AI and Energy: Balancing Innovation with Sustainability

The intersection of artificial intelligence (AI) and energy consumption is becoming a critical issue for business leaders striving to balance digital innovation with sustainability. In our latest webinar, AI’s Energy Challenge: Balancing Sustainability and Innovation, leading experts from Accenture, Siemens, and AECOM explored how AI is driving efficiency while placing new demands on global energy systems.

Webinar replay: AI's Energy Challenge 

Key Highlights from the Webinar

1. AI is Scaling – But So is Its Energy Footprint

Francis Hintermann, Global Managing Director at Accenture Research, explained that 2024 marks a tipping point: businesses are moving from AI pilots to full-scale deployments, especially in vertical applications like banking, life sciences, and consumer goods. While AI promises cost savings and improved capabilities, it also introduces a paradox—efficiency can lead to increased usage, thereby amplifying energy consumption. In fact, AI-related carbon emissions are expected to grow tenfold in the next six years if unchecked.

2. Clean Energy Isn’t Optional—It’s Urgent

Graeme Cooper, VP of Energy Advisory at AECOM, emphasized that the AI boom is layering on top of an already pressing energy transition. To meet rising demand, G7 economies must double electricity use by 2050 and generate 4–6 times more clean energy than they do today. The takeaway: a mix of energy sources, including nuclear, geothermal, and natural gas, will be required. Each data center must be assessed within its unique geographic and grid context.

3. Industrial AI is a Key to Sustainable Transformation

Pina Schlombs, Sustainability Lead at Siemens Digital Industries Software, introduced the concept of industrial AI—AI systems tailored to the physical laws of engineering and manufacturing. Unlike consumer AI, industrial AI demands trust, accuracy, and scientific reasoning. Siemens is already leveraging industrial AI to:

• Reduce energy use in robotics by over 90% through AI-based product redesign.
• Extend machine lifespan through predictive maintenance.
• Optimize supply chains using AI-driven scenario planning.

4. Thought Leadership Drives Scalable Action

All three panelists emphasized the role of thought leadership in translating AI-sustainability insights into scalable strategies. Francis stressed that success hinges on five key pillars: building a strong digital core (cloud and data maturity), investing in talent, implementing responsible AI governance, securing top leadership involvement, and committing to continuous reinvention.

5. Systemic Thinking is Non-Negotiable

The conversation repeatedly returned to the need for 'whole system thinking'. AI’s energy impact isn’t just about server power—it’s about integrating across data, grid infrastructure, supply chains, and emissions policies. For example, Siemens is partnering with vertical farms to recycle waste heat from data centers, and AI is being used to time-shift energy usage based on renewable availability.

6. Government & Policy Must Set the Foundation

Governments have a vital role in providing long-term certainty and infrastructure support. As Graeme noted, energy systems are inherently long-cycle and risk-averse. Regulatory frameworks must adapt to enable innovation without compromising resilience and security.

Business leaders cannot afford to treat sustainability and AI innovation as competing priorities. The winners in this space will be those who embed sustainability into their digital strategies—backed by smart data, thoughtful governance, and strategic partnerships.

Missed the webinar? Watch the replay here