Data Quality, Not Algorithms, Is Undermining Enterprise AI
Data quality in enterprise AI refers to how accurate, relevant, up-to-date, and well-governed an organization’s information is before it is fed into models, and poor data quality turns advanced AI systems into unreliable tools that waste compute, mislead users, and erode trust in automated decisions. While many teams blame model limits or prompt engineering, the deeper issue is that enterprise repositories are stuffed with redundant, obsolete, and trivial content. Industry estimates suggest more than a third of stored enterprise data is effectively garbage, and Gartner projects that 60% of AI projects will be abandoned due to poor data quality. When internal agents must parse millions of files, outdated policies and discontinued product documentation crowd out useful signals. The result is inaccurate outputs and higher token costs, even when the core models are strong. Fixing data quality is becoming the first requirement for any serious enterprise AI strategy.
Unstructured Data Cleanup Emerges as a First-Line Fix
Unstructured data cleanup is now a priority as enterprises confront years of accumulated files across shared drives and collaboration tools. A growing share of that content is ROT: redundant copies, obsolete formats no one can open, and trivial noise such as hidden system files or abandoned materials from departed staff. Clario, launched with USD 6 million (approx. RM27,600,000) in seed funding, connects directly to file systems like Google Drive, SharePoint, OneDrive, Box, and Confluence to scan metadata and flag likely garbage without opening files. Using checksums, naming patterns, access timestamps, and format support, its heuristics surface duplicates, legacy documents, and files untouched for years. Early customer work has uncovered garbage rates as high as 60%, including terabytes of discontinued product documentation and even full-length films. By removing ROT, teams cut storage costs and reduce the noise that confuses retrieval-augmented generation systems and internal agents, improving both performance and token efficiency.
Outcome-Based Pricing Aligns Cleanup Incentives with Results
One reason unstructured data cleanup has lagged is misaligned incentives: traditional tools bill for capacity or licenses, not for actually reducing garbage. Clario’s model shows how outcome-based pricing can change that dynamic. The platform only gets paid when customers act on a flagged file, whether they delete or archive it. This approach ties revenue directly to measurable data reduction rather than scanning or storage. To support that, Clario tunes its classification for precision, aiming to flag only what it is confident is garbage and routing decisions back to content owners through tools like Slack or Teams. The system then learns from user choices to become more autonomous over time. For CIOs and operations leaders wrestling with bloated repositories and rising AI costs, paying only when ROT is removed makes cleanup a clearer business case and connects data quality initiatives to visible savings and better AI behavior.
AI Data Governance Demands Durable Records of Decisions
Cleaning up data is only half the challenge; enterprises also need AI data governance that explains how decisions were made. As AI-assisted workflows spread into customer service, claims processing, fraud review, healthcare operations, and financial decision support, organizations face a simple but serious problem: when outcomes are questioned weeks or months later, they often cannot reconstruct what information the AI used or what it produced at the time. Obligra’s Verify platform addresses this by acting as a system of record for AI-assisted decisions. It captures prompts, responses, workflow context, timestamps, metadata, retrieval identifiers, environment details, and supporting evidence in one place. This richer record goes beyond standard logs that merely show an event occurred. It gives compliance, legal, risk, and audit teams the material they need to review, explain, or challenge AI outputs—without claiming to guarantee compliance itself—making enterprise data verification a practical, ongoing process.
Building Systems of Record Before Scaling Agentic AI
The emerging lesson for enterprise AI teams is clear: do not scale agentic workflows until data quality and systems of record are in place. Agents built on unclean knowledge bases must sift through outdated policies, obsolete support articles, and noise from millions of files, raising token usage and misclassification risks. As one Clario customer discovered, more than 20% of a 5.5 million-file corpus traced back to a handful of departed employees—a vivid sign of how much legacy content distorts current decisions. At the same time, Verify-style infrastructure shows why every AI-assisted decision needs retained context for later review. Together, unstructured data cleanup and enterprise data verification form the foundation of AI data governance. Organizations that treat data quality as a first-class product—cleaning ROT, defining trusted sources, and recording what AI does—will be better positioned to deploy agents that are reliable, auditable, and worth scaling.
