Artificial Intelligence (AI) is everywhere right now and as most businesses know, it’s about much more than creating memes or drafting school essays. AI can create significant efficiencies for companies dealing with large quantities of data by automating the ingestion, parsing, and analysis of massive, unstructured data sources. And one of the areas where we are seeing significant opportunities for AI advancement is in the context of regulatory reporting — specifically in the context of analyzing technical data and extracting key information for emissions reporting. In this article, we’ll take a look at how AI can streamline these processes, as well as why (and how) companies should exercise caution in deploying these solutions.
A. How AI can streamline regulatory reporting requirements
By utilizing Natural Language Processing (NLP), Large Language Models (LLMs), and Optical Character Recognition (OCR), AI systems can read documents, invoices, and sensor logs to identify and categorize all manner of emissions data and facilitate faster and easier regulatory reporting. These processes can increase efficiency by rapidly classifying raw data, identifying anomalies, and mapping activity data. Moreover, there are systems that can link data outputs to disclosure frameworks like the EU Corporate Sustainability Reporting Directive, the Carbon Disclosure Project, or the Climate Disclosure under California SB 253, with some AI tools able to generate “regulator-ready” reports.
To state the obvious, programs like this can create significant efficiencies. What once required manual analysis of vast sources of data, followed by report drafting under each of the applicable regulatory frameworks, can (in many instances) now be done in an automated fashion in a fraction of the time. Indeed, at least one source has estimated that AI can reduce the time spent on environmental, social, and governance (ESG) reporting by up to 75 percent. And the more complex the regulations are, the more helpful AI can be — for multinational organizations navigating complex requirements across multiple jurisdictions, the added efficiency can be quite significant.
AI also can have a forward-looking component, with real-time data tracking across the supply chain and agentic AI monitoring of carbon footprints helping companies to more easily engage in predictive modeling and develop meaningful strategies to reduce emissions.
B. The need for caution
There’s no doubt that AI can be a significant benefit to emissions analysis and regulatory reporting, but the ever-evolving nature of AI also comes with risks. Without adequate human involvement, there is a substantial risk of relying on data that is inaccurate, misleading, or (in rare cases) simply made up. For all the recent advances in AI technology, many AI tools still err on the side of giving the user the answer they want, even if that answer is wrong and even if the AI tool must create a “hallucination” in the data to get that result.
For this reason, it’s important that any company using AI for data analysis and regulatory reporting understand exactly what the AI tool is doing and how it is reaching its conclusions. Transparency is key. Indeed, while the speed and scope of AI tools is often the selling point, those same qualities mean that if things go off the rails in the AI tool’s analysis, those errors will quickly be magnified and spread across the analysis. Thus, even as companies leverage AI tools to make reporting functions more efficient, its critical that they keep a human “in the loop” to exercise judgment over the analysis and the final product.
Any organization that is considering the use of AI tools for sustainability analysis and regulatory reporting should make sure the following safeguards are in place:
- Lack of bias. In choosing AI tools and setting up queries and analysis frameworks, it’s important to ensure that you are avoiding biases that could improperly skew the results.
- Transparency. The AI tool should not be the proverbial “black box” — the organization needs to have a full understanding of exactly how the data is being analyzed.
- Accuracy. If results from an AI tool seem too good to be true, there’s a good chance that they are not accurate. While it’s not necessary to check every part of the work done by an AI tool, it’s important to do enough analysis to ensure accuracy.
- Human judgment. No matter how fast and efficient the AI tools may be, there is always a risk of flawed outcomes. And ultimately, the machines don’t answer to regulators — you do. Humans should be involved in every step of the analysis process and no reporting documents should go out without being fully reviewed and vetted by an internal expert. AI tools can make the regulatory reporting process much more efficient, but they cannot take the place of human judgment focused on credible, accurate, and transparent reporting.
