Operationalizing Autonomous AI: Architecting the Agentic Enterprise on a Converged Data Foundation
In its latest report, Operationalizing Autonomous AI: Architecting the Agentic Enterprise on a Converged Data Foundation, completed in partnership with Snowflake, Futurum Research examines why enterprises must move beyond bolted-on AI applications and adopt a natively governed, multi-cloud data…
Futurum's Brad Shimmin,
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Brad Shimmin, The Futurum Group, "Operationalizing Autonomous AI: Architecting the Agentic Enterprise on a Converged Data Foundation," August 27, 2026. https://preview.erikbethke.com/research-reports/operationalizing-autonomous-ai-on-a-data-foundation/

Enterprise leaders have moved well past experimenting with generative AI. Boards and C-suites now expect autonomous agents that can execute complex, multi-step business processes, not just summarize documents or answer questions. But most organizations built their AI initiatives on fragmented, bolted-on infrastructure: data scattered across disconnected clouds, rigid legacy systems, and unverified schemas that leave agents unable to act with confidence.
To move from pilot purgatory to production, organizations need a governed, natively converged data foundation that grounds large language models in a single semantic truth and lets agents read and write back to systems of record securely. The most effective approaches unify semantic context, open storage standards, and closed-loop execution to control cost and risk while scaling autonomous work.
In our latest thought leadership report, Operationalizing Autonomous AI: Architecting the Agentic Enterprise on a Converged Data Foundation, completed in partnership with Snowflake, Futurum Research covers why enterprises must move beyond bolted-on AI applications and adopt a natively governed, multi-cloud data foundation that provides model optionality and secure, closed-loop execution for autonomous agents.
In this report, you will learn:
- Why 44.5% of enterprises plan to increase semantic layer spend over the next 24 months to anchor a governed “Agentic Control Plane”
- The architectural barriers blocking real operational execution, including the 24.6% of organizations that cite the inability to write back to systems of record as their primary bottleneck
- How a unified semantic layer and open storage standards ground both human analysts and AI agents in the same source of truth
- Five recommendations for architecting a secure, cost-controlled foundation for autonomous AI, drawn from Futurum primary survey data and in-depth interviews with enterprise technology leaders
If you are interested in learning more, be sure to download your copy of Operationalizing Autonomous AI: Architecting the Agentic Enterprise on a Converged Data Foundation today.
Published by Futurum.
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