AI Data Layer Boosts Enterprises' ROI by 290%

PALO ALTO, Calif., Oct. 20, 2026 /CourierPR/ -- The AI data layer company, Denodo, has announced the findings of a comprehensive independent study conducted by research firm Veqtor8, revealing 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.

Enterprise deployment of a governed AI Data Layer has emerged as a primary bottleneck for enterprises transitioning from experimental generative AI pilots to running populations of autonomous and semi-autonomous agents in production.

Key findings of the study include a 2.9X More Business Value Generated from AI Initiatives, comprising both net cost avoidance and total business value added. The research also reveals that enterprise deployment of a governed AI Data Layer enables 3x faster agent deployment velocity, with agents reaching production three times faster by leveraging pre-built, reusable data products instead of building custom integrations for each deployment.

The study further highlights a 68% reduction in AI operating costs, achieved by delegating queries directly to the AI Data Layer and filtering data prior to model ingestion. This results in a significant reduction in context payloads per task, dropping from ~18,000 tokens to ~5,760 tokens, and a corresponding drop in compute costs by a similar amount.

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. Over a three-year period, the model demonstrates a 2.9X More Business Value Generated from AI Initiatives, comprising both net cost avoidance and total business value added.

Key quantitative outcomes include a 3X Faster Agent Deployment, with agents reaching production three times faster by leveraging pre-built, reusable data products instead of building custom integrations for each deployment. The study also reveals a 68% Lower AI Operating Costs, achieved by delegating queries directly to the AI Data Layer and filtering data prior to model ingestion, resulting in a reduction in context payloads per task and compute costs.

The research also highlights a >75% Integration & Governance Cost Avoidance, achieved by eliminating connector proliferation and MCP server sprawl, reducing maintenance costs by 79% and policy administration effort by 76%. Enterprises can now profile data once and reuse governed data products across every agent entitled to access them, overcoming the "MCP Server Sprawl" challenge.