REPORTED DEVELOPMENT

Denodo published Veqtor8 research on data-infrastructure economics for AI agents, reporting 290% three-year ROI, three-times faster agent deployment and 68% lower AI operating costs.

The stated methodology is a composite financial model scaling from 100 agents in year one to 900 in year three across approximately 100 enterprise data sources. The figures are not measured and certified ROI at one customer.

WHY IT MATTERS TO THE MARKET

The model offers parameters for comparing centralised data architectures with individual integrations. Published percentages can inform sensitivity analysis but are not automatically fashion-and-luxury benchmarks. Infrastructure, model consumption, integration, governance and attributable operating benefits remain separate.

MARKET EVIDENCE

SOURCE-LINKED INDICATORS

The large return estimate is a model output, not a realised customer return.

290%Three-year composite-model return on investmentMODELLED ROI3-YEAR MODEL / Veqtor8 for Denodo / composite agent deploymentComposite three-year model informed by eight interviews, not an observed customer's ROI. Deployment speed and cost reductions are model assumptions/results, not guaranteed outcomes.DENODO / VIEW SOURCE
3×Modelled speed of agent deploymentMODELLED COMPARISON3-YEAR MODEL / Composite data-platform evaluationComposite three-year model informed by eight interviews, not an observed customer's ROI. Deployment speed and cost reductions are model assumptions/results, not guaranteed outcomes.DENODO / VIEW SOURCE
−68%Modelled AI operating costMODELLED COST CHANGE3-YEAR MODEL / Composite financial assumptionsComposite three-year model informed by eight interviews, not an observed customer's ROI. Deployment speed and cost reductions are model assumptions/results, not guaranteed outcomes.DENODO / VIEW SOURCE
WHAT THE DATA SUGGESTS

A defensible investment case would reconstruct the model using the buyer's actual number of agents, data systems, labour rates, supervision and infrastructure expenditure.

INTERPRETATION BOUNDARY

The financial result uses a constructed organisation scaling from 100 to 900 agents, not observed benefits at one enterprise. Model inputs and sensitivity have to be independently tested.

ORIGINAL REFERENCES