Industrial AI Delivers 23% Energy Savings and 24% CO2 Cuts at Scale

How Industrial AI Boosts Sustainability and Performance

Industrial AI is moving from talk to results. A new Siemens and Reuters Events study of 263 senior sustainability leaders shows companies are scaling AI and seeing measurable impact on energy and carbon.

With 74% of organisations aiming for net zero by 2040, AI is becoming core infrastructure for decarbonisation and operational efficiency. The focus now: build systems that deliver outcomes, not experiments.

Adoption is translating into measurable gains

• 63% have moved beyond pilots to targeted or widespread deployment.
• Almost two-thirds report average energy savings of 23%.
• 59% report CO2 reductions averaging 24%.
• Year-over-year jump: in 2024, only 41% saw energy savings and 36% saw CO2 reductions.

As Eva Riesenhuber, Global Head of Sustainability at Siemens, puts it: "The complexity of juggling global interconnected system transitions in times of major disruptions can only be mastered with AI."

Energy management and grid optimisation lead

Energy management is the most mature use case, with 65% of organisations deploying AI here. 52% of respondents say optimising energy consumption is the primary way AI will help them reach their sustainability goals.

On the grid side, Alliander uses Siemens' Gridscale X to create a digital twin of its network. With real-time visibility and smarter orchestration, it can raise grid utilisation by up to 30% without physical upgrades.

In data centres, Greenergy Data Centers implemented Siemens' White Space Cooling Optimization. "When we first launched the system, it improved our efficiency by approximately 30% at the push of a button," says Kert Evert, Chief Development Officer. "But this was just the beginning because the system learns, adapts and improves over time."

Designing sustainability into products from day one

AI isn't just tuning operations. It's reshaping how products are designed. 60% have implemented AI for resource efficiency management, and 43% use it to improve waste management. At the product level, 63% have adopted generative design to optimise material use and carbon footprint upfront.

Eryn Devola, Head of Sustainability at Digital Industries (Siemens), explains: "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."

A practical example: Siemens developed lightweight robot grippers from a carbon-reduced polymer. The components generate roughly 30 kg CO2 from cradle to gate, compared with about 670 kg for conventional metal grippers.

Co-benefits are common. Brooke Tvermoes, Director of Climate, Energy and Environment at IBM's Chief Sustainability Office, notes: "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."

And as Peter Koerte, Managing Board Member and CTO at Siemens, adds: "AI is already transforming how we build and power the world - making it more sustainable every step of the way."

What management and product teams should do next

• Prioritise high-yield use cases: site-level energy optimisation, advanced scheduling, predictive maintenance, and yield improvement. These tend to show savings within a quarter.
• Close data gaps: metering by major loads, reliable SCADA historians, CMMS data quality, and product/BOM-level carbon factors (supplier-specific if possible).
• Stand up a cross-functional squad: operations, maintenance, data/ML, and sustainability reporting. Give them a clear savings target and decision rights.
• Test in a safe sandbox: use digital twins to trial control strategies before touching live assets; roll out with guardrails and operator overrides.
• Bake sustainability into design reviews: include energy, material, and embodied carbon as standard trade-offs alongside cost and performance.
• Procure for outcomes: specify telemetry, APIs, and data access in equipment contracts to avoid integration debt.

90-day execution plan

• Weeks 1-2: Baseline energy, CO2, throughput, scrap; lock the target and choose one pilot line or site.
• Weeks 3-6: Connect data (meters, BMS/SCADA, CMMS, ERP); deploy AI-driven energy or cooling optimisation.
• Weeks 6-10: Scale to a second asset; add predictive maintenance or schedule optimisation to capture compounding gains.
• Weeks 8-12: Run a generative design sprint on a high-volume component; include material and carbon constraints.
• Week 12: Report outcomes in business terms (kWh saved, tCO2e avoided, yield %, payback).

Risks to watch-and how to de-risk

• Model drift: set up continuous monitoring and retraining tied to asset condition and seasonality.
• Operator trust: keep models explainable; show the "why" for each recommendation and log interventions.
• Rebound effects: lock savings into setpoints and production plans; verify with metered M&V.
• Integration debt: use standard protocols and APIs; avoid single-vendor lock-in for data.

Metrics your board will care about

• Energy intensity (kWh per unit), site PUE (for data centres), and load factor.
• tCO2e avoided (market- and location-based), abatement cost per ton, and time-to-value.
• Yield, scrap/rework rate, OEE, constraint hours, and unplanned downtime.
• % of products with generative design applied and average material reduction per part.

Helpful resources

• Science Based Targets initiative (SBTi) for net-zero target frameworks and validation.
• IEA: Energy efficiency for benchmarks and policy context.

Level up your team

If you're building internal capability for AI-driven sustainability and operations, explore practical upskilling paths by role here: AI courses by job.