{"id":23600,"date":"2026-10-18T12:30:15","date_gmt":"2026-10-18T12:30:15","guid":{"rendered":"https:\/\/icv-controlling.com\/blog\/ai-in-sustainability-reporting-clear-governance-as-a-key-success-factor\/"},"modified":"2026-10-18T12:38:46","modified_gmt":"2026-10-18T12:38:46","slug":"ai-in-sustainability-reporting-clear-governance-as-a-key-success-factor","status":"publish","type":"blog","link":"https:\/\/icv-controlling.com\/en\/blog\/ai-in-sustainability-reporting-clear-governance-as-a-key-success-factor\/","title":{"rendered":"AI in Sustainability Reporting: Clear Governance as a Key Success Factor"},"content":{"rendered":"<section id=\"acf-block-67cc4c5105c60\" class=\"image-module bg-bg-default text-text-default dark:!bg-dark dark:!text-white  \">\n  <div class=\"max-w-screen-2xl spacing-px mx-auto\">\n    <div class=\"content-top max-w-3xl align-left \">\n          <\/div>\n  <\/div>\n      <div class=\"image-module__container mx-auto max-w-screen-fhd spacing-px \">\n      <div class=\"image-module__image\"> <img loading=\"lazy\" decoding=\"async\" width=\"748\" height=\"433\" src=\"https:\/\/icv-controlling.com\/wp-content\/uploads\/2026\/10\/KI-im-ESG-Reporting.png\" class=\"z-0 mx-auto !object-contain !h-full !w-auto\" alt=\"\" srcset=\"https:\/\/icv-controlling.com\/wp-content\/uploads\/2026\/10\/KI-im-ESG-Reporting.png 748w, https:\/\/icv-controlling.com\/wp-content\/uploads\/2026\/10\/KI-im-ESG-Reporting-300x174.png 300w\" sizes=\"(max-width: 748px) 100vw, 748px\" \/><\/div> \n          <\/div>\n  <\/section>\n\n<section id=\"acf-block-67cc4c5105ee6\" class=\"text-module bg-bg-light text-text-default  dark:!bg-dark dark:!text-white\">\n  <div class=\"max-w-screen-2xl spacing-px mx-auto spacing-py\">\n          <div class=\"lg:mb-lg mb-md\">\n        <div class=\"content-top max-w-3xl align-left \">\n          <h2 class='headline-element align-left typo-h2 font-bold'>Clear Rules for the Responsible Use of AI<\/h2>        <\/div>\n      <\/div>\n          <div class=\"typo-body text-element max-w-3xl align-left \">\n        <p><em>by Mike Schulze \/ Steffen Stadler \/ Alexander Gl\u00f6ckner<br \/>\n<\/em><\/p>\n<p> Artificial intelligence (AI) offers great potential for sustainability reporting. However, for companies to use it reliably and in a way that stands up to audit scrutiny, clear rules, defined responsibilities, and transparent processes are needed. Binding guidelines are essential, particularly when handling sensitive corporate, supplier, and personal data.<\/p>\n<p>A risk-based approach helps tailor the level of control to the specific use case. While the linguistic revision of a text poses a lower risk, AI-supported calculations of emissions data or the assessment of supplier risks require significantly stricter controls and comprehensive documentation.<\/p>\n<p><strong>Five Key Questions for Successful Exam Preparation<\/strong><\/p>\n<p>Before using AI, companies should address five key questions:<\/p>\n<ul>\n<li><strong>Purpose:<\/strong> What task should the AI perform?<\/li>\n<li><strong>Data Set:<\/strong> Is the data used complete, up-to-date, and approved?<\/li>\n<li><strong>Traceability:<\/strong> Can third parties verify the results from both a technical and computational standpoint?<\/li>\n<li><strong>Control:<\/strong> Who reviews, documents, and approves the results?<\/li>\n<li><strong>Audit Relevance:<\/strong> Does the use of AI affect information subject to reporting requirements or evidence relevant to the audit?<\/li>\n<\/ul>\n<p>These questions form the foundation for transparent and robust AI implementation.<\/p>\n<p><strong>How companies can get started<\/strong><\/p>\n<p>The implementation of AI should be carried out in a step-by-step and structured manner. The first step is to take stock of existing applications and identify suitable pilot projects. Tasks that involve a high degree of manual effort, good data availability, and manageable risk\u2014such as document review, data classification, plausibility checks, or benchmarking\u2014are particularly suitable.