Generative AI and large language models (LLMs) are rapidly transforming the nature of software engineering. In the next years (2025 - 2030), organizations will witness a dramatic shift: many traditional engineering tasks will be automated or entirely performed by AI, and engineering productivity will increase by an order of magnitude.
This evolution presents both a strategic opportunity and an existential challenge. To survive and thrive, companies must radically transform their engineering capabilities - not incrementally, but fundamentally and urgently.
The AI Engineering Adaptation Framework (AI-EAF) provides a comprehensive structure to guide organizations through the profound transformation of their engineering practices in response to AI advancements that may largely replace traditional engineering roles. Inspired by the AWS Cloud Adoption Framework (AWS CAF), but designed for a more radical transition, the AI-EAF offers:
- A structured approach to assess current engineering capabilities and readiness for AI replacement
- Clear pathways to transition from human-centered to AI-centered engineering
- Strategies to build organizational readiness for AI-driven engineering
- Guidance on managing the profound cultural and operational shifts required
- Methods to measure and demonstrate business value from AI engineering transformation
- Ethical frameworks for responsible workforce transition
The unprecedented pace of AI advancement requires organizations to build adaptability into the core of their engineering transformation strategy. The AI-EAF incorporates adaptability through:
- Continuous horizon scanning for emerging AI capabilities and their potential impact on engineering roles
- Flexible transition pathways that can accelerate or pivot as technology evolves
- Scenario planning for multiple AI evolution trajectories and their workforce implications
- Modular transformation approaches that can adapt to unexpected breakthroughs
- Regular reassessment cycles to adjust strategies based on AI advancement velocity
- Adaptive governance frameworks that evolve with emerging ethical challenges
- Dynamic workforce planning that responds to shifting AI capabilities
This adaptability principle is woven throughout all dimensions of the framework, ensuring organizations can respond effectively to the accelerating and unpredictable nature of AI evolution while maintaining strategic direction.
The framework helps organizations redesign their engineering functions across six key dimensions:
-
People & Skills: Transitioning talent from traditional engineering to AI oversight roles, redefining careers, and developing new competencies for an AI-dominated engineering environment
-
Tooling & Stack: Implementing and optimizing AI systems capable of autonomous engineering work, with human oversight architectures
-
Processes & Workflows: Reimagining software development lifecycles for AI-driven engineering with minimal human intervention
-
Governance & Ethics: Establishing responsible AI use policies, addressing bias, ensuring compliance, and managing ethical workforce transition
-
Culture & Change Management: Navigating the profound cultural shift to an organization where AI performs core engineering functions
-
Business Alignment: Transforming business models to leverage AI-driven engineering capabilities and manage the economic implications
New to AI-EAF? Start here:
Getting Started Guide: A simple onboarding checklist to help you begin your AI engineering transformation journey, including an initial assessment questionnaire and step-by-step guidance for your first 90 days.
In addition to the six core dimensions, the AI-EAF includes cross-cutting capabilities that span and support the entire transformation process:
Continuous monitoring and troubleshooting systems that help organizations identify issues early, measure transformation effectiveness, and enable rapid adaptation to evolving AI capabilities. Unlike periodic assessments, diagnostics provide real-time insights across all dimensions.
Principles of self-awareness, authenticity, purpose-driven leadership, mindfulness, and empowerment that guide leaders through the profound human transition required for AI engineering transformation. Authentic Leadership practices enhance all dimensions of the framework, ensuring that the human element remains central even as AI capabilities expand.
The AI-EAF includes a comprehensive maturity model that helps organizations assess their current state and chart a path toward AI-driven engineering. The model defines five maturity levels that apply across all dimensions:
Characterized by experimental use of AI tools by individual engineers, with no formal strategy for AI transition. Organizations at this level have limited awareness of AI's potential to replace engineering functions and are unprepared for the coming disruption.
At this level, organizations have basic AI tool integration in select teams and growing recognition that AI may substantially replace engineering roles. Initial planning for workforce transition begins, and leadership acknowledges the need for strategic response to AI disruption.
Organizations implement structured approaches to AI adoption with formal transition plans, standardized practices for AI-driven engineering, redefined roles and responsibilities, and regular measurement of AI capabilities versus human engineers.
This level features data-driven optimization of AI engineering systems, comprehensive transition programs for the workforce, advanced integration of AI across the development lifecycle, and continuous improvement based on measured outcomes. Human engineers primarily serve in oversight and strategic roles.
At the highest level, AI performs most engineering functions autonomously with human oversight focused on strategic direction and ethical boundaries. The organization has successfully transformed its operating model, workforce, and culture to thrive in an AI-driven engineering paradigm.
The maturity model provides specific indicators for each dimension, allowing organizations to assess their current state, identify gaps, and create targeted transformation plans. For detailed assessment methodology and implementation guidance, see the maturity model documentation.
