Diagnostics the Efficiency of Implementing Artificial Intelligence in the Activities of IT Sector Enterprises

The article is devoted to the study of the issues of diagnostics the effectiveness of implementing artificial intelligence in the activities of IT sector enterprises. In modern conditions of digital transformation, AI has ceased to be just an innovative concept and has become a key tool for optimizing business processes, reducing costs and forming long-term competitive advantages. Special attention is paid to IT companies that are the first to adapt the latest technologies into their operational processes, product creation and interaction with customers. The purpose is to study the impact of implementing artificial intelligence in business processes and economic results of IT companies, as well as to substantiate the methodology for comprehensive diagnostics of the effectiveness of its use. The following research methods were used in writing the work: analysis and synthesis, graph-analytical, and systems approaches. An analysis of modern scientific research and reports of leading consulting companies (McKinsey, Deloitte, BCG) was carried out, demonstrating the significant potential of using AI in the activities of IT sector enterprises. A methodology for comprehensive diagnostics of the effectiveness of AI implementation has been developed, which includes economic, technological, organizational, social, ethical and strategic aspects. Key performance indicators have been identified for each group. It has been established that such an assessment makes it possible to identify both financial and non-financial results of AI implementation, which allows IT companies to make informed management decisions. The results of the study confirm that the use of AI in the activities of IT sector enterprises provides significant positive effects: increased profitability, accelerated software development, improved customer service quality, the formation of an innovative corporate culture and strengthening the market positions of companies. Promising areas of further research related to improving the methodology for assessing the effectiveness of artificial intelligence implementation and developing adaptive diagnostic models for different types of IT enterprises have been outlined.