AI Maturity Assessment

For Software Engineering Teams (AI-MM SET)

🧪 Why Assess Your Maturity?

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.

Source: Github

📋 Questionnaire

How would you rate your own 'AI Literacy & Competency'?

Definition: Developer fluency with AI tools, prompt engineering, critical evaluation, and peer mentorship.

How would you rate your own 'AI Literacy & Competency'?

Exploratory: Basic awareness of AI tools with sporadic, unstructured usage. Limited understanding of capabilities and appropriate applications.

Applied: Developing practical AI skills for specific tasks. Learning prompt engineering and beginning to evaluate AI outputs critically.

Standardized: Consistent AI fluency across development tasks. Effective mentoring of others and establishment of competency standards within teams.

Strategic: Advanced AI expertise driving adoption initiatives. Creates learning frameworks and defines competency requirements for technical roles.

Transformational: Industry-leading AI engineering expertise. Shapes organizational AI strategy and influences industry standards for AI competency.

How would you rate your own 'Workflow & SDLC Integration'?

Definition: Integration of AI across all stages of the software development lifecycle.

How would you rate your own 'Workflow & SDLC Integration'?

Exploratory: Fragmented AI usage outside established processes. No systematic integration with development lifecycle or team workflows.

Applied: Targeted integration of AI into specific development phases. Following team guidelines with basic workflow documentation.

Standardized: Comprehensive AI integration across entire SDLC. Standardized practices with established patterns and quality gates.

Strategic: AI-first workflow design aligned with business strategy. Systematic identification of automation opportunities and process optimization.

Transformational: Revolutionary AI-native development paradigms. Continuous evolution of workflows that establish industry benchmarks.

How would you rate your own 'Tooling Integration'?

Definition: Embedding AI into developer environments, CI/CD, and scalable internal platforms.

How would you rate your own 'Tooling Integration'?

Exploratory: Basic external AI tools used in isolation. Limited integration with development environments and team infrastructure.

Applied: Adoption of team-approved AI tools with plugin-based integration. Competent usage of core AI development features.

Standardized: Well-integrated AI toolchain across IDE, CI/CD, and development workflows. Standardized tool configurations and team practices.

Strategic: Robust internal AI platforms with automated pipelines. Custom integrations and advanced AI infrastructure capabilities.

Transformational: Intelligent, adaptive AI platforms that drive engineering at scale. Industry-leading AI development experiences and infrastructure.

How would you rate your own 'Trust, Safety & Governance'?

Definition: Secure, fair, and transparent use of AI with enforceable policies and oversight.

How would you rate your own 'Trust, Safety & Governance'?

Exploratory: No formal policies or risk management. Unaware of security, privacy, and compliance implications of AI usage.

Applied: Emerging guidelines with basic safety practices. Growing awareness of AI risks and seeking guidance on governance issues.

Standardized: Defined policies with automated and peer-enforced compliance checks. Systematic risk assessment and governance frameworks.

Strategic: Comprehensive governance embedded in all AI workflows. Proactive risk management and validation systems for AI-assisted decisions.

Transformational: Institutionalized responsible AI practices with adaptive governance. Leadership in AI safety standards and regulatory compliance.

How would you rate your own 'AI-Augmented Collaboration'?

Definition: Practices for human-AI teaming, review workflows, and trust calibration.

How would you rate your own 'AI-Augmented Collaboration'?

Exploratory: Individual AI usage without team coordination. Limited transparency about AI contributions and decision-making processes.

Applied: Intentional AI usage with basic review processes. Growing transparency and incorporation of AI into collaborative workflows.

Standardized: Established AI collaboration patterns with clear review protocols. Effective human-AI teaming with defined roles and responsibilities.

Strategic: Optimized AI collaboration frameworks with sophisticated human-AI interaction models. Clear governance and shared oversight mechanisms.

Transformational: Revolutionary AI collaboration paradigms that redefine work patterns. Fluid strategic human-AI co-creation that becomes organizational standard.

How would you rate your own 'Business Impact & Innovation'?

Definition: AI’s role in accelerating delivery, enabling innovation, and driving measurable business value.

How would you rate your own 'Business Impact & Innovation'?

Exploratory: No measurable business value from AI usage. Viewing AI primarily as individual productivity shortcut without strategic consideration.

Applied: Targeted productivity improvements in specific use cases. Beginning to measure and communicate AI value for development work.

Standardized: Consistent measurable gains in quality, velocity, and team productivity. Systematic tracking of AI impact on business outcomes.

Strategic: Differentiated capabilities creating competitive advantages. AI-driven innovation that shapes product strategy and market positioning.

Transformational: Breakthrough AI capabilities that redefine business models. Transformational innovation that establishes new industry standards and market categories.