SYSTEMATIC USE OF ARTIFICIAL INTELLIGENCE IN BUSINESS PROCESSES OF ENTERPRISES

Abstract

The article is devoted to the study of the use of artificial intelligence in various functional areas of enterprise activity. The impact of AI on socio-economic efficiency is assessed. The need for systematic implementation of AI to ensure sustainable development is substantiated. It is stated that competitive enterprises need to change their approach to content marketing, video advertising, SMM. It is highlighted that artificial intelligence allows you to create effective content, personalize interaction with customers, and analyze large amounts of information. In the field of logistics, AI is used in supply chain management. New AI solutions will allow you to better identify threats in real time, increasing the level of protection against cyberattacks and fraud. Enterprises seek to reduce costs for artificial intelligence through the use of cloud computing and modular models. One of the trends is also the development of "edge AI" - a technology that allows you to process AI data directly on devices. It has been found that businesses are beginning to invest in “green AI” strategies that involve the use of energy-efficient models and alternative energy sources. In legal support of business, AI is used to automate routine tasks, prepare contracts, analyze legal documents, and even make legal decisions. The problem of using AI is confidentiality and regulation. The EU defines new standards of security and ethics in the use of artificial intelligence. It has been noted that there is growing interest in using blockchain technologies in combination with artificial intelligence to increase transparency and security of legal processes. Thanks to such solutions, it is possible to avoid document forgery and create more effective mechanisms for verifying contracts. Financial companies are actively implementing Generative AI in trading operations, forecasting market trends, and risk management. AI models can analyze huge amounts of market data and create personalized financial recommendations for investors. The problem is monetization, as most AI projects so far are focused on improving operational efficiency rather than creating new revenue streams.