// COCOSHEDE_FRAMEWORK
CAMI gives leadership a precise language for AI adoption. It classifies the current operating model, identifies the next safe maturity target, and turns vague AI ambition into a board-level roadmap.
L1
Shadow AI
AI usage exists informally through consumer tools, isolated experiments, or ungoverned workflows. Data leakage, duplicated effort, and unclear accountability are the primary risks.
The enterprise needs a controlled intake, policy, and first governed workflow before scaling.
L2
Isolated Knowledge
The business has basic internal knowledge retrieval or RAG capabilities, but AI remains mostly informational and disconnected from operational systems.
The next step is connecting knowledge layers to measurable workflows with review controls.
L3
Orchestrated Workflows
Secure agents, model gateways, and workflow orchestration connect across system boundaries to assist or execute defined business processes.
The enterprise can justify budget for controlled automation, integration, and operating-model change.
L4
Private Scale
The enterprise operates localized or private model infrastructure with mature evaluation, policy enforcement, monitoring, and reduced dependency on external API paths.
The enterprise is ready to treat AI as private strategic infrastructure.
// EVALUATION_MODEL
The index is deliberately operational. It does not reward AI theater. It rewards the structures that determine whether AI can be used safely, funded responsibly, and scaled beyond one pilot.
Who is allowed to use AI, for which workflow, under which approval and audit conditions.
Whether operational context is scattered, searchable, governed, or ready for safe retrieval.
Whether AI is informational only or connected to controlled business execution paths.
Whether sensitive work depends on public APIs, private cloud routing, or localized inference.
// BOARDROOM_USE
CAMI is designed to make AI adoption discussable in executive rooms where finance, legal, security, technology, and operations each need a different kind of proof.
Use the current and target CAMI level to explain why the first investment is a controlled operating-model upgrade, not a speculative model experiment.
Show which maturity gaps create data leakage, hallucination, auditability, or accountability exposure before implementation starts.
Translate maturity movement into concrete architecture work: retrieval boundaries, model routing, workflow gates, observability, and deployment posture.
Sequence adoption from human-in-the-loop augmentation to governed orchestration so employees see AI as controlled operating leverage rather than sudden replacement.
| Maturity movement | What changes operationally |
|---|---|
| Level 1 to level 2 | Stop shadow AI and create a governed knowledge layer with approved context, access boundaries, and usage policy. |
| Level 2 to level 3 | Move from isolated knowledge search to workflow-specific orchestration with human approvals, logs, and measurable process impact. |
| Level 3 to level 4 | Shift sensitive, high-volume, or strategic workflows toward private model infrastructure and sovereign operating controls. |
// NEXT_STEP
Cocoshede assigns a current and target CAMI level inside every blueprint, then links that maturity movement to architecture recipes, ROI, controls, and a 12-week execution plan.