Maturity Levels
Level 1: AI-Curious
Level 1: AI-Curious
Characteristics:
- Individual developers experimenting with AI tools
- No organizational strategy or guidelines
- Ad-hoc usage of code completion tools
- Establish AI tool policies
- Begin tracking AI-assisted productivity
- Encourage experimentation within boundaries
Level 2: AI-Assisted
Level 2: AI-Assisted
Characteristics:
- Organization has adopted AI coding assistants
- Guidelines exist for AI tool usage
- Human-driven development with AI support
- Develop spec-driven practices
- Train teams on effective AI collaboration
- Establish quality gates for AI-generated code
Level 3: AI-Integrated
Level 3: AI-Integrated
Characteristics:
- AI is embedded in development workflows
- Spec-driven development practices in place
- Metrics track AI contribution to delivery
- Implement agent-based workflows
- Develop AI-specific testing strategies
- Create feedback loops for AI improvement
Level 4: AI-Native
Level 4: AI-Native
Characteristics:
- Agents operate as team members
- Continuous learning loops established
- Human-AI collaboration is the norm
- Optimize agent orchestration
- Scale practices across organization
- Contribute to AI-Native community
Level 5: AI-Leading
Level 5: AI-Leading
Characteristics:
- Organization defines industry best practices
- Advanced agent orchestration and autonomy
- AI capabilities inform product strategy
- Share learnings with the community
- Push boundaries of human-AI collaboration
- Shape the future of software development
Assessment Dimensions
| Dimension | Questions to Ask |
|---|---|
| Strategy | Is AI part of your engineering strategy? |
| Culture | Do teams embrace AI as collaborators? |
| Process | Are workflows designed for AI-human collaboration? |
| Tools | What AI tools are adopted and integrated? |
| Skills | Are engineers trained in AI collaboration? |
| Governance | How is AI usage monitored and governed? |