Disclaimer: This article is republished with permission from the author, Emma Herd. The original article was published on LinkedIn and can be found here. Any views expressed in this article are those of the original author and do not necessarily reflect the views of Altiorem.

Recent advancements in artificial intelligence (AI) are rapidly transforming the sustainability landscape, offering innovative solutions to some of the world’s most pressing environmental challenges.

Through the EY Net Zero Centre, we’ve observed AI serving not just as a tool, but as a catalyst for meaningful change, redefining what’s possible in the pursuit of a sustainable future. From optimising energy grids to enhancing emissions reporting, AI is playing a pivotal role in advancing the net zero agenda. However, as we unlock its potential, we must urgently address its environmental footprint and ensure that AI itself aligns with sustainability principles.

The applications of AI in driving sustainability outcomes are vast and varied. Consider energy grids that autonomously optimise operations in real time, balancing supply and demand, or satellite systems providing precise monitoring of deforestation across vast areas. Envision algorithms enhancing the accuracy of Scope 3 emissions reporting and predictive models forecasting climate impacts with remarkable precision. These examples merely scratch the surface of AI’s potential in our collective journey toward net-zero emissions.

Importantly, AI is empowering sustainability teams to tackle complex challenges more effectively. Many environmental issues rely on analysing large datasets, identifying risks, and making informed decisions—areas where AI excels. By automating processes and delivering actionable insights, AI can help organisations save time and resources while addressing systemic challenges such as climate change and biodiversity loss.

Yet, as we embrace these opportunities, there is no denying that we must also confront the growing environmental footprint of AI itself, along with other social and governance challenges.

The energy-intensive nature of machine learning models and the increasing demand for data centre capacity necessitate careful consideration of how we design and deploy AI systems.

This dual focus—utilising AI for sustainability while ensuring the sustainability of AI—is crucial.

While the potential of AI is immense, integrating it into sustainability strategies comes with inherent risks that must be managed. Issues such as data integrity, algorithmic bias, and system integration can pose significant challenges when scaling up AI applications. To build trust and achieve reliable outcomes, organisations must prioritise transparency, accuracy, security, and ongoing monitoring in their use of AI.

To address these risks effectively, we recommend developing a robust AI Governance Framework that aligns with strategic sustainability goals. This framework should encompass policies that promote transparency and mitigate bias while establishing an ethics committee to oversee the development and deployment of AI tools. Regular reviews and training will ensure employees are equipped with the knowledge needed to foster responsible and ethical behaviours in their use of AI technologies.

For leaders eager to adopt AI in delivering a net zero agenda, understanding how this technology works—and how it can be applied—is paramount. The potential use cases for AI are limited only by our imagination; however, success requires a thoughtful approach that considers both upstream and downstream impacts.

Organisations should examine every stage of the AI value chain—from cradle to cloud—and embed sustainability considerations throughout. This includes sourcing clean energy for data centres, using environmentally friendly materials in supply chains, and responsibly managing e-waste from outdated hardware. Simultaneously, businesses should explore how AI can enhance downstream sustainability outcomes by improving customer engagement on shared environmental goals or increasing operational efficiency in ways that reduce emissions.

As we navigate this transformative landscape, balancing risk with innovation will be vital for success in both AI and sustainability. The rapid advancements in AI are fundamentally changing how organisations operate while addressing systemic challenges like climate change and resource scarcity. By embedding good governance into frameworks from the outset—and scaling responsibly—businesses can unlock opportunities to harness AI as a force for good while remaining committed to their net-zero ambitions.

A Call to Action

The time for decisive action is now. In this era where technology meets environmental stewardship, we must embrace the power of AI as a catalyst for driving our net-zero agenda forward.

To move forward effectively, organisations could consider actions such as:

1. Developing comprehensive AI strategies aligned with delivering on science-based sustainability targets
2. Integrating AI-driven climate risk assessments into core business strategy and financial planning
3. Setting ambitious short-, medium-, and long-term emissions reduction targets enabled by AI
4. Implementing AI-powered decarbonisation initiatives that address material emissions across Scopes 1, 2, and 3
5. Developing robust, AI-enhanced climate adaptation plans to build resilience against physical risks
6. Building sustainability principles into responsible AI frameworks.

We have really only begun to understand the full scope and breadth of the ways in which we can embed AI into our collective net-zero efforts.

EY is also conducting further research, to be released shortly, aimed at improving our understanding of how sustainability and AI is viewed globally, and how factoring in the environmental and sustainability footprint could impact the AI-readiness of organisations across Australia and New Zealand.

The journey ahead is filled with both challenges and opportunities. By taking decisive action now and positioning themselves at the forefront of AI-driven sustainability, companies can reduce risks, capture value, and lead the transition to a net-zero carbon economy.

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