Artificial intelligence now sits on both sides of the climate equation, both problem and solution. Industrial AI is being hailed as the tool that can help solve the crisis, optimizing energy grids, streamlining factories, mapping deforestation, and accelerating carbon removal. Yet AI itself is also rapidly becoming one of the biggest new sources of demand for power and water.
The International Energy Agency warns that data-center electricity use, driven largely by AI training and inference, could double by 2030, consuming as much power as some mid-sized nations. A 2025 UK government report, AI’s Thirst for Water, projects global data-center water use will soar from 1.1 to 6.6 billion cubic meters by 2027, threatening already-stressed basins.
Even hyperscalers are feeling the heat. Amazon Web Services’ 2025 sustainability report details an urgent shift to recycled and reclaimed water to offset the surge from AI cooling. Researchers at the University of Oxford’s Environmental Change Institute call this expansion a new form of systemic climate risk, locking in fossil backup generation and regional water strain unless efficiency breakthroughs are achieved.
The good news: those breakthroughs are within reach. A UNESCO–UCL study finds that smarter model design and use could cut AI’s energy use by up to 90%. The real question isn’t whether AI helps or harms the planet, it’s which kind of AI we choose to build.
The fastest-growing, and most overlooked, frontier isn’t in consumer chatbots or image generators but in the control rooms of industry, where AI is already cutting emissions, saving energy, and turning sustainability into competitive advantage.
Generative AI may dominate headlines, but industrial AI, which fuses artificial intelligence, IoT, and semantic digital twins, is already delivering measurable value in factories, energy grids, transport hubs, and water systems.
These systems drive operational efficiency, resilience, and decarbonization, often returning investment in months rather than years. Valued at just $4.35 billion in 2024, the industrial AI market is forecast to increase fortyfold by 2034. That shift, from generating words to generating measurable value, is what marks the real industrial AI revolution.
From Words To Watts
For Nick Tune, chief executive of UK-based Optimise AI, the real breakthrough that AI provided didn’t lie in content but in context. He told me, “Data is often fragmented and siloed in buildings. By utilizing semantic data models we structure the data in a relational basis; this provides meaning and context to each data point. This in turn provides the opportunity to deliver cause-and-effect analysis and enables utilization of machine learning to provide in-depth and accurate analysis and actuation.”
That structure turns static information into live, actionable intelligence. One Optimise AI client used its semantic digital twin to automate lighting across railway platforms based on occupancy and lux levels. The result was energy savings of up to 40% energy savings, achieved in a fraction of the time and cost of traditional audits. “It can take 10 minutes to get insights into the performance of buildings via Predict versus weeks in an analogue approach at a fraction of the cost,” Tune explains.
Caspar Hertzberg, chief executive of industrial software leader Aveva, sees the same pattern at scale. He explained in an interview that large language models are beginning to interpret industrial and time-series data, turning analytics into decision support rather than replacement. In other words, the next frontier for generative AI isn’t more words, it’s watts saved and downtime avoided.
Why Industrial AI Still Needs Humans
Arthur D. Little’s Smart & Secure report shows why adoption is accelerating. Predictive maintenance now achieves up to 90% accuracy, freeing staff from routine inspections. Real-time analytics can cut decision latency by 60%, giving operators instant insight into changing conditions. Cross-functional teams blending IT, OT, and sustainability skills are becoming the default model for AI-driven transformation.
The key is workforce augmentation, not replacement. “The issue is currently most facility managers deliver based on the experience they’ve amassed over many years, not necessarily on data,” says Tune. “New entrants don’t have that experience but can use tools like Optimise to create a human-in-the-loop system, person and machine working together. The human’s role shifts to oversight, which in turn increases productivity.”
Hertzberg argues this is not just a productivity story but a demographic one – he observed that across major markets, a shortage of skilled operators and engineers is driving the adoption of AI tools. The demographic cliff is steepest in mature industrial economies such as Japan, Germany and the U.S., but emerging markets face a skills mismatch rather than outright aging. In both cases, AI-enabled decision support systems are being deployed to preserve operational capacity and institutional knowledge as human expertise thins out.
