ANALYSIS OF THE USE OF ARTIFICIAL INTELLIGENCE FOR AUTOMATE ENTERPRISE INFRASTRUCTURE MANAGEMENT PROCESSES

Abstract

This paper research the application of artificial intelligence (AI) for automating enterprise infrastructure management processes. The increasing complexity of IT systems and the volume of operational data have rendered traditional monitoring and control approaches inefficient. In response, AI-driven solutions — particularly those involving machine learning (ML), deep learning (DL), and natural language processing (NLP) — have emerged as effective tools for enhancing reliability, adaptability, and operational efficiency. The study focuses on three core areas of AI integration: resource monitoring and optimization, automation of technical support, and intelligent document processing. In resource management, ML/DL algorithms outperform rule-based systems in anomaly detection, load forecasting, and fault prevention. The use of deep reinforcement learning enables adaptive resource allocation and improved fault tolerance in dynamic environments. In technical support, NLP based chatbots can classify tickets, generate automated responses, and self-improve over time, significantly reducing mean time to resolution and enhancing user satisfaction. Intelligent document management systems, powered by neural networks and hybrid semantic-rule-based approaches, support automated classification, data extraction, and compliance checking in real time. To guide AI adoption, a structured methodology is proposed, comprising stakeholder identification, resource and data flow analysis, model selection, and staged integration. The paper introduces the concept of AI as a full-fledged participant in business processes rather than a passive tool. Within the Augmented Intelligence paradigm, AI agents and human experts collaborate—combining algorithmic precision with ethical reasoning and creativity. The practical implementation could demonstrate that AI integration leads to measurable improvements in efficiency, responsiveness, and decision quality across infrastructure-related workflows. Furthermore, it fosters a shift toward cognitive enterprise models, in which AI agents continuously learn and adapt to evolving business requirements. The findings offer a foundation for future research on human-AI collaboration, organizational transformation, and intelligent process design.