New Research Reveals Denodo AI Data Layer Delivers 290% ROI And 3X Faster Agent Deployments For Enterprise Agentic AI
(MENAFN- Mid-East Info) Dubai, UAE – October, 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.
Borderless Finance. AI-Native.
Your digital companion for smarter everyday choices. Built for how you live, earn, and grow. Move money at your pace with AI-powered finance, all in one tap.
Join the waiting list. Earn 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%.
Legal Disclaimer:
MENAFN provides the
information “as is” without warranty of any kind. We do not accept
any responsibility or liability for the accuracy, content, images,
videos, licenses, completeness, legality, or reliability of the information
contained in this article. If you have any complaints or copyright
issues related to this article, kindly contact the provider above.

Comments
No comment