How AI can transform sustainability reporting

As sustainability reporting becomes increasingly complex, organizations are struggling to keep pace with the mounting demands of regulators, investors and stakeholders. From tracking emissions and energy use to monitoring supply chain risks, the sheer volume of data has meant disclosure is resource-intensive.

Artificial intelligence (AI) is emerging to ease this burden. By automating data collection, validating disclosures, enhancing supply chain visibility and predicting climate risks, AI offers companies a way to improve accuracy, streamline compliance and refocus resources on real-world impact.

However, adoption comes with caution. Without human oversight and transparency, AI can amplify errors, obscure context and undermine trust. The solution and challenge is to pair human judgment with AI’s computational power to build more reliable and effective reporting systems.

Sustainability reporting involves tracking a broad range of indicators – from carbon emissions and energy use to diversity, equity and inclusion metrics. As regulatory expectations become increasingly complex, organizations face mounting challenges in collecting, validating and disclosing this information.

Given that much of this work is still done manually, artificial intelligence (AI) can significantly streamline this process by aggregating data from multiple sources, detecting and addressing gaps and identifying errors and anomalies in datasets across multiple years.

In particular, autonomous systems in the form of agentic AI have become a critical enabler for data collection and insights in more recent times.

AI also plays a critical role in the verification and validation of data, which is essential for creating the necessary trust required by all stakeholders.

By automating time-consuming tasks, AI can improve the reliability of sustainability disclosures and enable sustainability teams to redirect their efforts. They can spend more time achieving real-world impact, such as reducing emissions and driving strategic change, than on compliance-related activities.

For large and multinational organizations, navigating the complex landscape of sustainability reporting frameworks across multiple jurisdictions can be a significant challenge.

AI can serve as a powerful compliance enabler by identifying the data points required under various standards and regulations and assisting in the preparation of tailored reports aligned with specific jurisdictional requirements.

Examples of where this is relevant include the specific compliance requirements for the purposes of the Corporate Sustainability Reporting Directive, Carbon Border Adjustment Mechanism and EU Regulation on Deforestation-free Products.

By automating these processes, AI helps reduce the administrative burden, ensures greater accuracy and consistency across disclosures and minimizes the risk of non-compliance.

Scope 3 emissions account for a substantial portion of a company’s total carbon footprint, yet they remain notoriously hard to reduce, given that a company’s products may come from thousands of suppliers.

AI can help address this challenge by enabling real-time data tracking across the supply chain and automating tasks such as calculating product-level carbon footprints.

Other tools, such as natural language processing and sentiment analysis can be used to assess whether a company further down your supply chain is at risk, providing a more complete picture of operations.

For example, Microsoft’s environmental, social and governance (ESG) value chain solution uses AI to collect, validate and integrate supplier sustainability data, streamlining product procurement processes and enabling early detection of noncompliant suppliers.

Enhanced transparency enables more accurate and timely reporting, supporting companies in meeting sustainability targets, ultimately leading to greater real-world impacts on emissions reductions.

Importantly, using AI techniques also reduces the administrative burden placed on supply chain participants, many of whom might be small and medium-sized enterprises.

Beyond enhancing reporting, AI is increasingly being leveraged to develop predictive tools that support climate risk management. By training models on vast datasets, including weather patterns, hydrological information and satellite imagery, AI can help forecast climate-related events and detect environmental changes such as deforestation in real time.

These capabilities enable companies to access faster, more accurate data and conduct forward-looking risk assessments, allowing for more proactive decision-making and improved preparedness in the face of climate-related challenges.

AI has the potential to bring more data into sustainability systems but this does not automatically translate into better or more reliable data. Without proper human oversight, there is a risk of over-reliance on AI-generated outputs, even when those outputs may be inaccurate or misleading.

For many users, AI remains a “black box”: data goes in, results come out but the reasoning behind the results are often unclear. This lack of transparency can be hazardous in sustainability reporting, where complexity and nuance are essential.

The very capabilities that make AI so effective – its speed, scale and analytical power – also make it inherently risky: AI can rapidly process vast amounts of information but it can just as easily amplify errors, misinterpret context or draw conclusions from incomplete data.

This is especially concerning, as sustainability data providers are increasingly relying on AI to assess corporate disclosures. If these tools misclassify companies or overlook critical details, they can mislead investors and other stakeholders.

Human judgment, oversight and responsible implementation are essential to ensure that AI supports credible, accurate and transparent sustainability reporting. AI might not distinguish between intention and action or between aspirational and science-based targets.

Moreover, blending multiple sources without validation can lead to conflicting conclusions. Human intervention is critical in cases where nuance matters. One key concern is the potential loss of the “human touch” when AI is used to generate sustainability reports.

The narrative – the unique story behind a company’s sustainability journey – can be diluted or overlooked entirely. Preserving this individuality is crucial to maintaining authenticity, building trust and communicating progress in a way that resonates with stakeholders.

To integrate AI responsibly, firms must establish robust validation processes and interrogate the models they use. Was the model trained on credible data? Is the methodology transparent?

AI will not replace sustainability professionals but those who can harness AI effectively and responsibly will have a clear advantage. Used well, AI can help fill data gaps, speed up reporting, uncover environmental risks and ultimately reduce costs for companies.

It also enables companies to focus their efforts much more on the implementation of actionable solutions rather than data collection and management. Used poorly, it can deepen inequality, reinforce greenwashing and make the sustainability field harder to navigate.

The key is to combine AI’s computational power with human judgment, transparency and stakeholder engagement. In doing so, we can build systems that enhance rather than erode trust in climate action.