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PALO ALTO, CA — March 10, 2026 — K2view, a global leader in data management and AI-ready data solutions, today published a new benchmark report titled The 2026 State of Enterprise Data Readiness for GenAI. The report finds that many organizations are preparing to move GenAI into production while still relying on analytics-oriented data architectures that were not designed for operational AI workloads. That creates growing risk as these systems begin operating against live enterprise data at scale.
Based on a survey of 300 senior IT and data executives at U.S. and U.K. companies with 1,000+ employees, the report examines how prepared enterprises really are to support GenAI and agentic AI in production environments.
The report surfaces several findings that point to a widening gap between GenAI ambition and enterprise data readiness:
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Production plans are accelerating, even as core deployment concerns remain unresolved: 45% of organizations plan early production GenAI deployments in 2026, up from 2% reporting production deployments in 2024. While responsible-use guardrails (76%) and workforce skills (66%) rank as the top overall concerns, enterprise data readiness (62%) and the reliability of LLM responses (52%) emerge as the most-cited technical barriers to production GenAI.
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Enterprise data readiness obstacles remain concentrated around quality, fragmentation, security, and real-time access: The top concerns about using enterprise data for GenAI in production are data quality and consistency (59%), fragmented data across systems (50%), and data security and privacy (50%), followed by the challenge of enabling real-time data integration and access (33%).
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Enterprises are relying heavily on analytics-oriented and API-based platforms for workloads they were not built to support: Respondents most commonly cite data warehouses (78%) and operational systems of record (66%) as foundational sources for GenAI, alongside lakehouses (58%) and vector databases for knowledge bases and unstructured data (57%). But these technologies were largely designed for analytics, point-to-point application integration, and document retrieval — not for inference-time, operational AI use cases such as agentic workflow automation, claims processing, or real-time customer service interactions.
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Next-wave agentic AI use cases remain early, even as expectations rise: Only 13% plan to deploy agentic AI applications to production in 2026, and MCP adoption remains largely exploratory: 53% are assessing vendors and approaches, while just 1% report MCP as operational or in production. The report also highlights an emerging cost pressure: retrieved data context can represent roughly 50–65% of query token costs, elevating data strategy from an engineering topic to an executive cost-to-serve decision.
Ronen Schwartz, CEO of K2view, commented: “The industry is trying to operationalize GenAI on top of data architectures built for analytics. That may be enough for pilots, but it breaks down in production, where AI systems need trusted, governed, real-time access to enterprise data in the flow of work. APIs, lakes, and vector stores each play a role, but on their own, they are not enough to support production-scale enterprise GenAI.”
To view and download “The State of Enterprise Data Readiness for GenAI”, visit: https://www.k2view.com/genai-adoption-survey-2026 .
About K2view
K2view Data Product Platform gets your data AI-ready: protected, complete, and accessible in a split-second. AI-ready datasets are packaged as governed data products, allowing you to reuse them at scale and across use cases, such as Agentic AI Automation, Customer Service Chatbots, Synthetic Data Generation, and Test Data Management. Our platform supports some of the largest organizations in the world, like Verizon, Regions Bank, Walmart, BBVA, Hapag-Lloyd, and Vodafone. For all these reasons, and more, Gartner rates us a Visionary – testifying to our ongoing commitment to innovation and value delivery. For more information, visit www.k2view.com.
