Authors: Navya Sri Maddukuri
Abstract: 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 systemati-cally sense automation opportunities, seize value through autonomous multi-step work-flows, 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 devel-ops and validates an Agentic AI Strategic Maturity Index (AAMI). Structural equation mod-eling 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) com-pared to fragmented AI tool adoption. Qualitative analysis of 42 executive interviews re-veals 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 perfor-mance in the post-generative AI era.
DOI: http://doi.org/10.5281/zenodo.20837491
