Start: Q1/2027
Publication: Q2/2027
Region: Global Research
Authors: Shawn Rodgers
Companies are moving agentic AI from sandboxes into production. As agents gain access to enterprise data, tools, and business processes, model performance alone is no longer enough. Weak data foundations, fragmented architectures, unclear ownership, and governance designed for conventional AI can limit progress. As the successor to BARC’s Lessons From the Leading Edge study, this global survey will repeat selected questions and compare results over time to show how adoption, priorities, obstacles, and practices are changing.
It also tracks industry-specific use cases, including industrial AI, and examines how earlier lessons on data quality, cost, and delivery structures evolve as AI becomes more autonomous. The study identifies the foundations, operating models, and controls that distinguish leaders: agent identity, orchestration, runtime governance, observability, context engineering, and cost management. Readers can benchmark readiness and prioritize the steps required for productive agentic AI. It asks where autonomy creates value and where human control remains necessary.