Fivetran Launches 2026 Agentic AI Readiness Index, Revealing Gap Between Enterprise Investment and Data Preparedness for Agentic AI

Fivetran, the data foundation for AI, today released “The 2026 agentic AI readiness index,” a global benchmark measuring how prepared enterprise data environments are to support agentic AI workloads/initiatives in production. The findings show that only 15% of organizations are fully prepared to support agentic AI in production, even as nearly 60% report investing millions to tens of millions in the technology.

Only 15% of organizations are fully prepared for agentic AI in production, despite nearly 60% investing millions.

Based on a survey of 400 data professionals across the United States, United Kingdom, EMEA, and Asia-Pacific, the index evaluates organizations across the core data requirements needed for agentic AI to operate reliably, including data freshness, lineage, governance, and interoperability.

Agentic AI systems are designed to plan, act, and execute across business workflows, increasing both the value and the risk of AI adoption. As these systems move into production, gaps in data quality, governance, and interoperability shift from background issues to operational failures, limiting what AI can safely automate at scale.

“Most companies aren’t failing at AI because of the models, they’re failing because their data isn’t ready,” said George Fraser, CEO of Fivetran. “Organizations are pushing agentic AI into production on top of brittle pipelines, missing lineage, and systems that were never designed for autonomy. When that happens, you don’t get better outcomes, you get faster failures.”

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Key findings from the report include:

• Production is outpacing readiness: 41% of organizations are already using agentic AI in production, despite significant gaps in data reliability, governance, and interoperability.
• Data challenges are the primary blocker: The most cited barriers to achieving agentic AI goals are data quality and lineage (42%), regulatory compliance and sovereignty (39%), and security and privacy risk (39%).
• Interoperability is critical to success: 86% of data leaders say platform extensibility and interoperability are important or critical, including 17% who consider them critical to AI and data decisions, yet many organizations remain constrained by fragmented systems and vendor lock-in. Data integration platforms are cited as the single biggest source of lock-in risk.

The findings underscore a broader industry trend: As AI systems become more autonomous, data infrastructure becomes the limiting factor. According to Gartner, up to 60% of AI projects may be abandoned due to a lack of AI-ready data.

Data readiness defines AI outcomes

The report measures readiness using the Agentic AI Readiness Index, a composite score that evaluates how prepared an organization’s data foundation is across key dimensions, including data freshness, lineage, governance, and interoperability. The average readiness score across respondents is approximately 61–62%, indicating most organizations need to close critical gaps to gain an ROI on their AI investment.

Organizations that report being fully prepared show a clear advantage, not just in confidence but in how they operate. These teams are more likely to run always-on, automated data pipelines that keep information and context fresh and reliable, enforce end-to-end lineage and governance to maintain trust and compliance, and standardize on interoperable architectures that allow data to move freely across their infrastructure.

As a result, they are able to deploy agentic AI more broadly, across both internal workflows and customer-facing products, and are significantly more confident in their ability to achieve meaningful ROI from AI investments.

Building a foundation for agentic AI

The report outlines four core requirements for supporting agentic AI in production:

• Fresh, reliable data delivered through automated pipelines
• Transparent lineage to track how data is created and transformed
• Strong governance controls to enforce security and compliance
• Open interoperability across systems to avoid lock-in and enable flexibility

Together, these capabilities form the baseline for an AI-ready data foundation, enabling organizations to scale agentic AI while maintaining control over cost, risk, and performance.

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