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EMA Research Report

What Is the Enterprise Control Plane, and Why Does It Matter?

Executive summary of From Outcomes to Authority: Defining the Enterprise Control Plane, July 2026, EMA Research.

No single platform coordinates enterprise IT, and decision-making authority is already spread across many systems, not because of AI, but because of how enterprise automation grew over time. AI raises the stakes on that fragmentation. EMA Research calls the remedy the enterprise control plane: the infrastructure that keeps governance intact as work crosses systems.

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What is the enterprise control plane?

EMA Research names it the enterprise control plane: the infrastructure a company uses to keep governance, authority, accountability, visibility, and confidence in outcomes intact across a mix of people and automated systems spread across many platforms. It is not another orchestration platform, and it does not centralize decisions; it keeps the decisions specialized systems already make visible, governed, and accountable.

Why is consolidating onto a single platform not the answer?

Enterprise orchestration is the most-claimed category among enterprise IT leaders: 34 percent select it when choosing from a list. Yet when asked to name the actual platform coordinating work in their environment, no matching product category emerges. Organizations instead name cloud platforms, workload automation tools, DevOps pipelines, ERP systems, even CRM software, each covering one piece of the whole. Enterprise orchestration functions less as a product category than as a role organizations believe must exist, though no platform yet fills it.

Why does AI make fragmented orchestration more urgent?

AI makes fragmented orchestration more costly to ignore. For decades, a completed workflow was a reliable proxy for a correct outcome, so monitoring focused on execution status and SLA attainment rather than the quality of the decision itself. That proxy is weakening; close to a third of organizations encounter incorrect or misleading AI outcomes frequently, and more than three-quarters of organizations running AI automation have had to correct or roll back an AI-driven action at some point.

Nearly half report that AI recommendations routinely require additional validation before they are allowed to run, most often because the recommendation conflicts with policy, misdiagnoses the root cause, or arrives without enough context to be right. Organizations running AI across the most interconnected systems report the highest error rates. That correlation does not prove the mechanism on its own, but it matches what EMA's research found happening at each handoff: information gets abstracted, summarized, or dropped, so a system can make a defensible call given what it could see and still be wrong because of what it could not.

Why is better data or context not enough to fix unreliable AI outcomes?

The industry has spent years pursuing better data, then better context. Both stop one step short. Data describes what already happened. Context explains what matters. But operations run on state: the current condition of a workflow, a dependency, a system, at the exact moment a decision gets made.

State is what tells a system, human or AI, what is safe to do next. And state is precisely what breaks every time work crosses from one platform to another, which in a federated IT environment happens constantly.

What do organizations require before giving AI more autonomy?

Enterprises are not waiting passively. Before granting AI systems more independence, they weigh three conditions consistently ahead of business value or executive sponsorship:

  • Explainability (49%)

    Visibility into why an AI system chose a given action, not just that it acted.

  • Proven accuracy over time (48%)

    A track record showing reliability is demonstrated, not assumed.

  • Observability across handoffs (43%)

    Visibility that extends to the boundaries between systems, not just inside each one.

Only 2 percent set no conditions at all. None of the three is a model capability; they are orchestration capabilities, which is precisely the gap the enterprise control plane is meant to close.

How does Control-M support the enterprise control plane?

Control-M does not replace the specialized systems already coordinating pieces of the work: applications, data pipelines, batch jobs, AI-driven steps. It is the layer that keeps their decisions visible, governed, and accountable as work crosses between them. Dependency-aware scheduling and predictive SLA risk detection apply to AI-driven steps the same way they apply to any other job, and role-based access, policy controls, and end-to-end auditability keep every action inside defined approval and audit boundaries, backed by ISO 27001, SOC 2, TX-RAMP, ENS, and IRAP certifications.

The result is one execution record across every system in the workflow, instead of a governance model that stops at the edge of each one. That is the role EMA's research identifies as still missing in most organizations.

This summary draws on EMA's May 2026 survey of 336 IT executives and financial and technical decision-makers across North America, Europe, and Asia, each screened for direct involvement in IT operations, automation, orchestration, or observability decisions. EMA's full report adds the regional breakdowns, the complete governance and authority findings, and the survey detail behind every figure above.

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Read the full report, From Outcomes to Authority: Defining the Enterprise Control Plane

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