Enterprise AI is undergoing a fundamental shift, moving from conversational chatbots to autonomous agents that make decisions and execute operational workflows. That shift stalls before reaching production more often than not. Futurum Research’s 1H 2026 Data Intelligence, Analytics, and Infrastructure Decision Maker Survey found enterprise agentic AI adoption leveling off at 47%, with more than a quarter of technology leaders citing data integration difficulties as their primary bottleneck.

Closing that gap takes more than better prompts. It takes an active, neutral foundation that links distributed data stores, embeds domain-specific semantic models, enforces runtime governance, and establishes an operational memory loop, so autonomous agents gain the governed business context needed to reason, execute, and act reliably across hybrid and multi-cloud environments.

In our latest thought leadership brief, Closing the Enterprise Context Gap, completed in partnership with Teradata, Futurum Research examines why autonomous AI agents stall at the production threshold and outlines the governed context architecture technology leaders need to move from pilots to reliable, auditable production deployments, including pre-built Industry Knowledge Models that ground agents in verified domain logic.

In this brief, you will learn:

• Why enterprise agentic AI adoption is leveling off at 47%, and which architectural bottlenecks are blocking production deployment
• The operational and economic costs of stitching together point tools like vector RAG, data catalogs, and standalone knowledge graphs
• How a four-tier context architecture, grounded in pre-governed Industry Knowledge Models, gives agents deterministic business context
• Five pragmatic next steps for moving autonomous agents from brittle proofs-of-concept to reliable production infrastructure

If you are interested in learning more, be sure to download your copy of Closing the Enterprise Context Gap today.