Register
Know what you are managing. Define the use cases, models, applications, agents, services, accountable owners, risk classifications and approval status that need to be visible.
Manage
Enterprise AI Management & Control Plane
Manage AI as an enterprise capability, not a collection of disconnected tools and projects.
- 01 Register inventory and owner
- 02 Observe usage, cost and risk
- 03 Govern policy and decision rights
- 04 Control approvals and limits
Business problem
Manage AI as an enterprise capability.
As AI spreads across business units, platforms and providers, leadership starts facing a different problem. It is no longer enough to approve individual use cases or publish an AI policy. The organization needs to know what AI it is running, who owns it, what it costs, what risk it creates, what policies apply and what happens when AI begins to act--not just answer questions.
SeaVic helps leadership put that management layer in place.
The management problem
AI complexity grows one decision at a time.
AI complexity grows one decision at a time. A business unit adds a copilot. Another team builds a RAG application. Developers introduce another model provider. Finance sees growing consumption. Security finds tools it did not know about. Then agents begin accessing systems and triggering workflows. Each decision may make sense on its own. The problem is that the enterprise ends up managing them separately.
Core problem areas
- AI is difficult to inventory.
- Governance is rebuilt application by application.
- Cost and evidence are fragmented.
- AI is beginning to act across systems and workflows.
Four management disciplines
Register, Observe, Govern and Control.
These disciplines sit inside the AI Management and Control offering. SeaVic's firm-wide client journey remains Diagnose, Govern, Implement, Transform and Manage.
Observe
Understand usage, cost and behavior. Bring usage, cost, ownership, performance, exceptions, risk events and business outcomes into a coherent management view.
Govern
Apply common policy and decision rules. Define policy requirements, decision rights, access and approval boundaries, risk tiers, exceptions, escalation, thresholds, evidence and human oversight requirements.
Control
Decide what AI is allowed to do. Define delegated authority, human approval boundaries, system and tool access, exception conditions, execution limits, traceability, intervention and shutdown responsibilities.
Enterprise principle
Governance should follow the enterprise AI estate--not the boundaries of a single vendor platform. The operating model should accommodate public and private models, multiple providers, hyperscaler platforms, SaaS AI, internally developed applications, AI embedded in enterprise systems and emerging agents. For regulated or sovereignty-sensitive organizations, deployment boundaries, data residency and control of the operating environment may also matter.
Where it is relevant
Particularly relevant where AI operates at scale or under scrutiny: large and multi-business enterprises, regulated, risk-sensitive or sovereignty-sensitive organizations, and environments where AI spans multiple platforms, business units, jurisdictions or accountability regimes. For selected SIs, MSPs and MSSPs, SeaVic can make its methodology, assessment model, governance design, management cadence, reporting approach and advisory or delivery support repeatable across client environments. Any technology component is scoped separately and depends on the applicable product, deployment model and commercial rights.
What SeaVic does
Establish the inventory; clarify ownership; create management visibility; define common governance; prepare for agentic AI; design the management cadence; and determine the enabling architecture. Existing enterprise platforms come first. Additional technology is considered only where it closes a defined management or control gap.
Engagement
Start with the management questions leadership cannot answer reliably today.
We bring together CIO, CTO or CAIO leadership with CISO/Risk, Finance/FinOps, AI governance, enterprise architecture, platform teams and accountable business owners. The work begins with the management questions leadership needs to answer, then scopes the evidence and design work needed to answer them reliably.
Outputs may include
- Current-state management model.
- Priority gaps.
- Target control-plane design.
- Ownership and decision framework.
- Reporting requirements and implementation roadmap.
Common questions
Is an AI Management & Control Plane another software platform?
Not necessarily. SeaVic uses control plane first as an operating-model concept: a common way to know what AI exists, who owns it, how it is being used, what it costs, which policies apply and what needs attention. Some organizations can support that with systems they already have; others may need additional technology. We start with the management requirement, not the product.
How does this work if we use several AI models, cloud platforms and vendors?
That is exactly where a common management layer becomes useful. Governance should follow the AI estate rather than a single vendor boundary, with common ownership, policy, evidence, cost and decision disciplines across models, platforms, SaaS applications, internal solutions and emerging agents.
What changes when AI agents start taking actions?
The control question becomes more important. When AI can call tools, access systems, move data or initiate activity, leadership needs explicit boundaries around delegated authority, human approval, exceptions, execution limits, traceability and intervention. SeaVic helps design those management and accountability boundaries. Any technical enforcement capability is separately validated and is never assumed.
Next step
Start with the AI decision or operating issue in front of you.
A SeaVic Discovery Call is a focused conversation about what is happening, what leadership needs to decide and whether there is a useful next step for SeaVic to support.