Beyond compliance: How AI can drive the future of sustainability

Artificial Intelligence (AI) has entered a transformative phase, driven by the rapid evolution of generative models and their integration into business operations and decision-making processes.

As AI becomes central to corporate strategy, its environmental and social footprint is drawing increased scrutiny — from the energy consumption of data centers, and resource intensity of hardware production, to concerns about algorithmic bias and equitable access to technology.

Yet while AI advances at high speed, regulatory frameworks are struggling to keep pace, raising critical questions about how companies can harness AI opportunities — especially as they face mounting pressure to meet ambitious sustainability targets outlined in United Nations Sustainable Development Goals (SDGs), Nationally Determined Contributions (NDCs), or National Energy and Climate Plans (NECPs).

In this context, AI holds promise as a powerful enabler for accelerating scientific discovery, optimizing complex systems, and helping close the green skills gap. But realizing this potential requires more than innovation; it demands a strategic, forward-looking approach to regulation and risk management.

In this article, Enhesa experts Marion Kerestedjian and Lydia García ask: What enabling conditions must your organization establish for AI to truly support your sustainability goals?

Read on for expert insights into how companies can navigate existing legal frameworks, prepare for emerging regulations, and leverage AI to enhance sustainability performance to mitigate risks while positioning AI as a driver of positive change.

Why AI matters for sustainability: Global challenges and systemic bottlenecks

To unlock the transformative potential of AI in advancing sustainable practices, we must first examine the scale of the problems. The urgency of sustainability is underscored by the scale and complexity of the challenges ahead. To understand AI’s potential as a catalyst for progress, it must be viewed in relation to the ambitious global targets that companies are expected to help achieve…

• According to the International Energy Agency (IEA), global renewable power generation must triple within the next decade, while methane and carbon emissions from fossil fuel operations need to be cut by 75%
• Food production must increase significantly to meet the demands of a growing population with at least a 10% rise required to achieve the Zero Hunger target, according to the Food and Agriculture Organization (FAO)
• The UN estimates that carbon dioxide removal technologies must scale up to eliminate several gigatonnes of CO2 annually by 2050, while water demand is projected to increase by 30% by 2050, placing immense pressure on freshwater resources

Progress toward these goals is often slowed by persistent and interrelated challenges.

• Innovation remains slow and costly, especially when relying on traditional research methods
• Social, technical, and biological systems — such as energy grids, food networks, and ecosystems — are increasingly complex and difficult to optimize
• The demand for green skills is rapidly outpacing the availability of qualified professionals

In this context, AI has a critical role to play. It can accelerate scientific discovery, optimize complex systems through advanced modeling and analytics, and personalize workforce training to help close the skills gap. To ensure that AI delivers on its promise for sustainability, effective regulation is essential, providing the safeguards and incentives needed to align technological progress with sustainable goals.

Making AI sustainable: Is regulation enough?

While AI technology is becoming deeply embedded in business operations, it remains a relatively new consideration in regulatory frameworks. Most jurisdictions still rely on existing laws — such as data protection, consumer rights, cybersecurity, and competition law — which, although relevant, are not designed to address the specific environmental and social risks associated with AI systems.

One of the most significant — and to date, most comprehensive — regulatory initiatives is the European Union’s AI Act. The Act introduces a risk-based classification of AI systems and sets out obligations for providers and users based on the level of risk.

How can AI regulatory sandboxes drive innovation?

One of the Act’s most promising features is the “AI regulatory sandbox”, which provides controlled environments for companies to test AI systems before public release. These sandboxes not only foster innovation in a safe setting, but also offer a unique opportunity to embed sustainability considerations into AI development. For example, sandboxes could be used to explore how AI can support climate change mitigation and the development of energy-efficient solutions.

Bridging the environmental gaps in EU AI governance

The AI Act has faced some criticism for missing a broader opportunity to address AI’s environmental impacts in a more systemic and ambitious way, particularly compared to the ambitious goals initially proposed by the European Parliament.

Its environmental scope remains narrow, primarily addressing energy consumption, while overlooking other critical issues such as water usage and e-waste generation, while the Act has also been criticized for falling short in recognizing AI’s potential to actively contribute to sustainability goals across sectors missing a strategic opportunity to frame a lever for the green transition.

To complement the EU AI Act’s limited environmental scope, the European Commission has proposed the Cloud and AI Development Act, placing sustainability at the core of its objectives. The initiative recognizes that expanding Europe’s computational capacity — particularly to support AI development — must go hand in hand with minimizing environmental impact. To this end, not only would it promote the construction and operation of data centers that are resource-efficient, but it also proposes financial support for projects that demonstrate high sustainability value, helping to overcome barriers such as permitting delays and access to capital.

