Denodo Study Finds AI Data Layer Delivers 290% ROI and 3x Faster Agent Deployment

Denodo has announced findings from an independent study by research firm Veqtor8, which reports that deploying a governed AI data layer can deliver a 290% return on investment (ROI) over three years, accelerate AI agent deployment threefold and reduce AI operating costs by 68%.

The report, The ROI of an AI Data Layer for Agentic AI, examines the financial and operational impact of using Denodo’s AI Data Layer compared with fragmented, custom-built data architectures.

As enterprises move generative AI projects from pilot stages into production environments with autonomous and semi-autonomous agents, access to live, governed and semantically consistent data is becoming a critical challenge. The study draws on in-depth interviews with enterprise practitioners, hyperscalers and technology partners across sectors including banking, telecommunications, chemicals manufacturing and logistics.

Quantifying the Business Impact
Veqtor8 developed a composite financial model based on the average scale of the enterprises interviewed. The model assumes growth from 100 AI agents in the first year to 900 in the third year, operating across 100 distributed enterprise data sources.

The study identifies several potential benefits over three years:

• 290% return on investment: The research reports a 2.9-fold increase in business value generated by AI initiatives, combining net cost avoidance and additional business value.
• Threefold faster agent deployment: Reusable, pre-built data products reduce the need to develop custom integrations for each new agent.
• 68% lower AI operating costs: Querying the AI Data Layer directly and filtering data before it reaches the model reduces context payloads from approximately 18,000 tokens to 5,760 tokens per task, with compute costs falling by a similar proportion.
• More than 75% integration and governance cost avoidance: The model attributes these savings to reduced connector proliferation and Model Context Protocol (MCP) server sprawl. It also estimates a 79% reduction in maintenance costs and a 76% reduction in policy administration effort.

The findings highlight the operational challenges of scaling AI agents across complex enterprise environments, where data is distributed among cloud platforms, software-as-a-service applications, legacy systems and operational databases.

Addressing MCP Server Sprawl
Without a unified logical data layer, enterprises may need to build and maintain separate MCP servers, retrieval-augmented generation (RAG) pipelines and security controls for individual data sources and agents. In regulated industries, where a production agent may interact with 30 to 50 systems, this can increase integration workloads and complicate governance.

“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.”

The Denodo Platform is designed to provide unified access to distributed data, consistent semantics and centralised policy enforcement without requiring large-scale data relocation. “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.”