Every era of enterprise computing has had its defining layer. In the 1990s, it was the network. In the 2000s, it was the database. The 2010s belonged to the cloud. Today, we are watching the emergence of the intelligence layer: a horizontal capability that doesn't sit in one department or system, but cuts across the entire enterprise: from customer service to supply chain, from finance to product development.

AI agents are now being embedded into applications at a pace that would have seemed implausible even two years ago. Gartner predicts that 40% of enterprise applications will embed task-specific AI agents by the end of 2026, up from less than 5% in 2025. That is an 8x jump in 12 months. For context, the cloud transition took the better part of a decade to reach comparable penetration.

The speed is exhilarating. It is also dangerous without the right foundations in place.

The intelligence layer, unlike the cloud, does not come with native enterprise guardrails. Cloud platforms like AWS, Azure, and Google Cloud were purpose-built with identity management, access controls, audit logging, and billing transparency baked in. AI tools — the models, agents, copilots, and assistants flooding the enterprise — were largely designed for individual users, not organizational governance. The enterprise has been handed a powerful engine without a dashboard.

Beyond security and compliance, shadow AI, and uncoordinated AI adoption more broadly, creates a financial exposure that is quietly compounding on enterprise balance sheets.

Consider the inference economics. Running large language models at scale isn't a fixed cost. It is a consumption-based cost that scales with usage, often in non-linear ways. According to research from Vista Equity Partners, the average enterprise spent roughly $7 million on AI model usage in 2025, nearly triple the $2.5 million spent in 2024. Enterprise AI spend is projected to jump a further 65% in 2026, reaching approximately $11.6 million per organization on average.

Inference, or the act of querying AI models in production, now accounts for 55% or more of total AI infrastructure spending, up from just 33% in 2023. Deloitte's analysis places it even higher at 66% of all AI compute load. An agent in production doesn't make a single inference call and stops. It reasons, plans, calls tools, evaluates results, and iterates dozens of times per task. Multiply that across a large enterprise workforce with no usage governance in place, and the cost curve becomes difficult to control.

Some organizations have discovered this the hard way. One company reportedly spent $500 million in a single month after failing to set usage limits on its AI deployments. It’s a cautionary tale for any enterprise treating AI adoption as purely an empowerment exercise rather than also a governance imperative.

The irony is that 80–85% of organizations miss their AI cost forecasts entirely, because traditional IT budgeting models were never designed to account for consumption-based inference pricing. The AI control plane is, in part, a financial instrument: a mechanism for understanding where tokens are being spent, by whom, for what purpose, and at what return.

Enterprise IT governance has always been reactive. Security teams patch vulnerabilities after incidents. Compliance teams audit tools after deployment. Procurement processes approve software before use, but they were designed for licensing models, not consumption models.

None of these frameworks were built for the velocity of AI adoption or the distributed, autonomous nature of AI agents.

According to IBM's 2025 Cost of a Data Breach Report, only 37% of organizations have an AI governance policy in place. Nearly 43% of large firms lack AI risk frameworks entirely, despite widespread adoption. And Gartner notes that only 8% of organizations have full visibility into their AI tool footprint.

The problem is structural. Governance programs built around a single AI model don't scale to two. At three or four models, the overhead is unmanageable. At ten — which is the default state of enterprise AI in 2026 — it becomes impossible. As noted by Airia, enterprise AI environments are heterogeneous by design. Procurement relationships vary by region. Legal and compliance constraints shape which models can be deployed in which jurisdictions. Engineering teams develop strong preferences for specific APIs. Business units adopt models that integrate with their existing workflows.

A governance approach that requires each model, from each provider, to be individually administered is not a governance approach. It is a bottleneck with a policy document attached.

In December 2025, Forrester introduced the concept of the agent control plane as a distinct and necessary functional layer in enterprise agentic architecture — sitting alongside the build plane (where AI systems are developed) and the orchestration plane (where they are coordinated) — as a governance layer that operates independently of both.

