Closing the gap

The missing layer is governed context, not more intelligence. Every finding in this report maps to one of two challenges, and enterprises are solving one of them faster than the other.

The first is about context. Before an agent acts, it has to know what the data means, whether that data is trustworthy in production, and what policy governs how it can be used. That is the same meaning, trust and policy that governed data products are built to carry. The instinct already exists among the 87% of decision-makers who are regularly re-verifying context and the 72% who recognize that when AI initiatives fall short, the root cause almost always tracks back to an unaligned or poor data foundation. What is missing, for the 39% who have trouble verifying the quality and/or security of the context their autonomous AI agents pull from, and the 34% of decision-makers whose organizations use autonomous AI agents who have been unable to verify or govern the unstructured data and context their agents retrieve over the past 12 months, is a layer that answers those three questions before the agent asks them, rather than a process that checks the answer afterward.
The second is about control, which belongs to a different layer. What is an agent authorized to do?
Can every decision it makes be traced? And who is accountable when it acts outside those bounds?

84% of decision-makers say they have clearly defined accountability when autonomous AI agents produce a flawed or harmful output., but 42% who use autonomous AI agents say manual review becomes operationally or financially impossible for their team past fifteen agents, and 29% who use autonomous AI agents have already found an agent accessing or surfacing data it should not have had permission to use. Accountability that depends on a human reviewing every output tends not to hold up at agent scale. Accountability built into the platform is more likely to.

Collibra is built to address both layers together, context and control, across any data, any model and any agent, which is why we call it the enterprise AI control plane. Collibra Data Catalog, Collibra Data Lineage, Collibra Data Quality and Collibra Data Governance work together to assemble governed data products, packages that carry their meaning, trust and policy with them so an agent does not have to check four systems before it acts. Collibra Maestro enables governance teams to build their own no-code agents with governance built in from the start to automate workloads, turning weeks of manual work into minutes. Live Map gives every agent a context graph of how a company’s knowledge connects, enabling performant agentic retrieval from curated unstructured documents. Deasy Labs (a Collibra company) extends that same governance into the unstructured data, contracts, tickets and transcripts, which this survey shows is the hardest layer for enterprises to reach on their own. AI Command Center owns the authorization, traceability and accountability, giving the AI leader the ability to see every agent, trace every decision and catch drift before it becomes the kind of incident 29% of decision-makers who use autonomous AI agents have already experienced. Guardian Agents with Agent Contracts will extend AI governance to the much larger fleet of AI agents built outside Collibra, replacing the manual review and correcting of agent outputs that 51% of decision-makers still must do before they go live. All of it runs on the Collibra Platform, the semantic and orchestration layer that connects data producers and consumers across the enterprise.

The outcome enterprises are ultimately buying is not governance for its own sake. It is measured the way a CAIO already measures agent performance: More tasks completed without a human stepping in, and a lower cost to reach each correct answer. The 51% of decision-makers spending significant staff time reviewing and correcting agent outputs before going live, and the 42% who use autonomous AI agents saying manual review becomes operationally or financially impossible past fifteen agents, are paying for the absence of that outcome today.

The decision-makers furthest along in this survey aren’t just better at one side of this problem. The same organizations leading on context measures, like verifying data quality and defining accountability, are also leading on control measures, like documentation and governance frameworks. That pairing is the finding: Treating context and control as one connected problem, not two, is what correlates with being ahead.

The names change. But the need does not. Whether the mandate sits with a CIO, a CDO or a CAIO, the enterprise still has to govern the meaning of its data and control the actions its agents take, before the next agent, not after.