AGENTIC ARTIFICIAL INTELLIGENCE AS A TOOL FOR IMPROVING OPERATIONAL EFFICIENCY AND TRANSFORMING ENTERPRISE BUSINESS MODELS

This article examines agentic artificial intelligence as an emerging class of technologies that fundamentally reshapes business process automation, shifting the paradigm from reactive assistant systems to autonomous solutions capable of independent planning, decision-making, and execution of multi-step actions within defined authority boundaries. The aim of this study stems from a growing realization that traditional AI implementation strategies are failing to deliver expected returns. This discrepancy between strategic ambitions and practical outcomes calls for a systematic investigation of alternative approaches to AI integration, particularly those offering higher autonomy and operational transformation potential. The study demonstrates that agentic AI fundamentally transforms the cost structure of business operations by converting variable labor expenses into predominantly fixed and highly scalable technology costs, thereby altering the economics of enterprise operations. The analysis reveals that organizations implementing agentic AI achieve dramatic operational improvements including task completion time reductions and significant cost optimizations through the elimination of manual inefficiencies. The findings further show that successful scaling of agentic systems depends not so much on technological excellence as on organizational readiness, in particular the development of appropriate governance frameworks for managing autonomous systems, ensuring transparency and explainability in automated decision-making, and integrating AI agents effectively into hybrid human-machine teams while addressing workforce skill gaps. The practical value of this research lies in providing business leaders and technology managers with actionable insights for strategic decision-making regarding AI investments, offering a systematic framework for evaluating deployment opportunities across different business functions, understanding critical success factors that differentiate successful implementations from failed pilots, and anticipating organizational changes required for effective integration. The conclusions provide direction for bridging the persistent gap between AI technological capabilities and practical business outcomes, while outlining promising avenues for future research into economic impact longer-term socioeconomic consequences of widespread AI adoption on workforce composition and organizational structures.