Regulatory ambiguity for AI around the world

Outside the European Union, no jurisdiction has yet adopted a similarly comprehensive framework for AI governance — particularly one that integrates sustainability as a core objective. While some countries have introduced targeted measures, such as transparency requirements or ethical guidelines, these efforts remain fragmented and largely non-binding. A few initiatives do attempt to incorporate sustainability principles, but they tend to focus on isolated aspects like energy efficiency or workforce adaptation, rather than offering a systemic approach.

Despite growing interest in the intersection of AI and sustainable development, it remains uncertain how — and to what extent — governments will regulate this space. Most current efforts are exploratory and narrowly focused, leaving companies to navigate a patchwork of expectations. This regulatory ambiguity reinforces the need for proactive corporate strategies and highlights the importance of establishing enabling conditions through legislation, including:

• Investment in sustainable AI tools
• Inclusive digital infrastructures
• Resource efficiency
• Renewable energy prioritization
• Robust governance frameworks
• Workforce capacity building
• Overall ESG performance tracking

Voluntary frameworks: Filling the gaps in regulation

In this unpredictable environment, companies can’t afford to adopt a wait-and-see approach. To ensure AI contributes meaningfully to long-term ESG targets, it’s essential that they proactively engage with voluntary frameworks and develop internal strategies to responsibly integrate AI into their sustainability agendas. Doing so not only mitigates risks associated with AI misuse, but also creates space for innovation while being ahead of the regulations that may come in the future.

A growing number of international and regional bodies have introduced voluntary frameworks that guide ethical AI development while reinforcing global sustainability efforts.

The leading international framework: OECD’s AI Principles

Among the most influential is the OECD’s AI Principles, which offer a blueprint for policymakers and organizations seeking to harness AI opportunities.

Grounded in five core values (inclusive growth, transparency, accountability, fairness, and robustness) the OECD framework encourages the deployment of AI systems that actively contribute to the UN’s Sustainable Development Goals (SDGs), including:

• Economic inclusion (SDG 8 – Decent Work and Economic Growth)
• Social equity (SDG 10 – Reduced Inequalities)
• Environmental sustainability (encompassing several goals such as SDG 6: Clean Water and Sanitation, SDG 7: Affordable and Clean Energy, SDG 12: Responsible Consumption and Production, or SDG 13: Climate Action)

With endorsement from over 47 countries, the OECD Principles have gained significant global traction, underscoring their relevance and legitimacy.

Regional frameworks

In parallel, regional frameworks are emerging to address AI challenges and opportunities within specific contexts.

In Africa, the African Union’s Continental Artificial Intelligence Strategy positions AI as a powerful tool for solving systemic challenges unique to the continent. It prioritizes key sectors for AI development, such as:

• Healthcare, using for instance early disease detection and predictive analytics (SDG 3 – Good Health and Wellbeing)
• Agriculture and food security, via weather forecasting and soil monitoring (SDG 2 – Zero Hunger)
• Education and skills development, with personalized learning platforms (SDG 4 – Quality Education)
• Climate resilience, through enhanced modeling and resource management (SDG 13 – Climate Action)

In Southeast Asia, the ASEAN Guide on AI Governance and Ethics (2024), along with its expanded edition on Generative AI (2025), offers a comprehensive framework for the region. It provides practical guidance and real-world examples (Annex B) to show how AI can be practically deployed not only to mitigate sustainability-related risks but also to open opportunities for advancing the SDGs.

Why do voluntary AI frameworks matter?

Voluntary frameworks matter because they’re shaped by the realities of each region. When companies use these standards as a reference, they’re not just ticking boxes — they’re making sure their approach to AI and sustainability aligns with local context and expectations. It also helps them stay ahead of regulatory changes, build trust with stakeholders, and show genuine commitment to responsible development.

But turning AI into a real driver of sustainable innovation takes more than just adopting new technologies. It requires companies to actively translate AI’s capabilities into measurable contributions toward global sustainability targets.

Aligning AI with SDGs: Corporate initiatives for better sustainability

Although companies are not yet legally required to contribute directly to the United Nations SDGs, they’re increasingly expected to do so due to growing investor pressure, evolving ESG reporting requirements, and heightened stakeholder scrutiny.

Regulatory frameworks like the EU Corporate Sustainability Reporting Directive (CSRD), along with voluntary standards such as the Global Reporting Initiative (GRI) and International Sustainability Standards Board (ISSB), are also pushing companies to disclose how their operations and innovations support progress toward global sustainability targets. Yet, global progress toward the SDGs remains uneven and too slow to fully reach targets by 2030, with only 18% of SDGs being on track and 17% showing moderate progress.

In light of this, corporate innovation — particularly through artificial intelligence — is emerging as a powerful lever. Leading companies across sectors such as technology, energy, manufacturing, and transport are integrating AI into their sustainability strategies, signaling a shift toward tech-enabled environmental stewardship.

