Abstract
The use of artificial intelligence in management allows enterprises to more effectively solve the tasks of automation, data analysis and strategic decision-making, which is key to competitiveness in the modern business environment. The growing relevance of the use of artificial intelligence technologies is explained by several aspects. First, the growth of data volumes. Secondly, the growing complexity of business processes. Third, competitive advantages. Finally, in the context of saving resources. Artificial intelligence technologies are developing rapidly, and it is important to understand how these innovations can be optimally implemented in management practices. Businesses use artificial intelligence to explore internal and external environments, solve problems, and even achieve specific goals. Artificial intelligence in the field of enterprise management is a real revolutionary step. The main directions of using artificial intelligence in management processes are: analysis of large volumes of data, management decisionmaking, automation of routine tasks, strategic planning. AI data analysis capabilities make it possible to effectively use large volumes of information for decision-making. Machine learning models can predict trends in market conditions, helping businesses adapt to change. Machine learning algorithms can analyze many factors and take into account their influence on decisionmaking, which helps to avoid human errors and ensure effective management. AI can automate routine tasks, freeing up managers' time for more strategic tasks. Forecasting models can take into account a variety of factors, from economic to social, allowing companies to be more flexible and adapt to changes in the environment. However, with this comes challenges such as ethical issues, data security and risks of bias, plausibility in systems. The correct solution of these aspects requires a comprehensive approach and further research into the stages of development and introduction of artificial intelligence into management processes at the enterprise.
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