Enterprise Agentic AI Adoption Criteria

Narrative

The academic and technical literature on enterprise agentic AI adoption reveals a critical gap: while dozens of vendor platforms, industry analysts, and practitioner guides address operational deployment challenges, peer-reviewed research from primary academic outlets (arXiv, NeurIPS, ICML, ICLR, ACL) on this specific topic area remains sparse as of April 2026. The most rigorous academic reference—Kellogg and colleagues' 2025 work via MIT Sloan—documents that 80% of enterprise agentic AI implementation work is consumed by data engineering, governance, and workflow integration rather than model architecture or prompting, suggesting that operational and institutional barriers dominate academic research priorities. Concurrently, practitioner literature increasingly addresses three interconnected failure modes: (1) Model drift and version stability: enterprises lack standardized frameworks for detecting and responding to model behavior changes between releases, though IBM, Tribe AI, and SmartDev outline Population Stability Index, Model Context Protocol (MCP), and retraining governance as emerging practices. (2) Vendor lock-in and procurement: McKinsey and Kai Waehner's April 2026 analysis identifies API dependency, framework capture, data gravity, and ecosystem entanglement as structural lock-in vectors, with multi-model strategies and open standards (MCP, Agent2Agent) cited as mitigations. (3) Operational readiness and SLAs: Gigster data shows enterprise deployment at only 11% despite 65% pilot adoption, driven by system integration complexity (legacy APIs), security/access control gaps, and infrastructure immaturity. Newgen and DevOps.com outline agentic SLA management as an emerging operational pattern—autonomous monitoring, predictive escalation, and self-adjusting thresholds—though implementation at scale remains limited.