For decades, enterprise data strategies have been built around a simple assumption: people are the primary consumers of data. Analysts request access. Executives review dashboards. Data scientists train models. Governance, access controls, and approval workflows all evolved to support that human-centered reality.
Even as data platforms modernized, that operating model stayed largely the same. Requests were intermittent. Consumption was limited. Humans, by their nature, acted as a governor on scale. Data access was slow, but predictable. Governance teams could manage demand, even if it wasn’t always efficient.
Not because it replaces people, but because it behaves nothing like them. AI systems, especially agents, are rapidly becoming the fastest-growing class of data consumer inside the enterprise. They request more data, more often, with fewer natural constraints, and at speeds governance models were never designed to support. That shift is already underway, whether organizations are ready for it or not.
The most obvious difference between humans and AI as data consumers is speed.
Humans tolerate delay. Minutes, or even longer, between requesting and receiving access are frustrating but manageable. Context is preserved. Work continues.
Agents act at machine speed. They request data, evaluate results, and move on immediately. When they need access, they expect the decision to happen just as fast. Any delay interrupts execution. When requests are automated and continuous, even small pauses compound into real blockers.
This isn’t a performance tuning issue. It’s a structural mismatch. Governance models built for human-paced decision-making cannot simply be sped up to support machine-speed systems. They were never designed for it.
But speed is only part of the challenge.
Human governance also quietly relies on judgment. People bring context with them. They hesitate. They generally understand when certain combinations of data feel inappropriate or risky. They have an implicit sense of purpose and consequence. Governance processes assume that human caution fills in the gaps.
AI agents don’t have that instinct.
They aren’t sentient. They don’t understand why some datasets shouldn’t be combined. They don’t recognize contractual or regulatory constraints unless those rules are explicitly enforced. If combining more data might improve an answer, they will attempt it. If a request fails, they retry.
That behavior isn’t malicious. It’s mechanical. But it fundamentally changes the risk profile.
AI agents don’t behave like the humans who create them, and that distinction matters for access and governance.
In practice, organizations can’t rely on implicit assumptions about acceptable use or context. Agents either need to inherit the credentials and entitlements of the human user that initiates them, or be governed as first-class, non-human identities with explicit entitlements and purpose-based controls.
Without one of these models in place, agents will do exactly what they’re designed to do: optimize for outcomes. Governance has to ensure that optimization happens within clearly defined boundaries.
This is why AI becomes such a disruptive data consumer so quickly. It exposes the limits on two invisible governors enterprises have relied on for years: human speed and human judgment.
Once AI moves beyond experimentation and into real operational use, these differences collide with existing access models almost immediately.
Ticket-based workflows that were already strained by human demand begin to collapse under volume. Requests pile up. Approval queues grow. Data governance and IT teams become bottlenecks. Backlogs balloon.
At the same time, pressure from the business intensifies. Teams building AI systems don’t slow down just because access is hard. They’re incentivized to make progress, not to wait.
What typically follows is predictable:
The very processes designed to reduce risk end up undermining it because they can’t keep up with demand.
Infrastructure costs start to rise as well. Agents retry failed requests. Duplicate ETL jobs proliferate. Systems pull more data than necessary because access policies aren’t being enforced consistently. What starts as an access problem quickly turns into a cost, complexity, and exposure problem.
In practice, many organizations may end up defaulting to a familiar move: treat AI agents like another class of user. Assign roles. Grant permissions. Plug them into existing identity and access frameworks.
That approach feels safe because it’s familiar. But it fails quickly.
Humans are relatively static consumers. Their access patterns change slowly and predictably. AI agents are dynamic. They evolve as models retrain, as objectives change, and as data environments shift. Static entitlements can’t keep up with that reality.
Organizations are forced into a false choice:
Neither option scales. Both undermine the value AI is supposed to deliver.
The mistake many organizations make is treating AI access as an extension of existing processes.
In reality, AI changes the nature of the problem.
If you increase the speed at which data is delivered, you must increase the speed at which access can be revoked. Provisioning and revocation have to move in parallel. Otherwise, governance breaks down.
Agents don’t stop operating. Access relationships change continuously. Without continuous monitoring, recertification, and revocation, organizations lose visibility into who (or what) has access to which data and why. Risk accumulates silently when revocation lags provisioning.
This is where the mindset needs to change.
AI doesn’t remove the need for governance. It raises the bar. Governance can no longer function as a manual gatekeeping layer that reacts to requests one at a time. It has to operate as an automated system that can keep up with machine behavior.
At a minimum, that means governance models that can:
When access is handled this way, governance stops being a brake and starts becoming an enabler.
Requests that begin as exceptions reveal patterns. Patterns become candidates for policy. Automation increases. Audit improves. Governance teams stop drowning in tickets and start operating the system instead.
This mirrors the same shift enterprises are beginning to make elsewhere: moving from manual intervention to systems that can run continuously, intelligently, and at scale.
AI becoming the fastest-growing class of data consumer isn’t a future scenario. It’s already happening.
Organizations that try to force this new reality into access models designed for humans will continue to struggle. AI initiatives will stall. Risk will grow in unexpected places. Teams will spend more time working around governance than working with it.
Organizations that recognize what’s changed, and adapt their provisioning and governance models accordingly, will find that AI can finally operate at the speed it promises, without giving up control in the process.
The opportunity isn’t just to support AI safely. It’s to treat data access as real infrastructure, something that can be designed, automated, measured, and improved over time for machines and people alike.
