Enterprise AI
Structure private AI access around approved use cases and operating controls.
AI-as-a-Service
A private AI service model for controlled environments.
AI-as-a-Service can be structured around approved workloads, validated infrastructure, operating scope, and contractual requirements.
Business teamsApproved workloads
Private AI service modelScope · operations · commercial validation
WorkloadsAccessModelsOpsReview
Contractual scope appliesNot a live public catalogue
A service model, not a marketplace claim.
AI-as-a-Service should be presented as a deployment-dependent commercial model until service catalogue, pricing, support, and operating scope are approved.
- Private AI service model planning
- Approved workload definition
- Operating responsibility mapping
- Governance and access model direction
- Service review cadence planning
- Commercial validation inputs
- Support boundary definition
- Contractual scope documentation
Where AI Service Model fits
Use this capability only where the AI workload, data boundary, operating model, and validation scope are clear.
Service Provider Cloud
Can be scoped for provider-led AI operating models after commercial validation.
Managed Cloud Operations
Align AI services with review cadence, support boundaries, and governance.
Architecture
Contractual scope applies
Not a live public catalogue
Validation path
Each AI capability should move through assessment, design, and validation before publication or commitment.
Confirm service candidates
Identify approved workloads, users, models, and operating requirements.
Map responsibility
Clarify platform, AI, support, governance, and commercial boundaries.
Approve commercial scope
Confirm pricing, support, catalogue, and contractual commitments before launch.
Next step
Shape a private AI service model with clear commercial boundaries.
Start with your workloads, operating model, and control requirements.