Enterprise Management Associates released a new research report today, July 6, 2026, with findings that should give any business leader pause before handing more decisions to autonomous AI systems.
The report, titled “From Outcomes to Authority: Defining the Enterprise Control Plane” and authored by EMA President and COO Dan Twing, surveyed 336 enterprise IT professionals globally. The headline numbers: nearly 30% of organizations encounter incorrect or problematic AI outcomes frequently or very frequently. More than three-quarters have been required to intervene and reverse AI-driven decisions at some point.
Taken together, those numbers paint a picture of an enterprise AI landscape running considerably faster than its safety infrastructure can keep up with.
What Is Going Wrong
The problem EMA is documenting is not that AI models are failing at their tasks in obvious ways. It is that AI-driven decisions are embedded in complex operational chains — automation pipelines, orchestration systems, cloud platforms, observability tools — and when something goes wrong, organizations often have no clean mechanism for catching it before damage occurs, let alone reversing it cleanly after.
Enterprise operations have become distributed across multiple automation platforms and AI systems that were not originally designed to coordinate with each other. When a decision made by one component cascades through adjacent systems, the intervention point is not always obvious. Sometimes there is no intervention point at all until a human notices the downstream effect.
The report introduces the concept of an Enterprise Control Plane: an operational coordination layer that governs execution authority across increasingly autonomous environments. Rather than replacing existing AI systems, it sits above them to provide governance, visibility, accountability, and policy enforcement across what EMA describes as a federated control function.
Put plainly: a management layer that ensures the humans responsible for business outcomes can see what AI systems are doing, understand why, and intervene when needed.
Why This Matters Right Now
The timing of this research matters. Enterprise AI adoption has accelerated sharply in 2025 and 2026, often faster than governance frameworks have been able to respond. Most organizations that have deployed AI agents, automated workflows, or AI-assisted decision systems did so using the governance tools they had available at the time, which were generally not designed for autonomous AI environments.
The result is a growing body of deployed AI operating in what EMA would call an authority gap: AI systems making consequential decisions without adequate visibility, oversight, or rollback capability. The 75% figure is not a fringe outcome. It is the majority of organizations saying they have already needed to reverse an AI decision, and it is likely undercounted given that many incorrect AI outcomes go undetected until well after the fact.
This is consistent with what other researchers have been finding. A recent Gartner study on AI agent governance found that most enterprises lack the infrastructure to govern agents at the speed agents are being deployed. The gap is between deployment velocity and governance maturity. Deployment is winning.
What Organizations Need to Do About It
EMA’s recommendation around an Enterprise Control Plane is a useful frame, but it is worth being concrete about what this means practically.
At minimum, organizations need visibility into what AI systems are doing and what decisions they are making. This is harder than it sounds when AI is embedded in third-party SaaS platforms, cloud services, and automation tools that do not surface AI decision logic to the operators running them.
Organizations also need accountability: clear ownership for each AI-assisted decision domain, with defined escalation paths when outcomes are unexpected. Autonomous systems do not remove accountability from humans. They make accountability harder to maintain, which means the organizational structures for it need to be more deliberate, not less.
Finally, organizations need rollback capability. Any AI system with authority to change data, trigger financial transactions, modify customer records, or affect operational workflows needs a defined rollback path. “We had to call the vendor” is not a rollback path.
What This Means for Business
If you are running AI pilots or early production deployments, the EMA findings should shape how you think about the next stage. The question is not whether to deploy AI more broadly. The question is whether the governance architecture you have today can handle a broader deployment safely.
The organizations that will get durable ROI from AI are the ones that treat governance as infrastructure, not as compliance overhead. That means investing in visibility and control tooling at the same time as investing in AI capability.
For businesses that have not yet built a governance layer for their AI operations, the good news is that it is not technically complex to start. It starts with an inventory of where AI is making or influencing decisions in your organization, what the accountability structure is for each, and whether you have the ability to audit outcomes. Most organizations that do this exercise honestly find gaps they did not expect.
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