Most Enterprise AI Efforts Lack Quantified ROI Results
Data preparation gaps leave nearly three-quarters of firms without measurable financial returns from their AI investments.
Updated on Oct. 15, 2026 in Artificial Intelligence
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Are you confident that your workplace investments in artificial intelligence are currently delivering measurable results?
A new survey of 500 technical decision makers finds that while most enterprises claim to be maturing their AI operations, few have achieved measurable financial returns. The research reveals that data readiness remains a primary bottleneck, with a vast majority of organizations lacking prepared data for AI and lakehouse environments.
Why it matters
The findings underscore a significant gap between corporate AI adoption claims and the foundational data engineering required to support production-grade models. This discrepancy highlights why enterprises are struggling to move beyond pilot programs despite high levels of reported AI investment.
Only 7% of organizations have reached a threshold where at least 70% of their data is cleaned and prepared for AI. Meanwhile, 49% of firms report utilizing Microsoft Fabric for their data lakehouse architecture compared to 32% using Google Big Query.
The players
Komprise
A Campbell, California-based data management software provider specializing in unstructured data analysis and storage migration.
Microsoft Fabric
A unified analytics platform from Microsoft that integrates data integration, engineering, and storage into a single lakehouse environment.
The details
The report indicates that organizations lack the necessary data management frameworks to process unstructured data into formats suitable for machine learning. This preparation process typically involves cleaning, labeling, and organizing raw information into centralized storage systems known as lakehouses. Without these pipelines, models often lack the high-quality input required to generate consistent or verifiable business outputs.
Timeline
September 2026: Survey data collected from 500 technical decision makers.
October 15, 2026: Results of the AI readiness survey released.
The Tech Race
This data marks a reality check for the industry's rapid move toward automated data lakehouses, where platform adoption is currently outpacing the underlying data quality. It suggests that firms using architectures like Microsoft Fabric are still largely in the exploratory phase of establishing usable data pipelines.
IT managers and data engineers should note that 38% of current quantified ROI is concentrated specifically in customer service workflows, suggesting that initial value is currently limited to front-end automation. Organizations without automated data cleaning protocols face a significant barrier to replicating these results in more complex back-end operations.
The takeaway
Enterprise leaders should prioritize data sanitation pipelines before scaling AI deployments, as infrastructure readiness currently remains the primary limiting factor for ROI. Follow upcoming industry benchmarks to see if the percentage of cleaned data increases as firms move away from pilot-stage testing.
Further reading
For broader trends in infrastructure readiness, see our coverage of Artificial Intelligence.
More information
For detailed methodology and findings, access the full report on the company's portal.
Live Poll
Are you confident that your workplace investments in artificial intelligence are currently delivering measurable results?
