OPTIMIZATION OF EMPLOYEES' TRAINING AND DEVELOPMENT PROCESSES IN IT COMPANIES WITH THE HELP OF ARTIFICIAL INTELLIGENCE

Abstract

This article explores the impact of artificial intelligence (AI) on learning and development (L&D) in IT companies, focusing on the use of large language models (LLMs) like GPT-3.5 and GPT-4 Turbo. These models enhance personalized learning pathways, apply the Feynman method for knowledge verification, and optimize software code. Experimental results demonstrate that AI significantly improves technical mentorship quality, reduces costs, and increases training effectiveness. The study addresses challenges in AI integration, such as data quality and the necessity for human expertise, and provides recommendations for integrating AI effectively into corporate training.

AI technologies enable the personalization of learning experiences, making training more engaging and effective by adapting to individual needs. By automating and optimizing L&D processes, AI allows organizations to scale their initiatives efficiently and ensures deep understanding through innovative methods like the Feynman Technique, which breaks down complex topics into simpler concepts.

However, integrating AI in L&D programs involves challenges that require careful implementation and ongoing evaluation to maximize effectiveness and mitigate risks. The article underscores the importance of combining AI with human oversight to align training with ethical standards and business objectives. It encourages companies to adopt AI as a strategic component of their training programs, focusing on developing employee skills for effective AI use and management.