How Industrial AI Drives Measurable Sustainability Gains

AI and sustainability have become two of the biggest drivers, challenges and requirements across most industries worldwide.

Now, as the demands and urgency rise for both, innovators are finding ways to make them not only closer in reach, but to accelerate each other.

With 74% of organisations aiming to achieve net zero by 2040, AI is now essential infrastructure for addressing decarbonisation challenges at the required speed and scale.

Addressing this challenge and opportunity, Siemens has published a report: From Pilots to Performance, How Industrial AI is Helping to Scale Sustainability Impact in cooperation with Reuters Events.

The study spans 263 senior sustainability professionals, carried out in the third quarter of 2025 – and AI Magazine looks at the main takeaways.

What the study’s main statistics tell about AI and sustainability 

The report finds that nearly two-thirds of organisations have moved beyond pilot projects in their industrial AI implementations focused on sustainability.

That 63% have progressed to targeted use, moderate adoption or widespread deployment across operational and product development functions.

This means that the acceleration in adoption is generating measurable environmental outcomes. 

Nearly two-thirds of organisations report achieving energy savings averaging 23%, while 59% have realised carbon dioxide reductions averaging 24%. 

These figures show substantial year-on-year improvements from 2024, when 41% reported energy savings and 36% achieved CO2 reductions.

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.”

Why energy management emerges as primary a sustainability application

Energy management has become the most mature application area, with 65% of organisations having implemented industrial AI solutions in this domain. 

The technology is proving particularly valuable in optimising energy consumption across operations, with 52% of respondents identifying it as the primary way AI will help achieve sustainability goals.

Practical applications demonstrate the environmental benefits. 

For example, Alliander, the Dutch electricity and gas network company serving 3.5 million customers, has implemented Gridscale X software from Siemens to support the energy transition. 

The software provides real-time visibility through a digital twin of the grid, allowing the company to boost grid utilisation by up to 30% without physical infrastructure upgrades. 

Additionally, this increased capacity is enabling greater integration of renewable energy sources.

Meanwhile, in Estonia, Greenergy Data Centers has deployed a White Space Cooling Optimization management system from Siemens at its facility near Tallinn. 

The system uses sensors to continuously monitor temperature and airflow, dynamically adjusting cooling units to match requirements. 

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.”

Beyond operational efficiency, AI is enabling circularity and resource efficiency. 

Some 60% of organisations have implemented AI for resource efficiency management, while 43% are using it to enable better waste management. 

Simultaneously, predictive maintenance, which extends equipment life and reduces material consumption, has been adopted by 60% of organisations.

How product design integration supports circular economy transition

At the product level, AI is enabling sustainability considerations to be embedded directly into design processes. 

Some 63% of organisations have implemented generative design for physical products, allowing them to optimise for material efficiency and carbon footprint from the outset.

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 has demonstrated these capabilities through its development of lightweight robot grippers manufactured using a carbon-reduced polymer. 

The components weigh less than 2kg and generate CO2 emissions of just 30kg from cradle to gate, compared with 670kg for conventional metal grippers weighing nearly 50kg. 

The company reports that modernised production lines using these components have approximately halved energy consumption and reduced CO2 emissions by more than 3 tonnes.

Furthermore, Automation Innovation, a manufacturer of glass production equipment, has used AI-driven analytics and digital twin technology to transform its mold cleaning operations. 

The new automated approach has saved 700,000 tons of raw materials per year, reduced on-site energy consumption by 30% and avoided nearly 1 billion kilograms of CO2 emissions to date while eliminating the use of harmful chemicals.

How confidence in AI’s sustainability impact grows substantially

The proportion of respondents expecting high or medium positive impact from industrial AI in accelerating the energy transition has increased from 42% in 2024 to 71% in 2025. 

Separately, 59% of respondents reported using AI to help decarbonise operations, according to the Siemens Infrastructure Transition Monitor 2025.

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 research indicates that organisations are approaching sustainability through AI holistically, addressing what Eva describes as three key impact areas: 

• Decarbonisation and energy efficiency
• Resource efficiency and circularity
• People centricity and society

“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 center 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.”