Purpose: The new era of artificial intelligence is discussed in terms of business trends, rapid changes in the global economy and new types of risks. Therefore, the probability of failure in the business is high. This fundamental research brings the problem into the current age of artificial intelligence. It provides a good perspective on Altman's model in the field of artificial intelligence. He also recommends looking at additional methods, parameters, or models that are more reliable than Altman'sfor predicting failure. Methodology: The researcher uses a qualitative method to analyze the failure and use of artificial intelligence in this field. This study uses an experimental method to demonstrate the role of artificial intelligence in Altman's failure prediction method. The researcher put forward many explanations for the use of AI in Altman's model, but I think it is impossible to develop AI tools, new methods or other ways to justify the German's model based on changes in companies' perceptions around the failure and rapid changes in the economy. Findings: Artificial intelligence algorithms are often referred to as “black boxes” because the way they make decisions is vague and difficult to understand. This can make it difficult to comply with regulations and ensure transparency in financial risk assessment. Moreover, Altman's model, based on historical financial data, will not be sufficient in the age of artificial intelligence. While Altman's model has proven its value in the past, it does not fully capture the complexity of AI-driven financial performance. As AI systems evolve, they become more complex, which can lead to new scenarios that can cause traditional effects such as Altman's incalculable effects. Implication: using AI to make economic decisions poses additional risks and ethical issues. For example, research showing how certain demographic groups can be disadvantaged in credit management demonstrates how biases in AI systems can perpetuate injustice and discrimination.