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AI and Data Are Clear Priorities Despite Significant Data and Integration Hurdle…
Organizations Must Manage Complex Cloud Analytics Costs by Positioning AI as a…
AI Delivers Value Through Better Decisions and Forecasts, but Challenges Lurk
Optimism for AI Agents Is High, but Successful Deployment Hinges on Several Fact…
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Research Report: Optimizing Cloud Analytics Costs in an Agentic AI Future
Research Report
Oct 30, 2025
by
Mike Leone, Emily Marsh
The transformative potential of agentic AI in cloud analytics to promote deeper insights and greater innovation is exciting. However, fully realizing this potential hinges on effectively managing often-overlooked cost demands. Organizations regularly face significant expenses from the massive data generation inherent in analytics, complex storage for diverse analytical datasets, intensive compute for processing sophisticated models, and continuous real-time operations that leverage historical context. If unchecked, these escalating costs can impose limitations on the full impact of agentic AI.
Organizations are navigating these evolving cost paradigms in analytics with various strategies focused on optimizing infrastructure and managing data and compute expenses. By determining best practices, businesses can ensure their agentic AI implementations are not only powerful but also sustainable and cost effective.
To gain further insights into these trends, Omdia surveyed 375 technical and business stakeholders in North America (U.S. and Canada) involved with or responsible for evaluating, purchasing, managing, and building analytics and business intelligence solutions.
Page Count: 22
Table of Contents
Executive Summary
Report Conclusions
Introduction
Research Objectives
Research Findings
AI and Data Are Clear Priorities Despite Significant Data and Integration Hurdles
Organizations Must Manage Complex Cloud Analytics Costs by Positioning AI as a Spending Concern and Long-term Efficiency Driver
AI Delivers Value Through Better Decisions and Forecasts, but Challenges Lurk
Optimism for AI Agents Is High, but Successful Deployment Hinges on Several Factors
