AI's energy dilemma: Challenges, opportunities, and a path forward

AI’s energy demand from data centres is growing. Image: Getty Images for Unsplash
- The energy demand of data centres, including hyper-scale facilities and micro edge deployments, is projected to grow from 1% in 2022 to over 3% by 2030.
- AI is already helping companies reduce energy use by up to 60% in some instances. Key use cases include optimizing energy storage, battery efficiency, and smart grid management.
- Coordinated efforts are needed to enable sustainable AI adoption across industries. Key focus areas for action include regulation, financial incentives, technological innovation and market development.
While there have been numerous forecasts around the energy demands of artificial intelligence (AI) and the efficiency gains it will unlock, it is hard to predict these with certainty, given the rapidly evolving landscape.
A recently published white paper from the World Economic Forum titled Industries in the Intelligent Age - Artificial Intelligence’s Energy Paradox: Balancing Challenges and Opportunities, suggests four key interlinked areas for navigating this uncertainty, managing challenges and unlocking opportunities for sustainable AI deployment.
These include:
- Leveraging AI deployment for decarbonization.
- Transparent and efficient AI energy use.
- Innovation in technology and design.
- Effective ecosystem collaboration.
AI’s energy consumption
AI presents opportunities and challenges in the energy landscape. With around 72% of surveyed companies leveraging AI for at least one business function, its transformative potential is clear.
According to aggregated estimates from Accenture based on data from Goldman Sachs, the International Energy Agency and the Organization for Economic Co-operation and Development, accompanying this rise in adoption, AI-related electricity consumption can be expected to grow by as much as 50% annually from 2023 to 2030, posing a challenge to power systems.
The electricity demand of data centres, from hyper-scale facilities to micro edge deployments, is projected to grow from 1% of global energy demand in 2022 to over 3% by 2030.
However, such projections can vary. Uncertainty remains around how profound AI’s overall energy impact will be and which strategies could mitigate challenges that arise or enable new solution opportunities.
Despite AI’s rapid expansion, AI data centre electricity consumption will still likely account for only a small fraction of global electricity demand.
However, when combined with other major demand drivers (such as the electrification of transport and buildings), it can still contribute to an increased strain on power grids and energy providers.
To address this, strategies such as energy-efficient hardware, AI-optimized cooling, and smarter data centre design and operations are being explored to limit AI’s energy consumption. Moreover, advancements in chips and algorithms (e.g., small language models) may further mitigate AI's energy consumption.
Cross-industry energy saving and optimization
Existing cross-industry use cases demonstrate reduced energy consumption or savings ranging from 10-60% in some instances (e.g. building and space, telecommunications, energy, advanced manufacturing, etc.), with potential for further optimization.



Surfacing, scaling and replicating successful use-case implementation strategies that deliver measurable energy efficiency and optimization benefits can drive sustainable AI approaches and enable cross-industry collaboration.
Lessons can already be learned:
- Regulatory, policy and financial enablers can incentivize responsible AI development through compliance frameworks and funding mechanisms.
- Opportunity exists to encourage more supply chain alliances, which have begun to emerge. For example, industrial partnerships and multistakeholder digital transformation collaborations, including in emerging economies, could incorporate skills building, infrastructure development, etc.
- Industry players have highlighted the need for alignment towards harmonized metrics. For example, decarbonization assessment tools and alignment between emerging voluntary industry standards with government regulations (like the European Union’s AI Act).
Managing challenges and unlocking opportunities
Ongoing assessment will be critical to understanding AI’s net energy impact as its adoption accelerates across various industries. The white paper proposes a framework comprising four interlinked areas for navigating this uncertainty and ensuring we manage the challenges and unlock the opportunities for sustainable AI deployment. These four areas include (adaptable over time):
- Leveraging AI deployment for decarbonization: Expand AI’s role in clean energy solutions, a decarbonized energy grid and energy optimization.
- Transparent and efficient AI energy use: Promote open data and optimize energy use in AI development and operations.
- Innovation in technology and design: Advance energy-efficient hardware and AI systems through technological advancements and sustainable design principles.
- Effective ecosystem collaboration: Foster sustainable AI through a collaborative ecosystem involving regulators, industry players and academia.

Although AI's energy impact remains uncertain, proactively monitoring its evolving intersection can help clarify challenges, uncover opportunities and guide transformative solutions.
Additional contributions to this article came from Michael Higgins, Project Fellow, AI Governance Alliance, Strategy Principal Director, US Utilities Strategy, Accenture; James Mazurek, Managing Director - Accenture Strategy, Utilities, Accenture; and Maria Basso, Digital Technologies Portfolio Manager, C4IR Digital Technologies, World Economic Forum.
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Aleksander Dardeli
February 14, 2025