Agentic AI Strategy As A Dynamic Capability: How Autonomous Systems Reshape Enterprise Transformation

As organizations transition from generative AI experimentation to agentic AI deployment, traditional frameworks for AI strategy have become structurally insufficient. This study conceptualizes agentic AI strategy as a dynamic capability through which firms systematically sense automation opportunities, seize value through autonomous multi-step workflows, and reconfigure governance, talent, and data infrastructures to sustain competitive advantage. Employing a longitudinal mixed-methods design — integrating annual-report text mining, AI investment announcements, patent data, and executive interviews from 312 large public firms across seven industry sectors between 2021 and 2026 — the study develops and validates an Agentic AI Strategic Maturity Index (AAMI). Structural equation modeling confirms that integrated agentic AI strategies are associated with significantly higher operational performance (β = 0.35, p < .001) and revenue growth (β = 0.29, p < .001) compared to fragmented AI tool adoption. Qualitative analysis of 42 executive interviews reveals five dominant strategic challenges: orchestration complexity, governance lag, talent asymmetry, value attribution difficulty, and cultural resistance to human-AI teaming. The paper advances a novel theory of autonomous digital transformation, provides empirical evidence on AI-driven competitive advantage, and offers actionable strategic guidance for executives managing enterprise-wide AI agents. Findings suggest that agentic AI maturity, not mere AI investment intensity, is the pivotal differentiator of sustained enterprise performance in the post-generative AI era.