Transforming sustainability reporting with AI—from data analysis to materiality

Talking points

• AI-driven materiality assessments can empower companies to identify and prioritize impactful sustainability issues.
• AI can bring increased effectiveness to the double materiality assessment process and enable data-driven decisions.
• This can simplify sustainability reporting and compliance and increase transparency for stakeholders.
  

Remember 2022’s Best Picture Oscar winner—Everything Everywhere All at Once? That title feels like a fitting description of the sweeping impact artificial intelligence (AI) is having across today’s business landscape. The sustainability field is no exception.

As the Corporate Sustainability Reporting Directive (CSRD) and International Sustainability Standards Board (ISSB) frameworks evolve, AI is increasingly shaping materiality analysis that informs sustainability disclosures. This includes CSRD double materiality assessments. The technology can bring a new level of effectiveness, guidance, insight, and optimism to what is widely considered to be a complex and demanding regulatory process. In this blog, we’ll explore both the potential opportunities and challenges of leveraging AI for materiality assessments and reporting.

Transforming materiality assessments

If you’ve used a chatbot, you know how AI can be leveraged to simplify labor-intensive activities like double materiality assessments. AI tools can accelerate and streamline both halves of the process—impact materiality and financial materiality—from interviews to data analysis. For example, rather than asking a CFO about every possible sustainability topic, AI can help narrow the focus to what’s likely relevant, saving time and enabling stakeholders to provide a clearer, more targeted view of today’s priorities. AI can also compare a company with its peers and competitors on a host of priority benchmarks, spotting trends and outliers.

AI’s effectiveness and focus can translate into enhancements across the double materiality assessment and reporting process. For example, companies can get timely snapshots, updates, and insights with less manual data analysis and overall effort. These benefits can allow organizations to allocate resources to strategic tasks and potentially get increased value from the process. 

AI sustainability use cases

Potential AI applications for materiality assessments, CSRD reporting, and ISSB obligations are numerous, but a few select use cases stand out:

• Data analysis, sustainability scoring, and modeling. AI typically excels at analyzing vast quantities of data. It can sift through hundreds of materiality assessments to reveal trends and insights across industries and regions. With human guidance, AI tools can help improve fragmented or low-quality data, perform sustainability scoring, and reverse engineer desired sustainability outcomes to map the necessary steps. However, while AI processes large volumes quickly, it can also amplify errors, misinterpret data, and produce incomplete information—making governance and human oversight important throughout the process.
• Assessing sustainability-related impacts, risks, and opportunities. This process is central to double materiality assessments and CSRD compliance. On the financial side, AI can identify cost-saving opportunities such as enhancing energy management or product research and development (R&D), while enabling solutions like smart energy controls and eco-friendly materials that can make these opportunities a reality. AI can also streamline assessment of impacts to society and the environment, ranging from chemical use and pollution, to human rights violations in the supply chain and loss of livelihoods from environmental or social disruptions.
• Supply chain emissions tracking. Scope 3 emissions account for a large part of most companies’ total carbon footprint. These emissions, however, are tough to track and reduce considering a company’s products may come from thousands of suppliers.¹ Companies are increasingly deploying AI to assist with timely data tracking across the supply chain and to automate tasks such as calculating product-level carbon footprints.

Managing AI’s materiality impact

While AI can offer many benefits, it also carries potentially adverse impacts, which may be captured in a company’s double materiality assessment alongside AI’s risks and opportunities. As AI is deployed at scale across sectors, these impacts can be social, such as privacy issues and workforce displacement, or environmental, such as electricity demands for AI processing and water consumption for cooling at AI data centers.

Recent headlines have highlighted the rapid rise in AI’s electricity consumption. According to projections from Lawrence Berkeley National Laboratory (LBNL), by 2028, more than half the electricity consumed by data centers may be devoted to AI.² At that point, AI would use as much electricity annually as 22% of US households.³

Innovative solutions for more sustainable AI

Fortunately, AI’s energy consumption seems to be an addressable problem. How? If you guessed AI, well done. The same capabilities AI brings to materiality assessments can also help reduce its own energy footprint. In real-world applications, AI-powered heating, ventilating, and air-conditioning (HVAC) controls reduced HVAC electricity consumption in commercial buildings by 15%.⁴ Since HVAC accounts for 39% of commercial building energy use in the US⁵ (about 542 of 1,390 TWh⁶), a 15% reduction means savings of 81 TWh annually.

For context, LBNL estimates that global AI-related electricity demand could reach 326 TWh by 2028.⁷ Using just this single HVAC example, AI-driven efficiency usage in the US alone could save about one-quarter of AI’s anticipated annual global electricity consumption by 2028.

What role can Deloitte play?

Deloitte can advise you on navigating a wide range of sustainability challenges. We deliver responsible, human-led, AI-powered innovations that can turn bold ideas into practical solutions across the sustainability ecosystem—from tracking and reporting to materiality assessments and regulatory compliance. With extensive industry, domain, and regulatory experience, we can recommend ways to transform financial complexity into strategic clarity, grounded in quality, integrity, and transparency.

For more information, visit our Audit & Assurance Sustainability services site or the Deloitte AI Institute. And don’t hesitate to reach out to us with any questions. 

Endnotes

1. Sacha Bazin and Mike Hayes, “How AI can transform sustainability reporting,” World Economic Forum, September 26, 2025.
2. James O’Donnell and Casey Crownhart, “We did the math on AI’s energy footprint. Here’s the story you haven’t heard,” MIT Technology Review, May 20, 2025. Copyright © 2026, All rights reserved MIT Technology Review; www.technologyreview.com.
3. Ibid.
4. BrainBox AI, “Cammeby’s International achieves 15.8% reduction in HVAC energy use and costs,” December 2024.
5. National Institute of Building Sciences, “High-Performance HVAC,” Whole Building Design Guide, accessed November 2025.
6. US Energy Information Administration, “Electricity explained: Use of electricity,” based on Monthly Energy Review, Electricity, Table 7.6, November 2023.
7. James O’Donnell and Casey Crownhart, “We did the math on AI’s energy footprint. Here’s the story you haven’t heard,” MIT Technology Review, May 20, 2025. Copyright © 2026, All rights reserved MIT Technology Review; www.technologyreview.com.

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