<\/p>\n<p>In addition, companies need binding minimum standards for data protection, quality assurance, the labeling of AI-generated content, and human oversight. Interdisciplinary teams of subject matter and technology experts help to appropriately evaluate the results and ensure the safe use of the tools employed.<\/p>\n<p>It is crucial to consider verifiability from the very beginning. AI-generated results must be documented, traceable, and reproducible. The responsibility for technical evaluation and approval remains with the company.<\/p>\n<p><strong>Where AI Creates Tangible Value<\/strong><\/p>\n<p>AI can make sustainability reporting more efficient, particularly when it comes to research and plausibility checks. For example, competitive analyses can be automated using publicly available data, or a company\u2019s own calculations can be verified against independent benchmarks.<\/p>\n<p>Even with metrics that are difficult to classify\u2014such as CO\u2082 equivalents\u2014AI can help identify unrealistic orders of magnitude. This requires that the results be critically reviewed and that their derivation be documented in a transparent manner. This applies to AI applications just as much as it does to traditional spreadsheets.<\/p>\n<p><strong>Conclusion: Use AI, but Stay in Control<\/strong><\/p>\n<p>AI can speed up sustainability reporting, improve analyses, and reduce the workload for reporting teams. However, it is no substitute for reliable data, clear processes, or professional judgment.<\/p>\n<p>As the use of AI increases, governance, transparency, control, and documentation are becoming increasingly important. Particularly in the context of mandatory CSRD audits, companies must ensure that AI-supported processes are auditable from the outset.<\/p>\n<p><strong>The key to success lies in using AI as a strategic tool without relinquishing responsibility.<\/strong> This way<strong>,<\/strong> initial experiments can lead to lasting productivity gains for sustainability teams, controllers, report preparers, and auditors.<\/p>\n      <\/div>\n          <div class=\"flex flex-wrap w-full gap-2 lg:gap-4 md:mt-lg mt-md \">\n                <a target=\"_blank\" href=\"https:\/\/icv-controlling.com\/wp-content\/uploads\/2026\/10\/Nutzung-von-KI-Schulze-Stadler-Gloeckner.pdf\" class=\"btn-secondary \">\n            Read the full article (in German)                    <\/a>\n                <a target=\"\" href=\"https:\/\/www.haufe.de\/controlling\/zeitschrift\/controller-magazin\/jahrgang-2026-28-88942.html\" class=\"btn-secondary \" rel=\"noopener\">\n            More about Controller Magazin                    <\/a>\n              <\/div>\n      <\/div>\n<\/section>","protected":false},"template":"","blog-category":[],"blog-tags":[210],"class_list":["post-23600","blog","type-blog","status-publish","hentry","blog-tags-controlling-en"],"acf":[],"_links":{"self":[{"href":"https:\/\/icv-controlling.com\/en\/wp-json\/wp\/v2\/blog\/23600","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/icv-controlling.com\/en\/wp-json\/wp\/v2\/blog"}],"about":[{"href":"https:\/\/icv-controlling.com\/en\/wp-json\/wp\/v2\/types\/blog"}],"version-history":[{"count":4,"href":"https:\/\/icv-controlling.com\/en\/wp-json\/wp\/v2\/blog\/23600\/revisions"}],"predecessor-version":[{"id":23605,"href":"https:\/\/icv-controlling.com\/en\/wp-json\/wp\/v2\/blog\/23600\/revisions\/23605"}],"wp:attachment":[{"href":"https:\/\/icv-controlling.com\/en\/wp-json\/wp\/v2\/media?parent=23600"}],"wp:term":[{"taxonomy":"blog-category","embeddable":true,"href":"https:\/\/icv-controlling.com\/en\/wp-json\/wp\/v2\/blog-category?post=23600"},{"taxonomy":"blog-tags","embeddable":true,"href":"https:\/\/icv-controlling.com\/en\/wp-json\/wp\/v2\/blog-tags?post=23600"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}