AI Adoption
AI Adoption & Implementation
AI is not deployed enterprise-wide on day one. It is adopted in phases, with governance, identity and data controls in place before any capability reaches users.
Adoption model
Three phases, in order
Each phase builds on the guardrails established in the one before it. Skipping ahead — enabling agentic automation before identity and policy are in place — is the most common cause of failed AI programs.
Phase 1
Govern & Prepare
Establish the guardrails, ownership and data foundations before any AI capability is enabled for users.
Strategy & readiness
- AI strategy & business use cases
- Data readiness
- Responsible AI principles
Control foundation
- Identity
- Security
- Governance
- Approved models & tools
- AI policy
- Guardrails
Phase 2
Enable & Integrate
Connect approved AI capability to enterprise data and systems, with identity-aware, auditable tool access.
Capability
- Microsoft Copilot
- Azure OpenAI
- Enterprise APIs
- RAG over enterprise data
Controlled access
- MCP
- AI tools
- Identity-aware agents
- Logging
- Human approval
- Secure tool invocation
Phase 3
Automate & Scale
Extend into agentic workflows and a broader MCP ecosystem, under continuous governance and cost visibility.
Scale
- AI agents
- Agentic workflows
- MCP ecosystem
- Automation
Sustain
- Observability
- Cost management
- Lifecycle management
- Continuous governance
Guardrails
The AI guardrails model
Eleven controls apply to every AI capability Contoso enables, regardless of phase or tool.
Identity
Every user, agent and tool call is authenticated and scoped to least privilege via Entra ID.
Security
AI endpoints, agents and data connectors sit inside the same security perimeter as any enterprise system.
Data
Data classification and sensitivity labels govern what content an assistant or agent can access.
Privacy
Personal and regulated data is handled under existing privacy obligations — no exceptions for AI.
Model
Only approved, evaluated models are permitted; model choice and version are tracked centrally.
Prompts
System prompts are version-controlled, reviewed and tested like any other production configuration.
Tools
Tool and function access is explicitly allow-listed per agent, not granted by default.
MCP
Model Context Protocol servers are inventoried, permissioned and treated as first-class integration surfaces.
Output
Generated content is labelled, and high-impact actions require validation before execution.
Audit
Every prompt, tool call and agent action is logged for traceability and incident review.
Cost
Token and compute consumption is metered per workload, with budgets and alerting in place.
Example architecture
Contoso AI request path
A representative Phase 2 pattern: an employee request is authenticated, grounded on enterprise data, and routed through an allow-listed MCP tool gateway — with approval and audit built in.
Step 1
Employee
Teams · Copilot · web app
Step 2
Identity & policy gate
Entra ID · Conditional Access
Step 3
Azure OpenAI + RAG
Grounded on enterprise data
Step 4
MCP tool gateway
Allow-listed tools only
Step 5
Enterprise APIs & data
Contoso systems of record
Human approval for sensitive actions
Any tool call that writes data, sends communications, or touches regulated records is routed for explicit approval before execution.
Full audit trail
Every prompt, retrieval and tool invocation is logged with identity context for traceability and cost attribution.
Cross-domain dependency
AI cannot be governed without data governance
Before AI touches enterprise data, the organisation needs to know what data exists, who owns it, its classification, and whether AI systems are authorised to consume it.