How companies are using AI to their advantage in the real world

AI presents a wide array of financial and non-financial opportunities in the environmental domain. It enables organizations to better manage risks and impacts while enhancing overall environmental governance. Here are just a few real-world examples of how leading organizations are using AI to create better sustainability practices:

Microsoft’s water positive goals

Microsoft has committed to becoming water positive — meaning it aims to replenish more water than it consumes. To achieve this, Microsoft is pursuing two key strategies:

1. Managing the water footprint of its rapidly expanding data center infrastructure
2. Deploying AI to drive water efficiency across its operations

The company is focused on reducing water-use intensity as well as restoring water sources and expanding access to clean water. One concrete initiative is its AI-powered leak detection and replenishment project, which aims to help identify and reduce water loss in aging distribution networks by applying acoustic analysis technology. Expected to save millions of cubic meters of water annually, this project contributes to the sustainable management of water resources by promoting practices that prevent water waste and align with SDG 6 – Clean Water and Sanitation.

Vestas’ AI-enhanced wind turbines

Danish wind energy company Vestas has successfully leveraged AI and high-performance computing to enhance the efficiency of its wind turbines. By optimizing a technique known as wake steering — which adjusts turbine positioning to reduce wind turbulence and maximize energy capture — Vestas has achieved up to a 10% increase in energy output. This technological advancement not only enhances renewable energy generation but also actively supports the achievement of SDG 7 – Affordable and Clean Energy, improving the reliability and accessibility of modern energy solutions.

Supply chain planning at IKEA

IKEA is leveraging artificial intelligence to optimize supply chain planning and prevent overproduction caused by excess inventory — one of the primary sources of waste in global supply chains. By developing AI-powered technologies to enhance demand forecasting based on statistical sales metrics, IKEA is fostering more sustainable production patterns, in line with SDG 12 – Responsible Consumption and Production.

Google’s wildfire detection

Lastly, Google has improved wildfire detection by combining satellite imagery with AI to more accurately identify wildfire boundaries. This information is made available in real time through Google Search and Maps, including during navigation, thereby complementing on-the-ground emergency efforts. By enabling earlier detection of ecosystem degradation, this initiative directly supports SDG 15 – Life on Land.

Going beyond AI-driven environmental initiatives: The social side of sustainability

Companies are also leading the way in using AI to enhance their social contributions. One of the most prominent areas of application is human resources, where AI is reshaping how organizations attract, hire, and retain talent. From identifying top candidates to fostering inclusive workplaces, AI is helping businesses remain competitive in the evolving talent landscape.

For example, Amazon employs predictive analytics to monitor employee performance and engagement, enabling targeted retention strategies based on real-time insights.

Across industries — especially manufacturing — businesses are leveraging AI to address specific social and safety needs. GE Vernova, for instance, applies AI-powered predictive maintenance to monitor equipment health and prevent costly downtime in its factories. By analyzing sensor data, their AI systems can anticipate potential failures and recommend optimal maintenance schedules, improving both safety and operational efficiency.

By leveraging such data-driven approaches, these companies advance full and productive employment, and promote decent working conditions, supporting SDG 8 – Decent Work and Economic Growth.

In the end, these initiatives clearly demonstrate that, when responsibly and effectively integrated into environmental and social strategies, AI emerges as a powerful enabler — allowing leading companies to accelerate progress toward the SDGs, enhance ESG performance, and demonstrate true leadership in aligning business ESG strategy with global priorities.

The way forward: From compliance to leadership in shaping the future of sustainable AI

As AI continues to evolve faster than regulation, companies must take ownership of their role in shaping a sustainable future. The regulatory landscape remains fragmented, and while voluntary frameworks and emerging proposals offer guidance, they are not yet harmonized or enforceable. This uncertainty is both a challenge — due to the lack of clear rules — and an opportunity to influence the standards and practices that will define sustainable AI.

To seize this opportunity, organizations must move beyond reactive compliance and adopt a proactive stance. This involves investing in internal governance, fostering cross-functional collaboration, and electing to align with emerging best practices voluntarily.

Although international frameworks are raising the bar for transparency and accountability, global progress toward the SDGs remains insufficient. In this context, AI offers companies a strategic lever to accelerate sustainability efforts and demonstrate leadership through measurable environmental and social impact. From resource efficiency and ecosystem protection to workforce development, AI-driven initiatives are helping organizations set new benchmarks for responsible business.

Ultimately, the question isn’t whether AI will be regulated for sustainability — it’s whether your organization will be ready when it is. By laying the groundwork today, companies can turn AI from a regulatory risk into a strategic engine for innovation, resilience, and long-term ESG value.