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Delivery AI Enablement & Productivity

Capability Template Details

Delivery AI Enablement & Productivity

Enables teams to assess, improve, and measure the foundational capabilities, structures, and conditions required to effectively adopt and scale AI. In partnership with Accelerated Innovation.

Ideal For

Software delivery organizations, groups, or teams, aiming to accelerate and scale AI adoption, use it more effectively, and boost productivity.

Capabilities by Dimension

21 total capabilities

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

AI-Assisted Engineering

• AI-Assisted Coding

• AI-Assisted Debugging

• AI-Assisted Unit Test Creation

AI-Assisted Testing

• AI-Assisted Test Case Creation

• AI-Assisted Test Data Creation

• AI-Assisted Test Execution

AI Product Management

• AI-Assisted Product Discovery

• AI-Assisted Story Writing

• AI-Assisted Documentation

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Capability Growth Criteria Example

AI-Assisted Coding

Select the option that best describes the extent to which AI is used to better enable the creation and modification of source code today.

Starting (0)

AI-Assisted Coding is not used or is rarely utilized; code is primarily written and modified without AI support
Examples:
1. No AI coding tools installed
2. PRs contain no AI-generated code
3. Boilerplate code recreated repeatedly
4. No discussion of AI usage in code reviews

Developing (1)

Multiple contributors independently use AI at their own discretion to assist with coding, with its use visible in work artifacts
Examples:
1. AI references in PRs
2. AI-generated code snippets in codebase
3. Code comments referencing AI
4. Personal coding prompts

Emerging (2)

Shared practices are defined for when and how AI is applied to coding tasks, producing visible, repeatable, and reliable coding patterns
Examples:
1. Published AI coding guidelines
2. Shared IDE AI tooling
3. Centralized coding prompt templates
4. Consistent AI patterns in code reviews

Adapting (3)

AI-Assisted Coding practices are consistently applied as part of standard development workflows and reliably produce desired outcomes reflected in performance metrics
Examples:
1. PR templates include required AI fields
2. Definition of Done includes AI code review
3. Estimation considers AI coding
4. Improved rework metrics for AI code

Optimizing (4)

AI-Assisted Coding practices within standard development workflows are intentionally and continuously improved, resulting in consistent gains in performance metrics over time
Examples:
1. AI impact on coding workflow reviews
2. AI coding handles larger units of work
3. Context enrichment prompt updates
4. Improved cycle time trends