Speed vs. Safety: The AI Dilemma Enterprise Leaders Are Facing

One enterprise company we've been working with spent months preparing for AI agents. Leadership understood the risk. They knew autonomous systems shouldn’t touch enterprise data without the right controls in place. So they did what felt responsible: they paused. They launched a six-month effort to design an agent gateway, define policies, and build guardrails before allowing a single agent into production.

Six months later, they still didn’t have an agent in production.

At the other end of the spectrum, a different global company took the opposite approach. They moved fast. Thousands of employees were given access to a self-service AI platform. Hundreds of agents were built and deployed. From the outside, it looked like exactly what the C-suite wanted: momentum, experimentation, visible progress.

But internally, a different picture was emerging. Outside of their core structured systems, they couldn’t confidently say what their agents could access. Years of unstructured documents, shared drives, and forgotten files were suddenly available to non-deterministic systems operating at machine speed. Innovation was happening, but so was uncertainty.

These two companies made very different choices. Both choices felt rational. And both reveal the same underlying tension that enterprise AI leaders are grappling with right now: move fast, or stay safe.

Most organizations believe they have to choose.

Why this dilemma exists

This tension shows up the moment AI moves into production, especially when organizations begin deploying agents rather than copilots. An agent doesn’t just assist an employee; it acts on their behalf. It queries data, synthesizes information, and makes decisions without stopping to ask whether a table is stale or a document was ever meant to be exposed.

AI agents don’t create new data problems. They surface the ones that have been quietly accumulating for years. Inconsistent definitions. Anomalous values. Data that was “good enough” when it powered dashboards, but brittle when it starts driving decisions automatically.

And this isn’t a fringe concern. In a recent survey of AI leaders, 44% ranked poor data quality as the number one obstacle to AI success. Last year, data quality barely registered as a concern. After teams tried to put AI into production, it jumped to the top of the list.

At the same time, pressure from leadership is intensifying. Implement AI. Reduce costs. Show impact. Preferably this quarter. Teams know data is the backbone of AI, but they’re being asked to scale systems that were never funded, staffed, or governed with autonomous decision-making in mind.

The false choice between speed and safety

When leaders feel forced to choose, they tend to polarize.

On one side is caution taken to its logical extreme. Lock everything down. Review every use case. Delay production until controls are perfect. The risk here isn’t just time. It’s learning. Organizations that never put agents into production never discover where the real issues are, and they watch competitors build muscle they don’t yet have.

On the other side is velocity without visibility. Ship quickly. Let teams experiment. Sort it out later. This approach generates momentum, but it also creates blind spots. When something goes wrong, leaders often can’t answer basic questions about what data was accessed, why a decision was made, or whether the behavior violated internal or regulatory expectations.

Both paths carry risk. The real mistake, however, is believing they are the only options.

A third path is emerging

A different pattern is starting to show up among organizations that manage to scale AI without stalling or losing sleep. One large organization we’ve worked with began cautiously, with tight controls and centralized review. Early success created demand, and demand quickly outpaced what manual governance could support.

Instead of choosing between opening the floodgates or slamming them shut, they changed the model. Governance stopped being something that happened before deployment and started becoming something that operated continuously, at runtime.

The question shifted from “Should we allow this agent?” to “Under what conditions should this agent be allowed to act?”

Governing AI at runtime

When governance operates at runtime, speed and safety stop being opposites. Visibility comes first: understanding what agents are accessing and how they’re behaving. Context comes next: surfacing data quality and policy signals at the moment an agent makes a decision. Enforcement follows for the highest-risk scenarios, where warnings aren’t enough and access needs to be denied automatically.

This isn’t about slowing AI down. It’s about letting it move fast inside boundaries that reflect the organization’s standards for quality, sensitivity, and compliance. Humans stay in control, but they’re no longer required to manually approve every action in advance.

Importantly, this approach acknowledges reality. Enterprise data is imperfect. It always has been. The goal isn’t to pretend otherwise, or to freeze progress until everything is clean. The goal is to make imperfection visible, contextual, and manageable before it turns into a customer issue or a regulatory problem.

What leaders should take away

The organizations that succeed with AI over the next few years won’t be the ones that chose speed over safety, or safety over speed. They’ll be the ones that rejected that framing entirely.

They’ll treat trust as infrastructure, not process. They’ll invest in understanding how data behaves under autonomous systems, not just whether a model performs well in isolation. And they’ll recognize that the fastest way to scale AI isn’t by removing controls, but by making those controls smart enough to operate at machine speed.

The AI debate isn’t about whether a bubble will burst. It’s about whether, two years from now, your organization will be explaining how it stayed ahead — or explaining why it hesitated while others figured out how to move fast without breaking things.

This challenge, and the consequences of getting it wrong, came up repeatedly in a recent Bigeye keynote on enterprise AI trust, where we spoke with data and AI leaders about what actually breaks when agents move into production. Watch the full conversation here.