.png)
4 months ago
4
As AI becomes embedded in mainstream development, engineering teams face rising challenges: large number of codebases now include AI-generated code, yet many teams lack structured governance frameworks, skill alignment, or clarity on responsible adoption. Tooling is racing ahead, but maturity varies wildly—creating risks around maintainability, trust, and innovation.
The AI Maturity Model for Software Engineering Teams (AI-MM SET) is a structured framework designed to help software engineering teams and individuals assess, align, and evolve their adoption of artificial intelligence. It describes how teams transition from early, unstructured experimentation to strategic and ultimately transformative use of AI across engineering workflows, development tools, and organizational culture.
Structured as a three-axis maturity matrix, this model outlines:
- Five Maturity Levels – From Exploratory (Level 1) to Transformational (Level 5), showing increasing sophistication, integration, and impact of AI.
- Six Core Dimensions – Capturing how capabilities grow across skills, processes, platforms, governance, collaboration, and outcomes.
- Role-Based Progression – Describing how expectations evolve from Junior Engineer to Distinguished Engineer, with growing responsibilities for integration, mentorship, and innovation.
| 1. Exploratory | AI usage is unstructured and highly variable across the team. Some developers experiment out of curiosity, while others may lack awareness. There are no shared practices, no oversight, and no influence from senior roles. Teamwide adoption is absent. |
| 2. Applied | AI tools are introduced through pilots or individual initiative. Engineers begin using AI for specific tasks, gaining experience with tools and prompting. Teams share emerging practices, but adoption is inconsistent and depends on personal interest. |
| 3. Standardized | AI usage becomes consistent across teams and roles. Engineers show fluency, peer review processes evolve to include AI outputs, and senior contributors mentor others. Tools are integrated into workflows, and governance policies are introduced and enforced. |
| 4. Strategic | AI is embedded across the entire software lifecycle and aligned with team and business strategy. Engineers design AI-first workflows, collaborate across roles, and lead scalable integration. Governance and performance measurement are proactive and structured. |
| 5. Transformational | AI is deeply embedded in culture, architecture, and day-to-day engineering. Engineers and AI systems operate as collaborative peers. Roles evolve to supervise, orchestrate, and co-create with AI. Innovation is continuous, and AI differentiates the organization. |
The AI Maturity Model evaluates progress across six critical dimensions that define responsible, effective, and high-impact AI adoption in software engineering:
| AI Literacy & Competency | Developer fluency with AI tools, prompt engineering, critical evaluation, and peer mentorship. |
| Workflow & SDLC Integration | Integration of AI across all stages of the software development lifecycle. |
| Tooling Integration | Embedding AI into developer environments, CI/CD, and scalable internal platforms. |
| Trust, Safety & Governance | Secure, fair, and transparent use of AI with enforceable policies and oversight. |
| AI-Augmented Collaboration | Practices for human-AI teaming, review workflows, and trust calibration. |
| Business Impact & Innovation | AI’s role in accelerating delivery, enabling innovation, and driving measurable business value. |
| AI Literacy & Competency | Basic awareness of AI tools with sporadic, unstructured usage. Limited understanding of capabilities and appropriate applications. | Developing practical AI skills for specific tasks. Learning prompt engineering and beginning to evaluate AI outputs critically. | Consistent AI fluency across development tasks. Effective mentoring of others and establishment of competency standards within teams. | Advanced AI expertise driving adoption initiatives. Creates learning frameworks and defines competency requirements for technical roles. | Industry-leading AI engineering expertise. Shapes organizational AI strategy and influences industry standards for AI competency. |
| Workflow & SDLC Integration | Fragmented AI usage outside established processes. No systematic integration with development lifecycle or team workflows. | Targeted integration of AI into specific development phases. Following team guidelines with basic workflow documentation. | Comprehensive AI integration across entire SDLC. Standardized practices with established patterns and quality gates. | AI-first workflow design aligned with business strategy. Systematic identification of automation opportunities and process optimization. | Revolutionary AI-native development paradigms. Continuous evolution of workflows that establish industry benchmarks. |
