Summary

Artificial intelligence (AI) is changing how companies prepare climate and sustainability reports. It can collect data faster, improve accuracy, support compliance with different reporting rules, monitor supply chains, and identify climate-related risks. At the same time, experts warn that AI should not be trusted without human review because it can make mistakes, miss important details, and reduce transparency. As sustainability reporting becomes more detailed, AI is becoming a useful tool for handling large amounts of information while helping organizations focus more on environmental action than manual paperwork.

Climate reporting has become far more detailed in recent years. Companies are now expected to measure carbon emissions, energy use, supply chain impacts, and many other environmental and social indicators. Regulators, investors, and customers also expect this information to be accurate, transparent, and easy to verify.

For many businesses, gathering this information is a difficult and time-consuming task. Data often comes from many departments and external suppliers. Checking its accuracy and preparing reports requires significant effort. Artificial intelligence is increasingly being used to simplify these processes without changing the reporting requirements themselves.

AI is helping companies collect and verify sustainability data.

Preparing sustainability reports requires companies to measure many different indicators. These include greenhouse gas emissions, electricity consumption, diversity and inclusion information, and other environmental, social, and governance data.

Much of this work is still completed manually. Employees often spend considerable time collecting information from different systems before checking whether the data is complete and accurate.

Artificial intelligence can reduce this workload by automatically collecting information from multiple sources. It can compare data across several years, identify missing information, and detect unusual figures that may require further review.

More advanced forms of AI, including autonomous systems known as agentic AI, are also improving the way organizations collect and organize sustainability information. These systems can perform routine tasks with less manual intervention while helping companies build larger and more complete datasets.

AI is also supporting data verification. Before sustainability reports are published, companies need confidence that the information is reliable. AI can quickly identify inconsistencies and help validate large volumes of information, making disclosures more dependable.

By automating repetitive work, sustainability teams can spend less time on paperwork and more time focusing on activities such as reducing emissions and improving environmental performance.

AI supports compliance and improves supply chain visibility.

Companies that operate across several countries often have to comply with different sustainability reporting rules. Each regulation may require different information and different reporting formats.

AI can help identify exactly which data is needed for each reporting requirement. It can also assist companies in preparing reports that match the requirements of specific regulations.

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This is particularly useful for reporting obligations under the Corporate Sustainability Reporting Directive (CSRD), the Carbon Border Adjustment Mechanism (CBAM), and the EU Regulation on Deforestation-free Products (EUDR). Automating these reporting activities reduces administrative work while improving consistency and lowering the risk of compliance errors.

Improving supply chain transparency

Supply chain reporting is another major challenge. Scope 3 emissions usually represent the largest share of a company’s total carbon footprint because they include emissions generated throughout the supply chain.

Companies may work with thousands of suppliers, making Scope 3 emissions among the most difficult sustainability metrics to calculate. AI helps by tracking supplier data in real time and automatically calculating product-level carbon footprints.

Natural language processing and sentiment analysis also allow companies to assess potential sustainability risks involving suppliers. These technologies analyze publicly available information and other data sources to identify possible concerns before they become larger problems.

Some AI-powered environmental, social, and governance solutions already collect, validate, and combine supplier sustainability data into a single reporting system. This helps organizations identify suppliers that may not meet sustainability requirements while improving procurement decisions.

Better visibility across supply chains also improves reporting quality. It supports more accurate sustainability disclosures while reducing the reporting burden on smaller suppliers, including many small and medium-sized enterprises.

AI is also being used to create predictive climate tools. By analyzing weather records, hydrological information, and satellite imagery, AI can detect environmental changes such as deforestation and help companies assess climate-related risks using larger and more detailed datasets.

AI also brings challenges for transparency and accuracy.

Although AI can process enormous amounts of information quickly, more data does not always mean better data. If companies rely entirely on AI without checking its results, reporting mistakes may go unnoticed.

One major concern is that AI systems often operate like a “black box.” Users may receive results without fully understanding how those conclusions were reached. In climate reporting, where context and detailed interpretation matter, this lack of transparency creates additional risks.

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AI can process information rapidly, but it can also spread mistakes just as quickly. It may misunderstand complex situations, rely on incomplete information, or produce conclusions that do not fully reflect reality.

These risks become even more significant as sustainability data providers increasingly use AI to analyze corporate disclosures. Incorrect classifications or missed information could influence how investors and other stakeholders understand a company’s environmental performance.

Human oversight therefore remains essential throughout the reporting process. AI cannot always distinguish between company intentions and actual achievements. It may also struggle to understand the difference between broad ambitions and scientifically supported climate targets.

Maintaining trust in sustainability reporting

Another challenge is preserving the unique story behind each company’s sustainability efforts. AI-generated reports may become too generic if they rely only on automated text generation. Human review helps ensure that reports remain authentic, transparent, and representative of the organization’s actual progress.

Companies using AI for climate reporting also need to understand how their AI systems were trained and whether the underlying data and methods are reliable. Strong validation processes remain necessary to ensure the information presented in sustainability reports is accurate, consistent, and trustworthy.

As of 2026, Artificial intelligence is becoming an important tool for climate reporting by improving data collection, validation, compliance, supply chain monitoring, and climate risk assessment. At the same time, organizations continue to rely on human expertise to review AI-generated information, maintain transparency, and ensure sustainability disclosures accurately reflect real-world environmental performance.