Ask your head of sales what Q3 revenue was and get $14.2M. Ask the CFO the same question and get $12.8M. Same underlying data, different answers, because business logic lives in different systems and every tool interprets it differently. This is metric drift, and it is one of the most expensive problems in enterprise AI, because agents that run on
Most companies have adopted AI and most are running it somewhere, but enterprise AI adoption at production scale is a different challenge from running a pilot. Getting it to run reliably, safely, and with measurable business return is a different problem entirely, and that is what 161 documented enterprise engagements between 2015 and 2026 reveal. Kanerika’s State of Enterprise AI
TL;DR Data governance companies help enterprises keep data accurate, compliant, and well-managed — covering data quality, cataloging, lineage, access control, and regulatory reporting. The 15 leading 2026 providers differ by strength: some specialize in compliance, others in Microsoft Purview implementation, cataloging, or industry-specific rules. The right partner depends on your regulatory exposure, existing platform (especially Microsoft ecosystem depth), and whether
FoodPharma’s Reporting Transformation with Microsoft Fabric and Kanerika
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Data Accuracy in Premium Reconciliation with Automation for Fortegra | Kanerika's case study
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