{"id":22315,"date":"2026-07-21T17:01:46","date_gmt":"2026-07-21T17:01:46","guid":{"rendered":"https:\/\/icv-controlling.com\/blog\/generative-ai-in-controlling\/"},"modified":"2026-07-21T17:11:01","modified_gmt":"2026-07-21T17:11:01","slug":"generative-ai-in-controlling","status":"publish","type":"blog","link":"https:\/\/icv-controlling.com\/en\/blog\/generative-ai-in-controlling\/","title":{"rendered":"Generative AI in Controlling"},"content":{"rendered":"
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Accurate calculations, but not necessarily better decisions<\/h2> <\/div>\n <\/div>\n
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Generative AI systems such as ChatGPT or Gemini are now remarkably capable of solving business management tasks. They calculate key performance indicators, explain calculation methods, and provide convincingly worded answers. However, especially in controlling, a mathematically correct calculation alone does not constitute a sound basis for decision-making. In <\/strong>her article in the Controller Magazin<\/em><\/strong> , Prof. Dr. Christoph Eisl<\/strong> and Prof. Dr. Werner Glei\u00dfner<\/strong> explain why controllers must always critically evaluate AI results and why human expertise will remain indispensable in the future.<\/p>\n

A clear example is the classic break-even analysis. Using given data such as material costs, production times, fixed costs, and a target margin, the AI calculates the selling price and the revenue at which a company breaks even. The result is mathematically correct and easy to understand.<\/p>\n

The real problem, however, lies not in the calculation but in the underlying assumptions. AI works exclusively with the information provided to it in the prompt. It implicitly assumes that all planned values\u2014such as material prices, production costs, or sales volumes\u2014are certain. In reality, however, this is rarely the case.<\/p>\n

Material prices fluctuate, supply chains can be disrupted, production times change, and demand often develops differently than planned. For this reason, in practice there is no fixed break-even point, but rather a range of possible outcomes.<\/p>\n

This highlights a fundamental limitation of generative AI: large language models generate linguistically and mathematically consistent responses, but do not automatically recognize uncertainties. Risks that are not explicitly mentioned are not taken into account. This can easily lead to a false sense of accuracy<\/strong> \u2014the results appear precise but only partially reflect economic reality.<\/p>\n

A particularly critical issue is that AI often adopts the same simplified models of thinking as many textbooks or users. As a result, it confirms assumptions rather than critically questioning them. Anyone who relies on such results risks making poor decisions\u2014for example, regarding investments, pricing strategies, or the assessment of economic risks.<\/p>\n

For professional use in controlling, this means that AI should be viewed as a support tool, not a replacement for business expertise. Deterministic calculations must be supplemented with risk analyses. Probabilities, scenarios, or simulations are often far more meaningful than individual key performance indicators.<\/p>\n

The future therefore lies in hybrid solutions. AI can process data, accelerate analyses, and explain results in an understandable way. Specialized controlling software provides well-founded models and simulations. However, people still play a crucial role: controllers and risk managers must critically examine assumptions, assess uncertainties, and interpret the results within the context of the company.<\/p>\n

Conclusion:<\/strong> Generative AI is a valuable tool for management accounting, but it is no substitute for sound decision-making logic. Precise calculations alone do not guarantee certainty. Only the combination of AI, appropriate methods, and human expertise leads to sound and responsible business decisions.<\/p>\n <\/div>\n

\n \n Read the full article (in German) <\/a>\n \n More about Controller Magazin (German) <\/a>\n <\/div>\n <\/div>\n<\/section>","protected":false},"template":"","blog-category":[],"blog-tags":[210],"class_list":["post-22315","blog","type-blog","status-publish","hentry","blog-tags-controlling-en"],"acf":[],"_links":{"self":[{"href":"https:\/\/icv-controlling.com\/en\/wp-json\/wp\/v2\/blog\/22315","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":3,"href":"https:\/\/icv-controlling.com\/en\/wp-json\/wp\/v2\/blog\/22315\/revisions"}],"predecessor-version":[{"id":22319,"href":"https:\/\/icv-controlling.com\/en\/wp-json\/wp\/v2\/blog\/22315\/revisions\/22319"}],"wp:attachment":[{"href":"https:\/\/icv-controlling.com\/en\/wp-json\/wp\/v2\/media?parent=22315"}],"wp:term":[{"taxonomy":"blog-category","embeddable":true,"href":"https:\/\/icv-controlling.com\/en\/wp-json\/wp\/v2\/blog-category?post=22315"},{"taxonomy":"blog-tags","embeddable":true,"href":"https:\/\/icv-controlling.com\/en\/wp-json\/wp\/v2\/blog-tags?post=22315"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}