Framework

The framework connects strategy, execution, and governance so AI initiatives can scale with control. Read this section for understanding; step-by-step delivery comes later in Implementation.

The Six Pillars

Pillar	Core Question	Primary Output
Value	Why are we doing this?	Outcome targets and success metrics
Use Cases	Where do we apply AI?	Prioritized portfolio
Operating Model	How do humans and AI work together?	Defined interaction mode and accountability
Foundation	What enables delivery?	Data, tooling, and integration baseline
Governance	How do we control risk and trust?	Guardrails, controls, and monitoring
Execution	How do we deliver at scale?	Delivery cadence, roles, and adoption plan

1. Value (Why)

Define the outcomes AI must improve:

• Efficiency: time, cost, throughput
• Quality: error reduction, consistency, reliability
• Capability: new products, services, or decisions

Reference alignment: ITIL SVS, PMBOK benefits realization.

2. Use Cases (Where)

Standardize AI opportunities into reusable categories:

• Automation
• Augmentation
• Generation
• Prediction
• Optimization

Each use case should include owner, baseline, expected impact, and risk rating.

Reference alignment: PMBOK portfolio prioritization.

3. Operating Model (How)

Choose the right human-AI interaction mode:

Mode	Decision Authority	Typical Use
Assistive	Human decides	Recommendations, triage support
Co-Pilot	Shared	Drafting, analysis, review workflows
Autonomous	AI executes with oversight	High-volume, low-ambiguity tasks

Reference alignment: ITIL 4 dimensions.

4. Foundation (What Enables It)

Build a delivery-ready baseline:

• Data: quality, lineage, classification, ownership
• Tools and models: approved platforms, versioning, evaluation
• Integration: APIs, identity, observability, deployment pipeline

Reference alignment: ITIL service design and transition.

5. Governance (Control and Trust)

Establish risk and compliance controls before scale:

• Privacy and security controls
• Bias and accuracy monitoring
• Legal and regulatory compliance
• Auditability and human override

Reference alignment: NIST AI RMF, COBIT, PMBOK risk management.

6. Execution (How It Happens)

Operationalize AI with repeatable delivery:

• Select approach: framework-driven, hierarchical, or pragmatic
• Assign roles: Explorer, Operator, Architect, Orchestrator
• Prioritize by impact, effort, and risk
• Drive adoption through training and change management

Reference alignment: PMBOK tailoring, ITIL continual improvement.

Cross-Cutting Layers

Maturity Layer (How Far)

• Adoption: experiments and early wins
• Integration: embedded, repeatable delivery
• Transformation: AI-native operating model

Measurement Layer (What Improves)

Every initiative must define:

1. Baseline
2. Target
3. Measurement interval
4. Owner for realization

Rule of Use

Do not start with tooling. Start with value and use cases, then design operating model, foundation, governance, and execution in sequence.

Next Step

Continue to the Maturity Model in the sidebar, then Transformation Approaches. After you finish the Playbook reading pass, open the Implementation Guide.