AI for Decarbonization: Supercharging Sustainable Industry Transformation

Industrial companies today operate under intensifying pressure to decarbonize. Globally, eight hard-to-abate heavy industries alone account for 40% of greenhouse gas emissions ^[1]. The urgency is compounded by the fact that global fossil fuel emissions reached a record high of 37.4 billion tonnes in 2024 ^[2]. At the same time, businesses must meet equally critical demands, including ensuring uptime, safeguarding performance, and managing volatile energy costs. 

However, significant barriers threaten to stall progress. According to Siemens Infrastructure Transition Monitor 2025, inadequate electricity grid infrastructure is holding back electrification efforts for more than half of organizations, and nearly 48% of executives now deem decarbonization too expensive ^[3]. As a result, the decarbonization of core operations has dropped significantly in priority. To reverse this trend, the industry is actively pivoting toward technology-driven solutions.

While almost two-thirds of leaders identify electrification as their most realistic path to net zero, they recognize that hardware alone is insufficient. The industry needs intelligent systems capable of interpreting vast, fragmented datasets and acting on them in real time, embedding sustainability directly into operational decisions. Consequently, 63% view digitalization—specifically AI-driven optimization—as a critical enabler of the energy transition ^[3].

This is where Industrial AI comes in. Unlike general-purpose AI, which is trained on broad consumer datasets, Industrial AI is engineered to be robust, secure, and reliable for application-specific environments. It interfaces with physical infrastructure, interprets real-time sensor data, and optimizes the performance of assets and systems continuously. In the context of sustainability, Industrial AI consolidates fragmented data sources and operational signals to support informed, system-level decision-making across operations and supply chains.

For this, Siemens takes an integrated approach to AI by embedding intelligence directly into the systems that power industrial operations and infrastructure. Instead of being treated as a separate function, AI is built into control systems, monitoring systems, and digital twins. This ensures that sustainability outcomes are not managed as one-off initiatives but embedded into the daily rhythm of industrial decision-making.

From Data to Decarbonization with Industrial AI

Industrial environments are defined by complexity, from coordinating thousands of assets to meeting safety and regulatory demands. In this setting, generic AI tools add little value because they lack the specific operational context required to make safe, high-stakes decisions. Impact comes only when intelligence is grounded in deep domain knowledge and integrated directly into the systems that govern performance.

The differentiator lies in Siemens’ integration of operational technology (OT) and information technology (IT). By training models on industry-specific data, Siemens ensures that its AI is a core operational component. Because trust is non-negotiable in these settings, Siemens designs its Industrial AI to be explainable and reliable in safety-critical processes. This transparency allows operators to understand the reasoning behind automated decisions, ensuring regulatory compliance and creating a foundation where sustainability is embedded into daily operations as an inherent capability without sacrificing efficiency or stability.

Embedded Intelligence: AI in the Loop

To drive decarbonization, Siemens embeds this domain-driven intelligence directly into core platforms, such as SIMATIC for process automation, Senseye for predictive maintenance, and Building X for cross-domain analytics. This ensures that AI actively shapes the decisions that determine energy use and emissions in real time.

Siemens deploys these models within closed-loop environments that continuously refine system behavior, detecting anomalies and responding to fluctuating inputs without waiting for human intervention.

• Automated Optimization: Applications like Comfort AI on the Building X platform leverage data from HVAC equipment, weather, and occupancy to predict and define optimal conditions. This creates a closed-loop optimization that operates systems more efficiently, potentially delivering more than 6.5% in monthly energy savings.

• Anomaly Detection: The Building X Energy Manager uses AI-based automation to compare energy consumption forecasts against historical data. It detects anomalies in consumption patterns early, identifying potential equipment faults and preventing energy overspending.

• Adaptive Control: In data centers, the White Space Cooling Optimization (WSCO) system utilizes machine learning to autonomously predict heat loads and regulate temperatures at the rack level, eliminating the need for manual cooling management while reducing operational risk.

Industrial AI in Practice: Use Cases

Industrial AI is already changing how critical sectors plan, operate, and maintain assets for lower emissions. Its value comes from embedding intelligence where decisions are made—in control loops, engineering workflows, maintenance practices, and portfolio analytics—so that efficiency gains translate into measurable sustainability outcomes across manufacturing and infrastructure alike.

Data Centers: Cooling Optimization Through AI

Data centers underpin digital economies, but cooling can account for up to 40% of their total energy consumption ^[4]. The largest levers sit inside control and airflow decisions that must adapt continuously to changing IT loads and ambient conditions.

Siemens applies Industrial AI to this control layer. Algorithms analyze environmental data, thermal loads, and equipment behavior to optimize setpoints, balance airflow, and coordinate chillers and CRAH/CRAC units in real time. In practice, this reduces overcooling and hotspots, stabilizes thermal profiles, and improves Power Usage Effectiveness (PUE) without compromising uptime.

