Barriers to agentic ai enterprise transformation

This study identifies, classifies, and critically analyzes barriers to enterprise transformation under the influence of agentic AI – autonomous software that leverages large language models (LLMs) to perceive its environment, reason through complex tasks, plan and execute actions, and use tools to achieve goals with minimal human oversight. A critical narrative literature review of 30 sources (2019–2026) was conducted. Barriers were identified inductively through open and axial coding; Sociotechnical Systems (STS) theory was then applied as an interpretive lens to map the resulting dimensions onto social and technical subsystems and analyze cross-subsystem interactions. Twenty-nine barriers were classified into five dimensions: technological (7), organizational (7), human (6), governance and regulatory (4), and economic (5). Each barrier was assessed for agentic specificity. Three barriers were identified as agentic-specific (error propagation in multi-agent systems, role ambiguity, accountability diffusion). At the same time, the remaining 26 are carried over from prior digital transformation waves – 22 in amplified form and 4 unchanged. STS mapping based on root-cause analysis revealed that 12 barriers originate in the technical subsystem and 17 in the social subsystem, with governance serving as the social subsystem's primary mechanism for managing the technical subsystem. Five interaction mechanisms were identified, with the majority propagating across the subsystem boundary. Agentic AI transformation barriers constitute an interdependent sociotechnical system rather than isolated obstacles. The governance calibration problem – balancing control with the autonomy that gives agentic AI its value – emerges as the STS joint optimization challenge: governance, as the social subsystem's mechanism for managing the technical subsystem, must simultaneously enable and constrain autonomous operation. The taxonomy provides a diagnostic framework for identifying priority barrier dimensions and understanding cross-dimensional amplification mechanisms. The agentic-specificity classification helps organizations distinguish challenges that require novel approaches from those that are addressable with established practices.