| Tooling Integration | Basic external AI tools used in isolation. Limited integration with development environments and team infrastructure. | Adoption of team-approved AI tools with plugin-based integration. Competent usage of core AI development features. | Well-integrated AI toolchain across IDE, CI/CD, and development workflows. Standardized tool configurations and team practices. | Robust internal AI platforms with automated pipelines. Custom integrations and advanced AI infrastructure capabilities. | Intelligent, adaptive AI platforms that drive engineering at scale. Industry-leading AI development experiences and infrastructure. |
| Trust, Safety & Governance | No formal policies or risk management. Unaware of security, privacy, and compliance implications of AI usage. | Emerging guidelines with basic safety practices. Growing awareness of AI risks and seeking guidance on governance issues. | Defined policies with automated and peer-enforced compliance checks. Systematic risk assessment and governance frameworks. | Comprehensive governance embedded in all AI workflows. Proactive risk management and validation systems for AI-assisted decisions. | Institutionalized responsible AI practices with adaptive governance. Leadership in AI safety standards and regulatory compliance. |
| AI-Augmented Collaboration | Individual AI usage without team coordination. Limited transparency about AI contributions and decision-making processes. | Intentional AI usage with basic review processes. Growing transparency and incorporation of AI into collaborative workflows. | Established AI collaboration patterns with clear review protocols. Effective human-AI teaming with defined roles and responsibilities. | Optimized AI collaboration frameworks with sophisticated human-AI interaction models. Clear governance and shared oversight mechanisms. | Revolutionary AI collaboration paradigms that redefine work patterns. Fluid strategic human-AI co-creation that becomes organizational standard. |
| Business Impact & Innovation | No measurable business value from AI usage. Viewing AI primarily as individual productivity shortcut without strategic consideration. | Targeted productivity improvements in specific use cases. Beginning to measure and communicate AI value for development work. | Consistent measurable gains in quality, velocity, and team productivity. Systematic tracking of AI impact on business outcomes. | Differentiated capabilities creating competitive advantages. AI-driven innovation that shapes product strategy and market positioning. | Breakthrough AI capabilities that redefine business models. Transformational innovation that establishes new industry standards and market categories. |
Each role in the software engineering career ladder—Junior through Distinguished Engineer—has tailored expectations across the six dimensions and five maturity levels. This allows for aligned growth at both the individual and organizational level.
- Junior Engineer – Builds early AI fluency and confidence through experimentation, learning, and feedback.
- Mid-Level Engineer – Applies AI to accelerate delivery and begins influencing team workflows.
- Senior Engineer – Leads by example in AI-assisted development and mentors peers in responsible use.
- Staff Engineer – Drives AI integration into team practices and cross-team architecture decisions.
- Principal Engineer – Aligns AI adoption with business goals, and steers platform-level innovation.
- Distinguished Engineer – Sets visionary direction for AI across the organization and influences industry best practices.
The model is informed by current research and grounded in real-world engineering practice, including the use of generative AI tools, AI-assisted software delivery, and responsible governance frameworks. It provides a grounded, role-specific roadmap for navigating AI adoption with confidence, managing risk, and realizing measurable value.
To learn more about how this model was created and why, see:
- methodology.md: Explains the research synthesis, sources, and design process used.
- motivation.md: Discusses the need for this model and the challenges it addresses.
Teams and organizations can use this model to:
- Assess their current maturity level across each dimension
- Explore how responsibilities differ by role and what advancement looks like
- Benchmark team or department-wide AI progress
- Plan investments in tooling, training, and process improvements
- Align AI practices with product strategy and long-term business goals
Use the matrix for retrospectives, planning, or capability assessments.
Contributions are welcome. See contributing.md for participation guidelines.
The AI-MM SET is licensed under CC BY 4.0 - you're free to use, adapt, and distribute with attribution. See LICENSE for details.
