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

Denodo has announced findings from an independent study conducted by research firm Veqtor8. The report, titled The ROI of an AI Data Layer for Agentic AI, examines the financial and operational benefits of using a governed AI Data Layer in enterprise environments.

According to the study, organisations can achieve a 290% return on investment (ROI) over three years. The research also reports three times faster AI agent deployment and a 68% reduction in AI operating costs.

As businesses move beyond generative AI experiments, many are preparing to deploy autonomous and semi-autonomous agents in production. However, these systems need reliable access to current, governed and consistent enterprise data. The study identifies this requirement as a major challenge to scaling AI.

Veqtor8 based its findings on interviews with enterprise practitioners, hyperscalers and technology partners. Participants represented global banking, telecommunications, chemical manufacturing and logistics organisations. The research compared Denodo’s AI Data Layer with fragmented, custom-built data architectures.

Study Highlights Financial and Operational Benefits

The report uses a composite enterprise financial model based on the average scale of the organisations interviewed. It assumes growth from 100 AI agents in the first year to 900 in the third year. The model also covers 100 distributed enterprise data sources.

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The study identifies several potential benefits over three years:

290% return on investment: The model reports substantial financial gains through cost avoidance and additional business value.

Three times faster deployment: Reusable, pre-built data products reduce the need for custom integrations during each agent deployment.

68% lower AI operating costs: Filtering data before sending it to AI models reduces the amount of information processed.

More than 75% integration and governance cost avoidance: The report identifies potential savings from reducing connector duplication and fragmented governance systems.

Lower maintenance and administration costs: The study reports a 79% reduction in maintenance costs and a 76% reduction in policy administration effort.

The research also examines how data handling affects AI operating expenses. According to the report, context payloads can fall from approximately 18,000 tokens per task to 5,760 tokens. This reduction can lower computing costs by a similar proportion.

Addressing MCP Server Sprawl

Enterprise AI agents often need information from multiple cloud platforms, software-as-a-service applications, legacy systems and operational databases. Without a unified data layer, organisations may build separate Model Context Protocol (MCP) servers, retrieval-augmented generation (RAG) pipelines and security controls for different data sources.

This approach can increase integration work and complicate governance. The report notes that a single production agent in a regulated industry may interact with 30 to 50 different systems.

Andrew Milroy, chief analyst at Veqtor8 and author of the report, said reliable data access is essential for effective AI agents. He explained that connecting one system is relatively straightforward. However, coordinating numerous systems within a consistent workflow presents a much greater challenge.

Milroy said Denodo’s AI Data Layer can help organisations manage this complexity. It allows businesses to profile data once and reuse governed data products across authorised AI agents.

Denodo Emphasises Governance and Cost Efficiency

Alberto Pan, chief technology officer at Denodo, said enterprises need to balance deployment speed, governance and cost efficiency as they expand AI use.

He said the Denodo Platform provides a common foundation for accessing distributed data. It also supports consistent data semantics and centralised policy enforcement without requiring organisations to relocate large volumes of data.

According to Denodo, this approach can help businesses build more trusted, enterprise-ready AI agents while maintaining control over data access.

About Denodo

Denodo describes itself as an AI data layer company. Its Denodo Platform supports data access for analytics, self-service applications and AI-powered systems. The company focuses on transforming enterprise data into reliable insights for applications and intelligent agents.

About Veqtor8

Veqtor8 is an advisory and insights platform focused on artificial intelligence, data management, cybersecurity and cloud infrastructure. It provides research, executive workshops, reports and presentations to help organisations prepare for AI adoption, optimise data strategies and strengthen digital resilience.