PALO ALTO, Calif. October 20, 2026. Denodo, the AI data layer company, announced the findings of a comprehensive independent study conducted by research firm Veqtor8. The research report, titled "The ROI of an AI Data Layer for Agentic AI," reveals that enterprise deployment of a governed AI Data Layer delivers a 290% return on investment (ROI) over three years, enables 3x faster agent deployment velocity, and achieves a 68% reduction in AI operating costs. 

As enterprises transition from experimental generative AI pilots to running populations of autonomous and semi-autonomous agents in production, reliable access to live, governed, and semantically consistent enterprise data has emerged as a primary bottleneck. Based on in-depth interviews with enterprise practitioners, hyperscalers, and technology partners across global banks, telecommunications groups, chemical manufacturers, and logistics leaders, Veqtor8’s research quantifies the economic and operational advantages of implementing Denodo’s AI Data Layer compared to fragmented, custom-built architectures. 

Key Findings and Economic Impact 

The research constructs a composite enterprise financial model, representing the average size of all enterprises interviewed. This model scaled from 100 agents in Year 1 to 900 agents in Year 3 across 100 distributed enterprise data sources. Key quantitative outcomes over a three-year period include: 

• 2.9X More Business Value Generated from AI Initiatives: Comprising both net cost avoidance and total business value added. 
• 3X Faster Agent Deployment: Agents reach production three times faster by leveraging pre-built, reusable data products instead of building custom integrations for each deployment. 
• 68% Lower AI Operating Costs: By delegating queries directly to the AI Data Layer and filtering data prior to model ingestion, context payloads per task drop from ~18,000 tokens to ~5,760 tokens, and compute costs drop by a similar amount. 
• >75% Integration & Governance Cost Avoidance: Eliminates connector proliferation and MCP server sprawl, reducing maintenance costs by 79% and policy administration effort by 76%. 

Overcoming the "MCP Server Sprawl" Challenge 

Without a unified AI data layer, enterprise agents require custom Model Context Protocol (MCP) servers, RAG pipelines, and per-agent security controls for every underlying data source. In regulated industries where a single production agent may touch 30 to 50 disparate cloud, SaaS, legacy, and operational systems, this approach causes severe integration drag and governance vulnerabilities. 

"Agents are only as effective as the data they can reach," said Andrew Milroy, Chief Analyst at Veqtor8 and author of the report. "Connecting an individual system is straightforward, but orchestrating dozens of systems into a coherent, reliable workflow under consistent security policies is extraordinarily complex. Denodo's AI Data Layer abstracts this complexity, allowing enterprises to profile data once and reuse governed data products across every agent entitled to access them." 

"Enterprises cannot afford to choose between speed, governance, and cost efficiency when scaling AI," said Alberto Pan, Chief Technology Officer at Denodo. "By unifying access, semantics, and centralized policy enforcement across distributed data estates without requiring massive data relocation, the Denodo Platform provides the essential foundation for trusted, enterprise-grade AI agents." 

Research Report Availability 

To download a complimentary copy of the full Veqtor8 research report, "The ROI of an AI Data Layer for Agentic AI," please visit denodo.com.