by Heiko Claussen, Co-CTO, AspenTech
As we approach 2025, asset-intensive industries face a pivotal moment where integrating advanced technologies, like Artificial Intelligence, has become essential. The AI market reflects this shift, surging to over US$184 billion in 2024, according to Statista, and projected to surpass US$826 billion by 2030.
Industrial AI, a tailored subset of AI, blends domain expertise with targeted algorithms to deliver energy-efficient and transparent solutions. Unlike broad, resource-intensive models, industrial AI focuses on smaller, efficient approaches.
For example, in manufacturing, predictive maintenance uses targeted algorithms and physics-based models to monitor critical equipment efficiently. Similarly, in chemical production, hybrid models optimise reactor performance by focusing on key variables like temperature and pressure, reducing waste and energy consumption.
With tightening sustainability targets, industrial AI offers a responsible way to guide decision-making while minimising infrastructure demands. It transforms complex operations, spanning infrastructure, manufacturing, and supply chains, into adaptive, data-driven environments. Grounded in engineering fundamentals and operational insight, industrial AI enables industries to meet evolving expectations while staying competitive in a world focused on cleaner, more efficient practices.
How industry trends will play out
Looking ahead to 2025, three predictions highlight how Industrial AI will reshape the industrial landscape. These themes all share a focus on reliability, efficiency, and the pursuit of a competitive edge.
First, “AI adoption will accelerate.” Certain asset-intensive sectors, from oil and gas to heavy manufacturing, have been traditionally cautious, placing paramount importance on reliability and safety. In the past, this conservative mindset slowed the uptake of new technologies.
As operational complexity grows, however, driven by shifts in market demand, more intricate supply chains, and compliance requirements, these industries are now searching for tools that can help them adapt swiftly, and provide robust and trusted results. Industrial AI, rooted in the physics and engineering that govern operations, allows companies to learn from patterns in large volumes of process data.
This approach doesn’t just streamline operations; it provides decision-making confidence grounded in real-world constraints. Decision-makers, recognising that these AI models work within known engineering boundaries, are increasingly seeing AI as an ally rather than a gamble.
Second, “Industrial AI will gain attention for its energy efficiency benefits.” Concerns about AI’s intensive energy use have risen as large, generic models strain infrastructure. In contrast, industrial AI narrows its focus to fit-for-purpose solutions that rely on smaller, more efficient models. By incorporating established engineering principles, it can achieve accurate insights without the immense computing power associated with large language and other foundation models.
The result is a reduction in both energy consumption and environmental impact – key goals for organisations under pressure to show their commitment to sustainability. Industrial AI solutions are not just meeting operational demands; they are doing so in ways that inherently limit waste, inefficiency, and unnecessary emissions. For industries focused on optimising the value chain and meeting rigorous environmental standards, this efficiency represents a crucial step forward.
Third, “Executives will embrace the disruptive power of AI to establish a competitive edge.” In recent years, some executives approached AI with scepticism, concerned about unpredictable outcomes, opaque decision-making, and the difficulty of validating results. Industrial AI changes this narrative.
By integrating domain expertise and engineering fundamentals into AI models, companies can establish guardrails that prevent unrealistic or unsafe recommendations. Instead of a black box, executives gain a transparent tool capable of delivering trusted results.
This shift gives leaders the confidence to invest in AI solutions that directly align with strategic objectives, improving operational resilience, reducing downtime, managing resource allocation more effectively, and ultimately driving better margins. It is a practical approach that combines trust, explainability, and tangible outcomes.
The common thread uniting these predictions is the recognition that AI must work within the real-world parameters that matter most to industrial operations. Engineering knowledge, scientific principles, and established safety standards form the bedrock of effective AI applications.
Industrial AI respects these boundaries, using them not as constraints that limit possibility but as essential guides that ensure outputs are reliable and actionable. The result is an environment in which advanced analytics and automated decision support enable more efficient operations and more responsible resource use.
Looking ahead positively
As these trends unfold, the value of industrial AI will become evident across the full range of asset-intensive industries. Whether it’s refining production processes, optimising maintenance schedules, improving reliability across complex systems, or meeting strict environmental regulations, Industrial AI solutions offer a balanced and holistic approach. They deliver improved results without compromising safety, transparency, or sustainability.
In essence, 2025 heralds a new age of progress for industries that have traditionally valued careful, deliberate change. By selecting AI solutions that incorporate engineering fundamentals and align with evolving energy and environmental priorities, forward-looking organisations can embrace AI confidently. The years ahead will favour those who harness Industrial AI to create more sustainable, efficient, and resilient operations, positioning them to thrive amid shifting market realities and more stringent sustainability goals.
