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.
Author Information
Brad Shimmin is Vice President and Practice Lead, Data Intelligence, Analytics, & Infrastructure at Futurum. He provides strategic direction and market analysis to help organizations maximize their investments in data and analytics. Currently, Brad is focused on helping companies establish an AI-first data strategy.
With over 30 years of experience in enterprise IT and emerging technologies, Brad is a distinguished thought leader specializing in data, analytics, artificial intelligence, and enterprise software development. Consulting with Fortune 100 vendors, Brad specializes in industry thought leadership, worldwide market analysis, client development, and strategic advisory services.
Brad earned his Bachelor of Arts from Utah State University, where he graduated Magna Cum Laude. Brad lives in Longmeadow, MA, with his beautiful wife and far too many LEGO sets.