That framing turns the automation debate on its head: industrial AI is less about replacing people than about keeping essential systems running when skilled labor is scarce. With half of plant operators nearing retirement, recruitment and training are as urgent as operational efficiency. Lisa Wee, head of sustainability at Aveva, noted in an interview that immersive digital twin simulations can make industrial training engaging and intuitive for younger workers, drawing on visual and interactive cues familiar from gaming.
From Efficiency To Resilience
The same data infrastructure that cuts costs today can buffer shocks tomorrow. In a world of climate volatility, energy shocks, and tightening regulation, industrial AI is also being deployed for resilience, predicting overheating risks in buildings before they happen, rebalancing electrical loads in stressed grids, and adapting systems rapidly during extreme weather.
Tune sees huge potential here saying, “It’s important to utilize digital twins with ML/AI to predict how assets are affected by climate change and then semi-autonomously adapt. For the first time ever, we have the technology – semantics to make sense of the data and ML/AI to learn – that can deliver this.”
Hertzberg argues that resilience is now as much about information flow as physical redundancy, explaining that industrial AI is beginning to connect systems that once operated in silos – such as generation, distribution, and consumption – allowing organizations to respond faster and more intelligently to changing conditions. That capacity to see across silos may become the ultimate resilience metric.
From Reporting to Accountability
The next wave of industrial AI isn’t about dashboards or disclosures but about proof. “Currently, scope 1 and 2 (and scope 3 from a supply-chain perspective) are supported by AI tools,” says Tune. “We’re expanding into further ESG areas such as water, waste, and embodied carbon. The opportunity is to semi-autonomously deliver ESG outcomes, not just report on them.”
Hertzberg noted that while the public debate around ESG has become more polarized, the underlying drive toward digital efficiency and sustainability hasn’t slowed. Many firms, he said, are now pursuing the same outcomes under the banner of operational excellence and reliability. turning documentation into delivery and credibility into advantage.
For Moritz Kandt, co-author of Smart & Secure, the commercial benefits extend beyond cost savings. He observed that real-time modeling of assets can strengthen stakeholder confidence in companies’ sustainability performance, turning documentation into delivery and credibility into advantage.
Even in traditional energy sectors, industrial AI is being used to reduce flaring, optimize production efficiency, and lower the emissions intensity of existing assets, buying time while the energy system transitions.
At the same time, the digital twins driving efficiency in operations are also shortening innovation cycles in materials science. Wee pointed to low-carbon cement, steel, and plastics, where AI and digital twins are being used in materials innovation, allowing teams to model and test different variables virtually (like energy efficiency or composition, to process settings) before building or scaling physical prototypes. The cost savings are significant, but the strategic advantage is greater: faster market entry with validated sustainability performance.
Securing The Industrial AI Stack
As industrial systems become more connected, cyber risk rises. Tune outlines Optimise AI’s approach, “Source code is hosted on GitHub in private repositories; two-factor authentication is required for all who have access. All services are configured behind Azure firewalls and tested before deployment. We operate under Cyber Essentials Plus certification.”
For executives wary of linking critical infrastructure to cloud-based intelligence, such architecture and governance are becoming non-negotiable. Hertzberg acknowledged the critical nature of security. He explained that cybersecurity and trust are integral to industrial operations, emphasizing that as AI becomes embedded in operational decision-making, security and reliability become core to resilience.
The Next Chapter: Generating Real-World Value
While many jobs will be reshaped by AI, the biggest productivity gains won’t come from chatbots but from systems-level intelligence that manages physical assets as deftly as language models process words, and that’s where industrial AI is already delivering.
The first phase of AI adoption dazzled with novelty. The next will be judged by tangible results: cleaner production, stronger materials, safer operations, and smarter, more resilient systems.
Ultimately, the future of AI shouldn’t be about scaling power consumption but scaling intelligence in service of resilience. Industrial AI will only represent true progress if it enables industries to decarbonize, close resource loops, and stay within planetary boundaries