AI in Software Architecture
This dimension assesses how AI influences and enhances the architectural decisions, patterns, and governance in software systems.
Sample assessment questions for each level:
- Level -1 (Resistant): “Does the organization actively resist incorporating AI considerations into architectural decisions?”
- Level 0 (Ad-hoc): “Are AI components added to architectures on a case-by-case basis without standards?”
- Level 1 (Exploratory): “Has the team identified architectural patterns or components that could benefit from AI enhancement?”
- Level 2 (Structured): “Are there defined standards for including AI capabilities in architectural designs?”
- Level 3 (Established): “Is there a comprehensive architectural framework that systematically incorporates AI capabilities?”
- Level 4 (Integrated): “Does the architecture actively adapt based on AI-driven insights about system behavior?”
- Level 5 (Transformative): “Is the architecture itself evolving through AI, with components that self-optimize based on usage patterns?”
Key metrics to track:
- AI-native architecture adoption: Percentage of systems designed with AI considerations from inception
- Architecture complexity management: Effectiveness of managing complexity introduced by AI components
- Technical debt reduction: Measured decrease in technical debt through AI-assisted architecture decisions
- Architectural resilience: System resilience to changes in AI models or capabilities
- AI integration standardization: Consistency of AI integration approaches across architecture
- Architecture adaptability: Speed at which architecture can evolve to incorporate new AI capabilities
- Architectural decision quality: Improvement in architectural decisions supported by AI analysis