At Greenergy Data Centers, one of the largest and greenest facilities in the Baltics, AI–driven cooling delivered around a 30% efficiency improvement shortly after deployment, according to CTO Toomas Kell ^[5]. This demonstrates how intelligent control can unlock significant operational gains. The advantage, however, is not limited to efficiency. By automating tuning and accelerating feedback cycles, AI removes what Siemens calls the ‘burden of time,’ thus reducing manual intervention and reaction lag and allowing teams to focus on higher-value reliability and capacity planning. Siemens supports this through a modular, partner-integrated approach (including partners such as Vigilent) that can scale across facilities and phases of growth ^[6].

Industrial Manufacturing: Predictive Maintenance with Senseye AI

In asset-intensive industries, even small disruptions can ripple across entire operations, leading to higher costs, energy waste, and avoidable emissions. Siemens addresses this challenge with Senseye Predictive Maintenance, an AI-enabled solution that forecasts failures, monitors asset health at scale, and integrates with existing maintenance workflows ^[7].

At Mercer Celgar, one of North America’s largest pulp and solid wood product producers, Senseye Predictive Maintenance consolidated data from multiple production lines into a single platform, providing early insights into equipment performance and potential failures. This not only improved uptime but also created a foundation for moving toward prescriptive maintenance, supporting more sustainable and resource-efficient operations.

Similarly, BlueScope, a global steel producer, applied Senseye across multiple plants to reduce downtime and enhance throughput. By combining predictive algorithms with IoT vibration monitoring, BlueScope closed critical gaps in its preventive maintenance routines, achieving substantial resource savings. Key performance indicators such as “downtime avoided” helped secure executive buy-in and drove a cultural shift toward predictive practices.

Industrial AI embeds value directly into production workflows. By turning machine data into early warnings and measurable KPIs, it helps manufacturers link reliability with sustainability, minimizing wasted energy, materials, and labor while keeping assets available at scale.

Hydrogen Plants: Scaling Clean Energy with Generative AI

Designing and deploying a hydrogen plant is a highly iterative process. Engineers begin with a basic process description, translate it into piping and instrumentation diagrams, configure control sequences, and simulate system behavior before any equipment is ordered or installed. These steps are essential, but also time-consuming, repetitive, and dependent on deep domain knowledge.

Siemens applies generative AI to streamline these workflows. The Hydrogen Plant Configurator helps teams create plant concepts from natural-language inputs. The COMOS Engineering Assistant supports automatic generation of equipment layouts and P&IDs, while SIMATIC PCS neo assists with automation setup, including sequential function charts (SFCs). Together, these tools form a connected environment that cuts manual engineering steps significantly, improving consistency and accelerating overall timelines without sacrificing quality ^[8].

Once a plant is operational, Siemens’ AI continues to support performance and availability. Predictive agents monitor operations, surface anomalies, and provide maintenance recommendations through intuitive interfaces ^[9]. This ensures that hydrogen infrastructure can scale with confidence, supported by reliable insights throughout the lifecycle.

Roland Busch, President and Chief Executive Officer of Siemens AG, captures this shift: “Generative AI has the potential to revolutionize the way companies design, develop, manufacture, and operate.” This vision is already taking shape in the hydrogen sector, where Siemens’ domain-driven approach enables companies to expand clean energy capacity with both speed and precision.

Commercial Real Estate: AI for Continuous Energy Optimization

Most commercial real estate still operates on fixed schedules. Heating, cooling, and lighting follow pre-set routines even as occupancy, weather, and tariffs fluctuate hour by hour. This mismatch makes it difficult to align daily operations with decarbonization goals.

Siemens addresses this through Building X, which applies Industrial AI across four critical pillars: energy and sustainability, operations and maintenance, HVAC optimization, and ecosystem-powered solutions.

• Energy and Sustainability: Siemens’ AI-driven Energy Manager compares consumption forecasts against historical data to detect anomalies and prevent overspending. This allows facilities to achieve up to 9% annual energy savings without major retrofits, simply by orchestrating existing systems more intelligently ^[9].

• HVAC Optimization and Adaptive Control: Rather than static setpoints, applications like Comfort AI use algorithms to "dynamically reset" controls based on weather and occupancy. This adaptive approach coordinates heating and cooling in real time, reducing energy usage while maintaining tenant comfort.

• Operations and Maintenance: AI shifts building management from reactive to predictive. By continuously tracking equipment health, the system flags faults for targeted maintenance before they cause downtime, enhancing productivity and reducing unnecessary site visits.

• Ecosystem-Powered Solutions: Building X operates as an open platform. This allows customers to integrate specialized AI solutions from partners—such as cleaning intelligence or advanced indoor mapping—directly into their building management systems, ensuring that sustainability strategies can evolve alongside new technologies.

