Readiness Assessment Worksheet

Baseline readiness across the six framework pillars, align stakeholders, and prioritize next actions.

Use with the Framework and the Implementation Guide.

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Assessment Metadata

Field	Value
Organization Name
Assessment Date
Facilitator / Owner
Current AI Stage	Just Starting / Exploring / Piloting / Scaling / Advanced

1. Value (Why)

Define outcomes AI must improve before selecting tools or use cases.

Prompts

• What is your one-sentence AI North Star?
• What are the top three business priorities AI must support?
• Which metrics will prove improvement in efficiency, quality, and capability?

Checklist

• Leadership aligns on value priorities.
• Success metrics are defined and baselined.
• North Star outcome is documented and shared.
• AI investments map to business strategy.
• Finance is involved in value definition.

2. Use Cases (Where)

Build and score a portfolio so the right opportunities move first.

Use Case Inventory

Use Case	Type	Business Owner	Impact	Effort	Risk	Priority
	Automation / Augmentation / Generation / Prediction / Optimization

Checklist

• Use case inventory exists.
• Scoring criteria are defined and applied.
• Top 3–10 use cases are selected.
• Owners are assigned to prioritized use cases.
• Intake process exists for new use cases.

3. Operating Model (How)

Clarify human-AI collaboration, handoffs, and decision ownership.

Human-AI Mode Selection

Process Area	Assistive	Co-Pilot	Autonomous	Notes
Customer-facing
Internal operations
Decision workflows
Data and analytics

Checklist

• Future-state workflows are mapped.
• Human-AI handoff points are defined.
• Decision ownership is documented.
• Exception and escalation paths exist.
• End users participated in design.

4. Foundation (What Enables It)

Confirm data, tooling, and platform readiness for priority use cases.

Readiness Rating

Area	Not Ready	In Progress	Ready	Notes
Data quality and access
Data governance and permissions
AI tools and models
Platform and infrastructure
API and system integration

Checklist

• Data sources for priority use cases are validated.
• Governance and access controls are active.
• Tools and models are selected or shortlisted.
• Integration requirements are mapped.
• Minimum viable foundation is defined.

5. Governance (Control and Trust)

Establish privacy, security, quality, and audit controls before scale.

Control Coverage

Area	Status	Owner	Evidence
Privacy policy and data handling
Access control and identity
Bias and accuracy testing
Legal and regulatory review
Audit logging and override

Checklist

• AI privacy policy is defined and communicated.
• Security review is required before deployment.
• Bias and performance thresholds are defined.
• AI risk register is maintained.
• Audit logging and override mechanisms are active.

6. Execution (How It Happens)

Select delivery approach, assign roles, and run an adoption loop.

Delivery Model

• Framework-driven
• Hierarchical (top-down)
• Pragmatic (bottom-up)
• Hybrid

Role Assignment

Role	Assigned Person	Team	Status
Explorer
Operator
Architect
Orchestrator

Checklist

• Delivery approach is selected and communicated.
• Roles are assigned with clear accountability.
• 6–12 month roadmap is documented.
• Change management plan is active.
• Adoption metrics are defined.
• Continuous improvement loop is in place.

Consolidated Summary

Section	Items	Completed	Notes
Value	5
Use Cases	5
Operating Model	5
Foundation	5
Governance	5
Execution	6
Total	31