“AI is in a ‘golden age’ and solving problems that were once in the realm of sci-fi.” (Jeff Bezos)

Yet, views around AI are polarising. Jim Covello from the Goldman Sachs Group called out examples with dot-com companies in the late 1990s and more recently, with cryptocurrencies. He added that it will likely happen with AI at some point. However, Joseph Briggs from the same firm estimates that AI will automate a quarter of work tasks and boost economic growth. 

Whether you are a proponent or opponent of AI as the next big thing, it is key to note the near-term investment themes cited by Dong Chen, Pictet Wealth Management. Of the three trends cited, two trends allude to the promise of AI and the industrial sector – linked to drivers, such as electrification, decarbonisation and digitalisation. 

Industrial AI is the key to energy transition, as it propels companies towards operational excellence. With built-in guardrails to ensure plant operations run safely, AI models can also help companies drive efficiencies, track equipment health and optimise sustainability. 

In the man versus machine debate, Industrial AI is not aiming to replace workers in a complex and safety-conscious world of process manufacturing sites. AI can solve some, but not all the problems. A key opportunity for asset-intensive industries is that AI provides realistic and correct recommendations. Operators and engineers remain in charge by ensuring the validity of the output of AI, relying on that the technology remains transparent and accountable. 

Industrial AI stems from combining AI with models, based on engineering fundamentals with crucial domain-expertise driven guardrails around AI applications. Results follow the scientific laws i.e., of chemistry, physics, electricity, and geoscience, ensuring that outcomes are safe. Industrial AI works around the premises of agility, guidance and automation – in particular, as an expert co-pilot and advisor to help workers make better, more strategic decisions, faster. 

In the next two decades, sustainability will be top of mind for asset-intensive industries, as companies work through the energy transition.

To optimally capitalise on Industrial AI, companies should factor in the following considerations to build a supportive culture. 

First, organisations must conduct a comprehensive assessment of their operations to identify areas with the highest potential for improvement. For example, companies can start by analysing energy-intensive processes and pinpointing inefficiencies. Industrial AI can impact predictive maintenance, where it anticipates equipment failures and optimises operational schedules to reduce downtime and energy use. In supply chain optimisation, industrial AI can help lower emissions and costs across asset-intensive processes. All of which leverage AI capabilities in data analysis and process optimisation to achieve significant sustainability gains.

By using predictive analytics, business can forecast energy demand and optimise usage. AI can also monitor emissions in real-time, identifying excessive sources and suggesting corrective actions. Machine learning models can analyse production processes to improve efficiency and reduce wastage. AI-driven simulations can explore operational scenarios, enabling companies to adopt strategies that minimise their environmental impact. By continuously learning and adapting, AI systems help businesses stay on top of their sustainability goals and dynamically adjust their operations for better environmental performance. Regular audits and updates to AI models ensure that the latest data and trends are incorporated.

Second, companies should be aware of potential challenges in deploying Industrial AI solutions. For example, there can be complexity in integrating AI into existing operations and ensuring compatibility with legacy systems. Data quality and availability can also present issues. While in the case of AspenTech, it is possible to look beyond hard to come by big data from the field, as available first principles models can generate simulation data on-demand, this is not the norm. Other exceptions include AI methods, such as reinforcement learning that do not need historic data but continuously learn during operation. That said, AI systems often benefit from significant amounts of high-quality data, which may not be always readily accessible in a ready-to-use format. Companies also need to mitigate data privacy and security, particularly when handling sensitive operational data. Careful planning, investment in training and a commitment to continuous improvement are required. 

Third, Industrial AI can revolutionise key areas of business operations, delivering significant impact through enhanced agility, guidance, and automation. As such, AI-driven agility allows organisations to swiftly adapt to changing business conditions, optimising value creation. In an industrial setting, model sustainment reduces the gap between simulations and reality, improving efficiency and minimising waste. This ensures that businesses can respond rapidly to market demands and operational changes. AI also levels up the workforce to guide them daily and make complex decisions faster. Industrial AI-led software can assist new employees in understanding system optimisations and support experienced staff in managing complex processes. Such systems not only accelerate decision-making, but also improve accuracy in daily and complex scenarios via a systematic approach. 

AI-powered automation frees engineers from routine tasks, allowing them to focus on higher-value activities. One practical application is the automatic segmentation and interpretation of subsurface image volumes. This enables businesses to efficiently identify optimal locations for CO2 storage, driving sustainability efforts. In fact, the ability of Industrial AI to enhance agility, provide insightful guidance, and automate routine tasks significantly boosts operational efficiency and strategic decision-making, positioning businesses to achieve greater success and sustainability. 

Fourth but not least, AI plays a crucial role in embedding industrial sustainability into a corporate strategy by providing data-driven insights that inform decision-making. AI-powered tools can evaluate the environmental impact of various business activities, allowing companies to make informed choices that align with their sustainability goals. To support decision-making processes, Industrial AI can help prioritise initiatives that offer the greatest sustainability benefits, balancing environmental, social, and economic considerations. For stakeholder engagement, AI can enhance transparency and communication by providing real-time data on sustainability metrics and progress towards environmental, social and governance (ESG) targets. AI can also facilitate the identification and management of risks related to environmental and social factors, ensuring that companies can proactively address these issues. 

By integrating AI into their strategic framework, businesses are positioned to drive and communicate their commitment to sustainability more effectively. Finally, AI can also help to track regulatory changes and ensure compliance, further embedding sustainability into the company’s core operations.

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Dr Heiko Claussen is Senior Vice President & Co-Chief Technology Officer at Aspen Technology, leading the company’s artificial intelligence research & technology strategy. Prior to Aspen Technology, he was Head of Autonomous Machines and Principal Key Expert of AI at Siemens. During his 15-year tenure at Siemens, he worked in many areas related to AI and digitisation, including remote monitoring, machine learning, robotics, pattern recognition and statistical signal processing. Heiko has been named Inventor of the Year twice at Siemens – in 2016 for the development of a virtual sensor to monitor the flame status of gas turbines, and in 2019 for the development of a neural net accelerator for industrial control systems.