AI Readiness

Capability Template Details

AI Readiness

Built for software delivery organizations, business teams, and enterprises that want to accelerate AI adoption, improve AI readiness, and scale AI enablement across the business. In partnership with Accelerated Innovation.

Ideal For

Organizations, groups, and teams across all functions aiming to understand their readiness to adopt, accelerate, and scale effective AI use.

Capabilities by Dimension

15 total capabilities

AI Direction

• AI Usage Clarity

• AI Strategy

• AI Roadmap

AI Technology & Data

• AI Tools & Platforms

• AI Data Access

• AI Infrastructure

Responsible AI

• AI Ethics

• AI Governance

• AI Security

AI Fluency

• AI Understanding

• AI Prompting

• AI Output Evaluation

AI Value Management

• AI Use Case Discovery

• AI Use Case Prioritization

• AI Impact Measurement

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Capability Growth Criteria Example

AI Understanding

Select the option that best describes the extent to which people understand what AI is and how it works.

Starting (0)

AI Foundational Awareness is not present or minimally exists; contributors demonstrate little to no understanding of AI concepts and capabilities
Examples:
1. Confusion about AI capabilities
2. Misuse of AI tools
3. Inconsistent terminology usage
4. Avoidance of using AI

Developing (1)

Multiple contributors have an understanding of AI concepts, with references and application visible in work artifacts
Examples:
1. Informal AI learning efforts
2. AI training sessions
3. AI resource sharing
4. AI concepts discussed in meetings

Emerging (2)

A shared understanding of what AI is and how it works exists, with common language and explanations visible across work artifacts
Examples:
1. AI trainings
2. Documented AI terminology glossary
3. Shared AI learning materials
4. AI discussions without AI concept clarification

Adapting (3)

A shared understanding of what AI is and how it works is consistently demonstrated, resulting in reliable application of AI concepts
Examples:
1. Work artifacts show common AI use patterns
2. Shared clarity on when to apply AI
3. AI applied to appropriate tasks
4. Shared structured prompts

Optimizing (4)

AI Understanding is intentionally improved, resulting in reliable application of AI concepts across a broader set of use cases
Examples:
1. AI Communities of Practice
2. Role-specific AI training
3. Ongoing AI use case discovery
4. AI experiments

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