The thesis is compelling: as enterprises deploy heterogeneous agents across vendors, cloud environments, and business domains, governance cannot be embedded in any single vendor's ecosystem. It must sit above all of them.

This is what an AI control plane does. It is the management layer through which every AI model, agent, workflow, and inference call is observed, governed, and optimized regardless of which model is running, which cloud it sits on, or which vendor built the agent.
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The key principles of a well-designed AI control plane are:

• Model and provider agnosticism. The control plane governs agents and models it did not build, across any framework, cloud, or deployment environment. Whether an enterprise is running GPT-4, Claude, Gemini, Llama, or a proprietary fine-tuned model, the control plane applies governance uniformly. This is not a theoretical ideal — it is a practical necessity. As Airia has articulated, a governance program tied to a single vendor's ecosystem becomes unscalable the moment a second vendor enters the picture.

• Policy enforcement at execution time, not after the fact. Traditional compliance runs on periodic audits. The AI control plane enforces policy in real time, at the moment an inference call is made or an agent action is initiated. This means a guardrail against uploading proprietary data to a non-compliant model isn't a policy review that happens quarterly. It's a technical control that fires in milliseconds.

• Full observability and cost attribution. Every token call, every agent action, every workflow execution is logged, attributed, and made visible. Not just to IT — to the business leaders accountable for the ROI of those investments. The control plane connects AI spend to business value streams, enabling the kind of FinOps discipline that cloud computing eventually taught enterprises, now applied to inference economics.

• Audit trails that satisfy regulatory requirements. For enterprises operating under GDPR, HIPAA, SOX, PCI DSS, or the EU AI Act, the control plane is not a nice-to-have. It is the mechanism by which AI deployment becomes legally defensible. Immutable audit logs, role-based access controls, and data residency enforcement are not features of the AI models themselves; they are capabilities that must be layered above them.

• Integration with existing investments. A control plane designed for enterprise deployment does not ask organizations to abandon their cloud, data, or workflow infrastructure. It orchestrates AI within it — sitting above existing systems, connecting to them through standard integration protocols, and extending the governance posture enterprises have already built.

It’s worth making the cost of inaction concrete.

The argument for an AI control plane is sometimes misread as an argument for slowing AI adoption. It’s the opposite.

The enterprises that will lead in the AI era are not those that move fastest in the next 18 months. They are those that build the foundations to move consistently for the next 10 years without triggering a governance crisis, a data breach, a compliance failure, or a cost implosion along the way.

The cloud analogy is instructive. In the early 2010s, cloud adoption outpaced governance in many organizations, spinning up shadow cloud instances, unmanaged S3 buckets, ungoverned AWS spend. The response wasn't to stop using cloud. It was to build cloud governance: FinOps disciplines, cloud security posture management, identity and access management, cost allocation frameworks. Cloud then became the managed, scalable backbone of modern enterprise operations.

AI governance is at the same inflection point. The intelligence layer is being deployed faster than governance frameworks can follow. The control plane is how enterprises close that gap — not by constraining AI, but by making it trustworthy enough to deploy at scale.

Gartner projects that spending on AI governance will reach $492 million in 2026 and surpass $1 billion by 2030. Governance is no longer a compliance conversation. It is becoming a platform selection criterion;  the deciding factor in which AI investments scale and which create liabilities.

The decisions enterprises make about AI architecture now will define their competitive position for the rest of the decade. Not because AI is moving fast, but because the technical debt of uncoordinated AI deployment compounds. Every agent deployed without governance is another integration that will need to be retrofitted. Every model adopted without a control plane is another vendor relationship that will resist consolidation. Every shadow AI incident is a breach that erodes board confidence in AI investment precisely when that confidence should be growing.

An AI control plane that’s model-agnostic, provider-independent, and built on the infrastructure you already have is not a constraint on what AI can do for your enterprise. It is the foundation that determines how much of it you can actually trust, scale, and sustain.

The intelligence layer is here. The question is who controls it.

Send us a message if you want to explore how a model-agnostic AI control plane that fits your enterprise architecture.