Siemens: Industrial AI Can Deliver Marked Green Improvements

Industrial AI is rapidly becoming a core enabler of measurable sustainability outcomes, from energy savings to carbon reductions, across sectors aiming for net zero.

As organisations scale from pilots to full deployment, the technology is shifting from experimentation to a critical part of infrastructure for decarbonisation and resource efficiency.

AI and sustainability now sit among the leading strategic priorities and pressures for businesses worldwide, and the two are increasingly reinforcing each other as expectations rise.

With the majority of organisations targeting net zero around the 2040 horizon, industrial AI is emerging as essential to tackle decarbonisation at the necessary pace and scale.​

Reflecting this, Siemens has worked with Reuters Events on a report, From Pilots to Performance: How Industrial AI is Helping to Scale Sustainability Impact, based on insights from more than 260 senior sustainability leaders.

The research underpins AI Magazine’s exploration of how industrial AI is progressing from exploratory projects to proven impact.

What the data shows on AI and sustainability

The report indicates that almost two-thirds of organisations have moved beyond proof-of-concept into targeted use, moderate adoption or broad rollout of industrial AI focused on sustainability outcomes.

These deployments span both operations and product development, signalling that AI is being embedded across value chains rather than in isolated initiatives.​

This scaling is translating into tangible environmental benefits, with many organisations reporting significant energy savings and reductions in carbon emissions from AI-enabled optimisation.

Year-on-year improvements since 2024 suggest that as implementations mature, the sustainability return from industrial AI is strengthening.​

Eva Riesenhuber, Global Head of Sustainability at Siemens, says: “Climate change, biodiversity loss, population growth require customers to embrace energy transition, circularity transition and societal changes at the same time.

“The complexity of juggling global interconnected system transitions in times of major disruptions can only be mastered with AI.”

Energy management as a leading use case

Energy management has emerged as the most advanced application area, with a large share of organisations already deploying industrial AI solutions to optimise energy use.

For many respondents, this is seen as the primary way AI will help them meet their sustainability objectives, as it directly links to emissions and cost.​

Practical examples illustrate the benefits. Dutch grid operator Alliander, which serves millions of customers, is using Siemens’ Gridscale X software to create a digital twin of the electricity network, enabling higher grid utilisation without immediate physical reinforcement and supporting the integration of more renewables.

In Estonia, Greenergy Data Centers has installed Siemens’ AI-powered White Space Cooling Optimisation system, which uses dense sensor networks and machine learning to fine-tune cooling in real time and significantly improve energy efficiency.​

Kert Evert, Chief Development Officer at Greenergy Data Centers, says the environmental impact was immediate: “When we first launched the system, it improved our efficiency by approximately 30% at the push of a button,” he says.

“But this was just the beginning, because the system learns, adapts and improves over time.”

Circularity, resources and maintenance

Beyond cutting energy use, industrial AI is playing a growing role in resource efficiency and circularity strategies.

Many organisations are applying AI to optimise consumption of materials and enable smarter waste management, helping to keep resources in use for longer.​

Predictive maintenance is now widely adopted and is central to this shift, as it extends asset lifetimes, reduces material throughput and minimises unplanned downtime.

By intervening before equipment fails, companies avoid scrap, unnecessary replacements and associated emissions.​

How AI can help with sustainable design

At the product level, AI is allowing sustainability metrics to be built into design from the outset rather than bolted on later. Generative design tools are being used to explore configurations that reduce material use, weight and embodied carbon while maintaining performance.​

Eryn Devola, Head of Sustainability at Digital Industries, a division within Siemens, says AI tools are making sustainable design practical without overwhelming engineering teams.

“It’s now easier to say, ‘While we’re working on this design, let’s also address resource efficiency and carbon footprint.’ Today, we can model these factors and embed them into decision-making to achieve the right trade-offs without adding major effort for engineering."

“And we can go further: since we’re already touching the design, we should also explore how to dematerialise, reduce size, increase modularity, etc.

“These are all key factors that contribute to creating a truly sustainable product for the long term,” she says.

Siemens highlights this with lightweight robot grippers made from a lower-carbon polymer, which are significantly lighter and emit far less CO₂ across their lifecycle than traditional metal equivalents.

These components have enabled upgraded production lines that use much less energy and avoid several tonnes of additional CO₂ emissions.​

Automation Innovation, a specialist in glass production equipment, shows how AI-driven analytics and digital twins can transform material and energy footprints.

By optimising its mould cleaning process with Siemens solutions, the company has cut raw material use by hundreds of thousands of tonnes annually, reduced on-site energy consumption by around 30 percent and eliminated harmful chemicals, while avoiding close to a billion kilograms of CO₂ emissions.

Rising confidence in AI's ability to contribute to sustainability

Confidence in industrial AI’s ability to accelerate the energy transition has grown sharply, with a rising share of leaders expecting a high or medium positive impact. Separate Siemens research, such as the Infrastructure Transition Monitor, reinforces that many organisations are now actively using AI to decarbonise operations.​

Brooke Tvermoes, Director of Climate, Energy and Environment at IBM’s Chief Sustainability Office, says: “We implemented AI in our manufacturing operations and the focus was actually to help improve product quality and yield.

“But by doing that we also reduced waste and energy consumption. That’s a tangible result with real dollar values associated with it, which resonates with people.”

The findings suggest that organisations are increasingly approaching AI-enabled sustainability in an integrated way, across three major impact domains: decarbonisation and energy efficiency, resource efficiency and circularity, and people centricity and societal outcomes.

This systems view reflects Eva’s argument that climate, circularity and social considerations must be tackled together rather than in isolation.​

“This is important because you cannot choose what challenge you address in the world,” she says.

“We talk about decarbonisation, but really we also need to conquer circularity at the same time and we need to keep people and society at the centre of our thinking.”

Peter Koerte, Managing Board Member and Chief Technology Officer (CTO) at Siemens, adds: “AI is already transforming how we build and power the world - making it more sustainable every step of the way.”