities, supply chains, and infrastructure. They take time. And they have an energy footprint and other fixed and variable costs. They also can face setbacks. Whether a trillion-dollar carbon capture infrastructure or a global high voltage transmission system to interconnect renewables across the planet, good things that seemed inconceivable can get done.
Software is key to sustainability. As the impact of climate change increases on global economies, health, and geopolitics, software is seen as perhaps one of the most critical tools for industrial companies to achieve more sustainable operations. This is particularly true for AI. However, this technology has the unique role of being both a boon and bane for industrial companies when considered through the lens of stakeholders dealing with sustainability.
Industrial AI and Decarbonization
The accelerated pace of innovation and change associated with both AI and ETIS are remarkably similar. On the surface, both are sources of a lot of excessive focus in one area (corporate reporting for sustainability and generative AI as recent examples) at the expense of a clearer big picture. This emphasis has resulted in unproductive hype and knee-jerk reactions. Such reactions are our natural responses to big transitional events in human history, events which are of such great consequence, it is hard to conceptualize what changes lie ahead.
However, once past the hype, it becomes clear that the two are inextricably linked. Success (or failure, for that matter) in sustainability is dependent on a company’s capacity to leverage AI effectively. Understanding this relationship is key to enabling companies to utilize AI technology to provide transparent and demonstrable improvement in sustainability performance.
In recent market research conducted by ARC, creating an AI strategy was identified as the most critical technology priority. Also, more than 35 percent of survey respondents cited AI as the most impactful technology that will change manufacturing over the next five years, more than 3 percent higher than the second technology, the cloud.
Additionally, when considering that survey results, it is also helpful to understand the challenges associated with ETIS, according to similar research. For industrial executives, the number one challenge in ETIS is finding the right technologies, followed by measurement and performance, business value, and lack of clear strategies.
In looking at the potential relationship of the two, the application of AI solutions to ETIS challenges is already occurring and will inevitably become stronger. The breadth, operational depth, real-time dynamism, and complexity of the data needed to report just on sustainability performance is well suited to the capacity of AI to consume, organize, and visualize. AI can address the reality that no one system can manage all the processes and data associated with sustainability, particularly as related performance metrics continue to evolve.
The ability of product lifecycle digital twins and their AI models to reduce risks is of vital importance to enable energy equipment suppliers and their infrastructure customers to create larger collaborative frameworks, where uncertainties about future forks in energy supply infrastructure scenarios are comprehended in contractual ways which keep the needed flow of R&D and O&M pipelines funded. Project and performance digital twins will also ensure an optimized plant lifecycle sustainability performance across design and build, operate and maintain, and mindful decommissioning.
AI can sit on top of, be embedded into, or connect to systems, adding purpose, accuracy, and speed to decisions related to sustainability. In that way, it enables the realization of a cornerstone concept necessary to manage energy and sustainability issues ― dealing with data within an industrial system-of-systems.
AI as Carrot and Stick for Transparency
Because of its ability to deal with data accurately and quickly, AI presents industrial organizations with a path forward for demonstrating the trustworthiness of their approaches. This will become increasingly apparent as companies are required to move beyond traditional rollout and estimation techniques, which tend to be accounting-centric and often lead to complaints of greenwashing.
AI’s capacity to deal with large, unstructured, and dynamically changing data sets, such as those often seen in industrial operations, will enable these companies to increase the sophistication and accuracy of their reporting. This shift will enable companies to demonstrate their brand effectiveness more quantifiably to consumers showing a growing preference to back sustainability via their purchasing. Supply chain transparency and partner decisions will improve as data is shared across operations ecosystems using AI applications. In these ways, AI provides industrial companies step-change capabilities to demonstrate improved sustainability performance.
Despite the transformational benefits AI provides, it is also likely to become a stick by external organizations focused on holding industrial companies to increasingly stringent sustainability performance requirements. From vision systems and machine learning monitoring emissions to product design models for carbon footprint tracking, AI is uniquely suited to the data challenges inherent in compliance.
For investment and boardroom accountability, AI is replacing humans in poring through the immense data available to track sustainability. This includes novel applications of the technology in sustainability, such as sentiment analytics, in capturing issues that would have previously gone unreported and relatively unnoticed. Shareholders and the financial community will be provided with more quantifiable means to conduct brand performance comparisons that drive investment decisions. Supported by these AI tools as part of the measurement process, asset investors will be able to shift capital more accurately and quickly to companies and industries with lower sustainability risk.
In the long-term, AI will be a leveler for industrial companies and those measuring them. Given the enormous complexity inherent in sustainability, when used correctly, AI provides a means to determine if any action or set of actions quantifiably improves sustainability performance. In this way, it is both the carrot and the stick.
Table of Contents
Industrial AI and Decarbonization
Industrial AI in a System of Systems
Industrial AI and ETIS Maturity
Examining Select Use Cases Across Industries
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