Artificial intelligence is advancing at an unprecedented pace, fundamentally altering the landscape of software engineering. Eric Schmidt, former CEO of Google, recently warned that within "three to five years," researchers may achieve artificial general intelligence (AGI), leading to AI systems that "won't have to listen to us anymore". https://futurism.com/the-byte/former-google-ceo-ai-escape-humans
This projection underscores the critical need for organizations to proactively transform their engineering practices. The AI-EAF provides a structured and adaptable approach to navigate this radical transformation, ensuring that organizations are prepared for a future where AI may largely replace traditional engineering roles.
By adopting the AI-EAF, organizations can position themselves to harness the benefits of AI while managing the profound workforce and organizational changes that will accompany this transition.
The AI-EAF is designed for:
- Engineering Leaders: CTOs, VPs of Engineering, and Engineering Directors responsible for navigating the transition to AI-driven engineering
- Technology Executives: CIOs and CDOs responsible for digital transformation initiatives in the face of AI disruption (Executive Summary)
- Engineering Managers: Team leads implementing AI systems and managing workforce transitions
- Technology Strategists: Those responsible for long-term technology roadmaps in an AI-dominated future
- HR and Organizational Leaders: Professionals managing the human aspects of engineering transformation
Unlike previous technological shifts, AI engineering transformation demands more than new tools or incremental changes - it requires a fundamental rethinking of the engineering function itself. The AI-EAF enables leaders to prepare for and navigate a future where AI systems may perform most engineering tasks, transforming how organizations build and maintain technology.
The AI-EAF can be utilized in several ways depending on your organization's needs:
- Assess your organization's vulnerability to AI disruption of engineering roles
- Identify critical capabilities needed for the transition to AI-driven engineering
- Develop a comprehensive transformation roadmap with clear milestones
- Prioritize initiatives based on business impact and implementation urgency
- Align stakeholders around a common vision for AI-driven engineering
- Evaluate your organization's readiness for AI-driven engineering across each dimension
- Benchmark your capabilities against industry standards and best practices
- Identify strengths to leverage and critical gaps to address
- Track progress through the transformation journey
- Measure the impact of AI adoption on engineering productivity and workforce transition
- Structure engagements around the framework's six dimensions
- Use the framework to guide discovery and assessment activities
- Develop tailored recommendations based on the framework's principles
- Create implementation plans that address all aspects of transformation
- Provide ongoing guidance aligned with the framework's progression model
The AI-EAF Implementation App provides a digital platform to guide organizations through their transformation journey:
- Interactive assessment tools and visualizations
- Customized transformation roadmap generation
- Progress tracking and reporting
- Knowledge repository and collaboration tools
- Adaptability management for evolving AI capabilities
While the AI-EAF provides a comprehensive foundation for engineering transformation across organizations, specific industries may require tailored adaptations to address their unique contexts, workflows, and challenges. The framework is designed to be flexible and can be customized to meet the distinct needs of various sectors.
Industries with specialized engineering practices, creative processes, or regulatory environments may need to adjust certain aspects of the framework while maintaining its core principles. We provide guidance for industry-specific adaptations in the adaptations directory, currently including:
- Gaming Industry Adaptation: Addressing the unique blend of creative and technical processes in game development
- Healthcare Industry Adaptation: Navigating regulatory requirements, patient safety concerns, and clinical validation needs
- Financial Services Industry Adaptation: Addressing regulatory compliance, fiduciary responsibilities, and risk management requirements
- Additional industry adaptations will be added based on community contributions and implementation experiences
Organizations are encouraged to use these industry adaptations as starting points while further customizing the framework to their specific organizational context and needs.
With extensive experience in engineering transformation and AI & Data integration, Markus Schmidberger and Miriam Schmidberger offer strategic advisory services to organizations seeking to implement AI-EAF. While not available for direct execution of implementation tasks, they provide high-level guidance and collaborate with trusted partner consultants to drive the transformation process.
Miriam Schmidberger brings deep expertise in authentic leadership principles and practices from The Authentic Leader Academy, enhancing the framework's human-centered approach to AI transformation.
Advisory Services Include:
- Framework Customization: Tailoring the AI-EAF to align with your organization's unique context and objectives.
- Capability Assessment: Evaluating current engineering capabilities and AI readiness to identify areas for improvement.
- Transformation Roadmap Development: Creating a strategic roadmap to guide your organization's AI integration journey.
- Change Management Support: Assisting in managing organizational change and workforce transition during the transformation process.
- Ongoing Strategic Advisory: Offering continuous support and guidance throughout your AI engineering journey.
- Authentic Leadership Development: Building leadership capabilities essential for successful human-centered transformation.
For advisory inquiries, please contact Markus Schmidberger directly at [email protected].
We welcome contributions to the AI-EAF. To contribute:
- Please reach out to Markus Schmidberger at [email protected] before starting work on any contributions
- Submit contributions via pull requests, clearly describing the changes and their purpose
- Ensure contributions align with the framework's overall vision and structure
- Include relevant examples or case studies when applicable
- Follow the existing formatting and documentation standards
Your experiences and insights can help improve this framework for the entire community of leaders navigating the AI transformation journey.
This project is licensed under the MIT License - see the LICENSE file for details.