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At BigDATAwire we have covered how the race to deploy agentic AI is already heavily contested. However, the real question is whether enterprise data infrastructure is ready for it. It appears it is struggling to keep pace.
Fivetran’s 2026 Agentic AI Readiness Index found that while 41% of organizations are already using agentic AI in production, only 15% believe they are fully prepared to support it with the necessary data foundation. What can enterprises do about this?
To get to that, let’s understand the key issues. That AI readiness gap becomes more important as AI systems move beyond generating recommendations and begin operating autonomously across enterprise workflows. Agentic AI systems increasingly rely on access to trusted and governed data in order to trigger actions and make operational decisions in real time.
The report argues that the next major enterprise AI challenge is whether organizations can build interoperable and reliable data environments capable of supporting autonomous AI at scale.
Enterprises are entering a more difficult phase of AI adoption – one where deployment speed by itself is not an issue, but it is beginning to outpace operational maturity. Organizations seem to steam ahead as they continue investing aggressively. Nearly 60% report multimillion dollar commitments toward agentic AI initiatives. Meanwhile, many others are still in the phase of evaluation and pilots before broader rollout.
What complicates that transition is the condition of the underlying data environment itself. Many enterprises continue operating with brittle integrations. They face siloed systems, inconsistent governance standards, and limited visibility into how operational data moves across the organization. Those weaknesses matter as more AI systems operate autonomously.
Simply getting AI into production is not enough anymore. It is equally if not more important to make sure the surrounding infrastructure can support autonomous systems safely and consistently once they arrive there.
According to the report, organizations further ahead on readiness are approaching data movement differently, and this could offer you a clue on what you can do. These organizations are prioritizing continuously refreshed pipelines instead of periodic updates and improving observability across systems. They are also consolidating trusted data into centralized warehouse and lakehouse environments.
The report emphasizes that scaling autonomous AI requires scaling reliable infrastructure first. That takes us to our next finding that the biggest obstacles to scaling agentic AI are no longer centered around model performance.
Fivetran’s report reveals that the most common blockers are data quality and lineage issues (42%), followed closely by regulatory compliance and sovereignty concerns (39%), which is tied with security and privacy risks (39%).
We’ve seen these challenges as part of a broader shift happening across enterprise AI. For years, most organizations focused on experimentation, proof of concepts, and access to increasingly capable models. Agentic AI changes the equation because these systems are expected to operate inside real business environments, often with the ability to trigger actions automatically.
In that environment, poor governance is not a technical inconvenience – it becomes an operational problem. An autonomous AI system operating on incomplete or poorly governed data does not gradually improve over time. It simply scales mistakes faster and across more systems.
(Bishop Iuliia/Shutterstock)
That concern is already shaping enterprise purchasing decisions. The report found that 65% of organizations would either heavily restrict or completely reject vendors unable to meet governance and sovereignty requirements, including 25% that would reject those vendors outright.
The report recommends that organizations should start treating governance as production infrastructure. Many still think of it as compliance paperwork. What they need to do is to build stricter access controls around what AI agents can see or modify and improve end to end lineage and auditability. They should also work on enforcing regional sovereignty controls. It would help to clearly define which systems agents are allowed to interact with before deployment.
Interoperability is highlighted by the report as a growing strategic priority for enterprises deploying agentic AI – especially for those deploying at scale. An overwhelming majority (86%) of organizations consider platform interoperability and extensibility important or critical, while many increasingly worry about becoming locked into rigid data integration ecosystems. In fact, respondents ranked data integration platforms as a larger vendor lock-in concern than cloud providers or enterprise applications.
That concern becomes understandable once agentic AI moves beyond isolated pilots. Autonomous systems increasingly require access across warehouses, operational environments, analytics platforms, and enterprise software – all at the same time. If those environments remain disconnected, the AI systems operating on top of them become harder to scale consistently.
The report argues enterprises should focus on flexibility now before infrastructure complexity becomes harder to unwind later.
One of the recommended approaches is to include adopting vendor neutral integration layers, centralizing governed data access, and building around open formats such as Apache Iceberg and Delta Lake can also help. These would enable organizations to move across tools and platforms more easily over time.
(Iurii-Motov/Shutterstock)
Enterprises are also being encouraged to design infrastructure in ways that allow models and AI services to evolve without repeatedly rebuilding core pipelines underneath them.
It is becoming increasingly evident that the next phase of the enterprise AI race may depend heavily on which organizations can build infrastructure that can actually support autonomous systems across what appears to be increasingly complex environments. The recommendations in the report could be a good starting point for organizations to overcome these challenges.
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