Agentic Generative Artificial Intelligence in Enterprise Organizational Behavior: An Integrated Scholarly-Practitioner Mathematical and Theoretical Framework

This comprehensive review paper synthesizes current research to develop an integrated framework for understanding agentic generative artificial intelligence (GenAI) in organizational behavior contexts. We propose a tripartite framework combining visual architectural models, mathematical formulations, and scholarly-practitioner perspectives that addresses the transformation from traditional human-centric to hybrid human-AI enterprises. Our analysis spans individual, group, and organizational levels, examining how autonomous AI systems reshape decision-making structures, communication patterns, leadership dynamics, and ethical governance. The framework includes: (1) visual blueprints for multi-agent systems and governance architectures; (2) mathematical models that quantify human-AI synergy coefficients (typically in the 0.6-0.9 range), performance improvements (often in the 1.5-2.5× baseline range), and optimal role allocation ratios; and (3) implementation strategies bridging theoretical insights with practical applications. We identify critical success factors including executive commitment (explaining 25-30% of variance), change management processes (15-20%), and technical infrastructure (10-12%), along with implementation success rates typically between 65-85% and adoption periods ranging from 4-8 months. As a review and synthesis paper, this work consolidates current knowledge while proposing integrated frameworks for researchers and practitioners navigating the complex intersection of agentic AI and organizational behavior.