One of the biggest problems with using AI in accounting and auditing is making sure that the data is accurate and of high quality. This is because AI systems are only as good as the data they are trained on, and financial data is often broken up or not structured in a consistent way. Another issue is making sure that AI-driven decision-making is open and understandable, especially when auditors are asked to give explicit reasons for risk assessments or anomaly detections made by complicated algorithms. There is also a skills gap: many professionals don't have the technical knowledge to properly deploy or manage AI solutions. This means that there is a demand for people who know both accounting and machine learning. Companies might develop auditable AI models with "explanation layers" or blockchain-backed data trails to make them easier to follow and more trustworthy. Finally, there are regulatory and ethical issues with bias, accountability, and compliance, which means that AI's usage in financial monitoring needs new rules to follow.

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In the following publication, you can find general recommendations on the benefits and limitations of using artificial intelligence in production activities:

Article Artificial Intelligence in the Service of Health and Safety ...

Although the use of artificial intelligence is somewhat appropriate, in some cases it cannot adapt to the present. Artificial intelligence is built using algorithms, but accounting rules and standards, especially in the field of taxation, are changing. Even if artificial intelligence keeps itself up to date, it will not have the proper analytical power and will not provide appropriate data for decision-making.

Artificial Intelligence is making a big impact in accounting and auditing, helping automate repetitive tasks and offering smarter insights. But adopting AI isn’t without its challenges. For one, the technology needs clean, organized data to work well — and that’s not always easy to come by. Many professionals also find themselves facing a skills gap, as AI tools often require technical know-how that goes beyond traditional accounting training.

There are also important ethical and regulatory concerns. AI decisions need to be transparent and in line with financial rules, which isn’t always straightforward. Bias in AI systems can lead to errors or unfair outcomes, so human oversight is still essential. Security is another major issue — with sensitive financial data involved, protecting privacy and guarding against cyber threats is critical.

On top of that, not everyone is ready to embrace change. Some may worry about job security or simply feel unsure about new technologies. Cost can be a barrier too, especially for smaller firms that may not have the resources to invest in AI. And since there are no clear industry standards yet, figuring out the best way to use AI can feel overwhelming. Still, with the right approach and ongoing learning, AI has the potential to support and strengthen the work of accountants and auditors — not replace it.