In projects such as PRODEA’s smart real estate developments in Greece, Siemens has demonstrated how this integrated, cloud-enabled approach can advance both efficiency and sustainability at scale ^[10]. By embedding AI into every layer of building management, properties evolve from static structures into responsive assets that are smarter, more efficient, and more sustainable from the inside out.

Scalable, Industry-Ready AI

Industrial AI must do more than optimize one factory, one building, or one machine. To deliver lasting impact, it must scale across assets, sites, and industries while retaining the reliability and trust that critical operations demand. Siemens is working to realize this vision through a layered approach that combines foundational technologies, portfolio integration, and an open business platform.

At the core would be an Industrial Foundation Model (IFM) ^[11]. This domain-trained, large-scale model is designed to “speak the language of industry” by building on the principles of trustworthy AI, thus leveraging robust, secure data sets to ensure that intelligence is both scalable and reliable. By embedding institutional engineering knowledge into AI, an IFM would make generative and predictive capabilities consistent and reusable across contexts. 

The role of Siemens Xcelerator is to provide an open digital business platform and ecosystem that allows these capabilities to scale. Through Xcelerator, AI-enabled products interoperate with digital twins, partner applications, and external data sources, making it possible to replicate best practices portfolio-wide and across industries. This openness ensures that AI deployments are not siloed experiments, but part of a consistent, expandable digital infrastructure.

Scaling also requires trust. Siemens designs its AI systems with explainability, security, and compliance built in, ensuring they can be applied to safety-critical assets without compromising oversight. According to a Siemens global survey, more than half of industrial leaders expect AI to control high-value assets within five years ^[12].

This vision is echoed by Peter Koerte, Chief Technology and Strategy Officer at Siemens: “The superconvergence of industrial AI with numerous technologies will eventually result in the industrial metaverse; an immersive digital environment that mirrors and simulates real-world systems.”

The outcome is not AI everywhere, but AI embedded where it matters: standardized by foundational technologies like IFM, deployed within Siemens’ trusted portfolios, and scaled through Siemens Xcelerator. This makes industrial AI both repeatable and resilient, helping organizations turn sustainability goals into system-wide operational results.

Operationalizing the Energy Transition

Industrial AI is becoming a direct enabler of decarbonization. By embedding intelligence into core systems, companies can continuously optimize performance, accelerate deployment, and cut energy waste. With interoperable portfolios, foundational technologies, and an open ecosystem through Siemens Xcelerator, industrial AI is positioned as a critical layer of infrastructure.

References

1. “Net-Zero Industry Tracker 2024,” World Economic Forum, December 2024. [Online]. Available: https://www.weforum.org/publications/net-zero-industry-tracker-2024/ 
2. P. Friedlingstein et al., “Global carbon budget 2024,” Earth System Science Data, vol. 17, no. 3, pp. 965–1039, doi: https://doi.org/10.5194/essd-17-965-2025 
3. "Siemens Infrastructure Transition Monitor 2025." Siemens. 2025. [Online]. Available: https://www.siemens.com/global/en/company/insights/infrastructure-transition-monitor-2025.html
4. EPRI, “Powering Intelligence: Analyzing Artificial Intelligence and Data Center Energy Consumption,” May 2024. [Online]. Available: https://restservice.epri.com/publicdownload/000000003002028905/0/Product 
5. Reuters Events and Siemens, “A New Pace of Change: Industrial AI x Sustainability,” 2024. [Online]. Available: https://www.siemens.com/global/en/company/insights/a-new-pace-of-change-industrial-ai-x-sustainability.html
6. “Data Centers Solutions,” Siemens. [Online]. Available: https://xcelerator.siemens.com/global/en/industries/data-centers.html 
7. “Senseye Predictive Maintenance – reliability redefined,” Siemens. [Online]. Available: https://www.siemens.com/global/en/products/services/digital-enterprise-services/analytics-artificial-intelligence-services/senseye-predictive-maintenance.html 
8. “GenAI in Hydrogen,” Siemens. [Online]. Available: https://xcelerator.siemens.com/global/en/industries/chemical-industry/applications/hydrogen/genai-in-hydrogen.html 
9. “Artificial Intelligence (AI),” Siemens. [Online]. Available: https://www.siemens.com/global/en/company/digital-transformation/artificial-intelligence.html
10. “Commercial Buildings,” Siemens. [Online]. Available: https://xcelerator.siemens.com/global/en/industries/commercial-buildings.html 
11. “Industrial Foundation Model that speaks the language of industry,” Siemens. [Online]. Available: https://www.siemens.com/global/en/products/automation/topic-areas/industrial-ai/industrial-foundation-model.html 
12. “Would you trust the algorithm?” Siemens. [Online]. Available: https://www.siemens.com/global/en/company/stories/research-technologies/artificial-intelligence/survey-next-gen-industrial-artificial-intelligence.html