Komprise Survey of Unstructured Data and AI Reveals Wide Gap Between Perceived AI Maturity and AI Results
Campbell, California — Komprise, the leader in analytics-driven unstructured data management, today announced the results of a new survey on AI data management and data readiness. Enterprise organizations say they are largely maturing or mature with AI (68%), yet the ROI numbers tell a different story, as only 28% of organizations on average have quantified ROI from AI.
The September survey of 500 technical decision makers from IT managers and data engineers to directors of AI, CIOs and CTOs uncovered a significant gap in unstructured data readiness: 93-96% of organizations report that most of their data is not ready for AI and lakehouses. Access the full report at komprise.com/survey.
Enterprise AI programs are running ahead of the data underneath them, at a time when AI’s safety and validity are under fire more than ever before. The Komprise Unstructured Data for AI & Lakehouses Survey indicates a wide gap between how mature enterprises believe their AI programs are and how much of their unstructured file data is prepared to feed those programs, which is resulting in a mismatch between perceived maturity and actual ROI.
Top pain points for AI data readiness include finding the right data for a project and cleaning up low quality and duplicate data, followed by metadata extraction and enrichment.
Outside research affirms the complexity of getting enterprise data filtered, classified and governed for AI. Gartner found that 63% of organizations either lack or are unsure they have the right data management practices for AI.
Highlights of the Survey
Unstructured data readiness: Only 7% of organizations have 70% or more of their data cleaned, prepared and classified for AI, while the majority (57%) have 50% or less ready.
Schema for lakehouses: Only 4% have more than 70% of their unstructured data ready, with schema, for analysis in a lakehouse and only 24% have more than half ready and available.
Data quality: When it comes to the largest gaps in data readiness, the majority (nearly 50%), cite finding the right data for a project and cleaning up ROT, followed by metadata extraction and enrichment (42%).
The most popular data lakehouse: Nearly half (49%) use Microsoft Fabric, followed by Google Big Query (32%), Amazon Redshift (28%), Databricks (27%) and Snowflake (22%).
Data preparation tactics: Over 70% do not yet have the capability for advanced classification techniques that discover stale data and allow users to search and find the right data for specific AI use cases.
Data-first strategy: Making unstructured data available for query in AI and lakehouses without moving it was deemed a priority (46%) over getting AI ready infrastructure in place (34%).
AI maturity: 48% say they are maturing* and 20% say they are mature** in AI, with standards, strategy and governance forming and the most mature deploying agentic workflows connected across departments.
AI ROI: Customer service is the highest area for quantified ROI at 38%. Quantified ROI for employee productivity and decision speed clocked 28% each, followed by revenue growth at 26% and operational savings at 21%.
“Our survey showed that although most enterprises are maturing concerning their adoption of AI, only 21%-26% of enterprises have quantified ROI on the critical areas of operations and revenue growth,” said Krishna Subramanian, COO and cofounder of Komprise. “Enterprise IT and AI leaders recognize that data intelligence is lacking but the pathway to ensuring high data quality is not straightforward. It begins with visibility and analytics across storage silos, unstructured data classification and industry-specific metadata extraction.”
About Komprise
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