Unlocking Efficiency and Precision in CSRD Materiality Assessment with AI

A wealth of relevant information accurately reflecting stakeholder opinions is already publicly available. AI can aggregate and organize these many thousands of data points, slicing and dicing them according to the needs of the company and its stakeholders, potentially eliminating biases that can arise from manual data collection.

By building an initial, wide-ranging topic universe, AI can collect a variety of different data points about an organization to develop a rich list of potentially relevant topics that may not have been evident at the outset. These topics can then be assessed and scored for relevance, allowing for a broader and holistic approach.

AI can also help process and effectively analyze large volumes of information. Given the abstract and high-level nature of ESRS standards and definitions, each organization's mapping is likely to be completely different and can be difficult to define. AI systems are able to map the collected data to the relevant ESRS standards. Following the initial mapping process, both quantitative and qualitative scoping exercises are also possible, where issues can be measured against a variety of criteria to determine relevance, such as identifying key issues, determining peer group issues, assessing the wider industry agenda, considering the potential impact of overlooking an issue, and using external trend analysis to identify emerging issues.

AI can significantly reduce the time, resources, and costs associated with CSRD materiality assessments, which typically require significant implementation costs and tie up internal and external resources for up to several months. By automating tasks that would otherwise require significant human effort, AI can optimize operations, streamline processes, and increase productivity.

While the traditional approach to ESG risk analysis typically combines various external ratings, frameworks, and financial considerations to identify initial risks and opportunities, such a static framework may not be sufficiently responsive to ever-changing aspects over time. To address this, AI introduces a dynamic element to the risk mapping process by continuously tracking metrics that identify emerging risks not captured by static frameworks. It then offers the ability to continuously screen for new risks, enabling early intervention and increased adaptability.

A major challenge in the industry is ensuring repeatability and replication of manual processes, often requiring starting from scratch with each reporting cycle. AI offers a way to overcome this by performing tasks in a repeatable, streamlined process, eliminating the need to reinvent the wheel every year. Additionally, CSRD needs to be audited. However, auditing and the use of AI models are not mutually exclusive. In fact, far from being a mysterious black box, AI operates as a structured model, similar to traditional valuation methods. And because it is replicable, it lends itself to auditability (as opposed to traditional questionnaires, for example). The output is a clear explanation of the sources used, the method applied, and the output produced. This combination of repeatability and accountability increases trust in AI assessment, rendering it a reliable tool for mapping materiality assessment or ESRS standards.