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DATA MANAGEMENT Komprise: AI needs AI-ready data and there isn’t enough of it Data manager Komprise says its Unstructured Data for AI & Lakehouses Survey shows a gap between how mature enterprises believe their AI programs are and how much of their unstructured file data is prepared to feed those programs. In other words, they need more of their unstructured data made AI-ready. If your product pitch is making data ready for AI then you have to find customers whose data is not ready for AI and show them that making it ready is worth while; that there is a discernible AI project ROI. Finding customers can be made easier by getting the tech media to publish stories about survey reports showing x percent of potential customers don’t have AI-ready data. So here goes… The survey shows two-thirds of respondents (68 percent) describe their organization as mature or maturing on AI, defined as setting standards, automating business processes, agentic workflows and applying governance models. Yet only 7 percent have classified and cleaned the majority of their file data. As well, 57 percent say they have half or less of their data ready and 13 percent cannot say how much is ready at all. Krishna Subramanian, COO and cofounder of Komprise, said: “Our survey showed that although most enterprises are maturing concerning their adoption of AI, only 21 percent-26 percent of enterprises have quantified ROI on the critical areas of operations and revenue growth. 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.” Komprise says it unlocks unstructured data for AI while optimizing data placement to cut storage costs. The top 5 takeaways from its survey are: - AI returns map to data readiness.Low AI ROI correlates with low unstructured data readiness. To generate measurable and meaningful ROI on AI, improving data quality must be job number-one among IT, data and AI leaders. - Data readiness is an ongoing tactic Data readiness does not resolve as organizations grow adoption of AI across departments and functions. By investing in AI data management, enterprises can get ahead. Top pain points are metadata enrichment/extraction and clearing up redundant, obsolete and trivial (ROT) data to discover the right data for each project. Sophisticated metadata management builds trust and lowers costs - Metadata analysis and extraction across the complete data lifecycle creates a foundation of trust, where AI users and engineers can easily find the most relevant, specific data for a project, without running up AI costs and risks. - Lakehouses are behind on unstructured data. A scant 4 percent of enterprises have developed schema for most file data. Until unstructured data carries the metadata that lakehouses require, these platforms deliver a single source of truth for structured data only, eroding their usefulness for AI. - Automation is still immature for unstructured data preparation Filtering data for security risk is moving forward but less than one third filter for quality and 31 percent are not preprocessing unstructured data. The survey noted customer service is the highest area for quantified ROI at 38 percent. Quantified ROI for employee productivity and decision speed clocked 28 percent each, followed by revenue growth at 26 percent and operational savings at 21 percent. There are no details of what quantified ROI actually means for the surveyed organizations, just that there has been quantifiable ROI. Other AI data readiness surveys Many storage and data protection suppliers have issued AI data readiness reports. For example: - Seagate — 2026 Data Infrastructure Readiness Report (survey of 2,700+ enterprise tech decision-makers). Key findings: 99 percent expect AI to increase storage needs, but only 38 percent feel fully prepared; data quality/readiness and storage infrastructure ranked as top AI deployment challenges. - NetApp — Multiple reports, including the AI Space Race Report, Data Complexity Report (2024), and an IDC-sponsored Enterprise AI Maturity Study (2025). These examine data readiness, infrastructure optimization for AI, and gaps between perceived and actual preparedness (e.g., 84 percent still said storage was not fully optimized for AI in one study). - Hitachi Vantara — State of Data Infrastructure Global Reports (2024 and 2025 editions, surveying 1,200+ IT leaders). These focus on data infrastructure maturity, data quality as a driver of AI success, governance/security gaps, and the link between infrastructure readiness and AI ROI. - Cohesity has a Gaia solution brief discussing AI data readiness - Commvault — State of Data Readiness reports (e.g., for ANZ and Asia). These cover data management complexity, AI adoption risks, cyber resilience, and readiness challenges in regulated environments. - Iron Mountain — Reports such as The Information Supercycle: Aligning Data, Infrastructure and Capital for AI Scale and earlier AI-readiness research with FT Longitude. Findings highlight that data concerns delay or derail AI projects for a large majority of organizations, with emphasis on unstructured/legacy data and governance. - Veeam — Surveys examining the gap between confidence in data management/protection and proven AI readiness, recovery capability, and governance. - Dell Technologies — Studies such as the Modern Enterprise Readiness Study that address data readiness challenges for AI/GenAI alongside broader enterprise readiness. Read the 13-page Komprise survey report here to find its take on the topic. Bootnote Maturing is defined as "beginning to establish standards and use AI more strategically, automating key business processes and applying governance models across the organization." Mature is defined in the survey as: "deployed across multiple interconnected departments with agentic workflows and a unified governance and security strategy in place."
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