Appendix B: Implementation checklist for an ADM TOM
This comprehensive checklist provides you with a structured approach to implementing an application development and maintenance (ADM) target operating model (TOM). The checklist considers governance, organizational structure, personnel roles, processes, and tools for each of the following phases of implementation:
Each phase builds upon the previous phase, enabling organizations to scale their AI capabilities systematically while managing risks and ensuring sustainable enterprise-wide adoption.
Phase 1: Foundation setting
This phase occurs in months 1–3. It establishes basic governance structures and introduces essential AI tools while achieving quick wins.
Governance and organization
1.1. Establish an AI governance steering committee.
1.2. Develop initial AI ethics guidelines for ADM processes.
1.3. Create a baseline AI risk assessment framework.
1.4. Identify key roles for AI integration across ADM teams.
1.5. Define initial AI champion roles within existing teams.
1.6. Outline the vision and mission for an AI Center of Excellence (COE) in ADM.
1.7. Conduct an AI skills gap analysis across ADM teams.
1.8. Develop a basic AI literacy training program for all staff.
1.9. Review existing vendor contracts for AI integration potential.
1.10. Establish initial budgeting guidelines for AI initiatives in ADM.
Roles
1.11. Software developer
Adopt AI-assisted coding, pair programming, and code completion tools.
Establish guidelines for reviewing and optimizing AI-generated code.
1.12. Test engineer
Adopt AI-powered test case generation, execution, and data quality improvement tools.
Implement AI-augmented exploratory testing techniques.
1.13. UX designer
Adopt AI-assisted design tools and data-driven design techniques.
1.14. DevOps engineer
Implement AI-powered CI/CD pipeline optimization.
Adopt AI-assisted infrastructure as code (IaC) generation tools.
1.15. Support engineer
Use AI-powered knowledge bases for faster issue resolution.
Implement AI-driven ticket classification and routing systems.
Processes
1.16. Create clear escalation protocols for complex issues.
1.17. Establish guidelines for integrating AI-generated and manual code.
1.18. Develop new QA processes for AI-generated code.
1.19. Establish processes for human oversight of AI-generated designs.
1.20. Establish processes for continuous refinement of AI testing models.
1.21. Improve knowledge collection, methodology refinement, and experience reuse for new initiatives.
Tools
1.22. Adopt AI-assisted coding, pair programming, and code completion tools.
1.23. Implement AI-driven code quality, consistency checks, and bug detection systems.
1.24. Adopt AI-assisted documentation tools for design documents.
1.25. Implement AI-powered collaboration tools to reduce time zone dependencies.
1.26. Adopt AI-powered test case generation, execution, and data quality improvement tools.
1.27. Implement AI-assisted project estimation tools.
1.28. Set up predictive defect analysis using AI.
1.29. Adopt AI-assisted design tools and data-driven design techniques.
Phase 2: Capability building
This phase occurs in months 3-6. It expands AI adoption and addresses processes of medium complexity.
Governance and organization
2.1. Implement AI governance policies and procedures.
2.2. Establish an AI ethics review process for ADM projects.
2.3. Develop AI-specific KPIs for ADM processes.
2.4. Create new AI-focused roles such as an AI integration specialist.
2.5. Realign team structures to support AI-augmented workflows.
2.6. Launch the AI COE with a dedicated team.
2.7. Establish COE operating procedures and service catalog.
2.8. Implement role-specific AI training programs.
2.9. Develop AI-focused career paths and progression models.
2.10. Develop AI-specific procurement guidelines.
2.11. Implement AI cost allocation and return on investment (ROI) tracking mechanisms.
Roles
2.12. Project manager
Integrate AI-driven project planning, risk assessment, and resource allocation tools.
Develop protocols for AI-human collaborative decision making.
Set up real-time project health monitoring and predictive analytics using AI.
2.13. Release manager
Adopt AI-powered release management, planning, and risk assessment tools.
Implement automated deployment and rollback strategies using AI.
Set up predictive post-release monitoring and issue detection systems.
2.14. Site reliability engineer
Adopt AI-driven predictive maintenance tools.
Implement AI-powered anomaly detection and automated remediation systems.
2.15. Technical writer
Use AI-assisted documentation generation tools.
Implement AI-powered content optimization and readability analysis.
Processes
2.16. Create feedback loops to improve AI models continuously based on project outcome.
2.17. Implement continuous learning mechanisms for AI support system.
2.18. Implement continuous learning mechanisms for AI prediction models.
2.19. Establish processes for validating AI-generated solution proposals.
2.20. Establish processes for human validation of AI-generated release plans.
Tools
2.21. Integrate AI-driven project planning, risk assessment, and resource allocation tools.
2.22. Set up real-time project health monitoring and predictive analytics using AI.
2.23. Implement AI-driven tools for continuous solution optimization.
2.24. Implement AI-driven user research analysis and persona creation systems.
2.25. Set up automated usability testing and feedback analysis using AI.
2.26. Adopt AI-powered release management, planning, and risk assessment tools.
2.27. Implement automated deployment and rollback strategies using AI.
2.28. Set up predictive post-release monitoring and issue detection systems.
2.29. Implement AI-driven monitoring, predictive maintenance, and resource allocation systems.
2.30. Set up accelerated issue resolution processes using AI.
Phase 3: Transformation scaling
This phase occurs in months 6–12 and beyond. It implements advanced solutions and tackles higher complexity challenges.
Governance and organization
3.1. Integrate AI governance into overall enterprise governance.
3.2. Implement continuous improvement processes for AI policies.
3.3. Establish cross-functional AI governance committees.
3.4. Fully integrate AI roles across all ADM teams.
3.5. Implement AI-driven organizational design optimization.
3.6. Expand COE capabilities to include advanced AI research.
3.7. Establish partnerships with external AI research institutions.
3.8. Implement AI-powered personalized learning paths.
3.9. Establish an AI innovation incentive program for employees.
3.10. Develop AI-specific contract templates and service level agreements (SLAs).
3.11. Implement AI-driven financial forecasting and optimization for ADM.
Roles
3.12. Product owner or business analyst
Implement AI-powered market analysis and requirements gathering tools.
Develop prompt engineering skills for effective AI interaction.
3.13. Solutions architect
Adopt AI-powered solution design tools and methodologies.
Implement AI-driven tools for continuous solution optimization.
3.14. Full-stack developer
Adopt AI-powered full-stack code generation and optimization tools.
Implement AI-driven API design and integration systems.
3.15. Technical lead
Adopt AI-powered application lifecycle management tools.
Create training programs to upskill teams in AI-augmented DevOps practices.
3.16. Security subject matter expert (SME)Implement AI-powered threat detection and response systems.
Adopt AI-assisted security policy generation and compliance checking tools.
3.17. Domain-specific SME
Use AI tools for domain-specific knowledge extraction and application.
Implement AI-assisted domain modelling and simulation tools.
Processes
3.18. Redesign enterprise architecture (EA) processes to incorporate AI-driven insights and automation.
3.19. Implement continuous learning mechanisms for AI systems to stay current with evolving regulations.
3.20. Establish clear protocols for human oversight of AI-generated compliance recommendations.
3.21. Establish clear protocols for human oversight of AI-generated recommendations.
3.22. Implement a comprehensive change management strategy.
Tools
3.23. Implement AI-driven architecture decision support systems.
3.24. Set up AI-powered integration and interoperability assessment systems.
3.25. Invest in data integration and quality assurance processes for AI analytics.
3.26. Establish robust security and governance frameworks for AI-driven reporting.
3.27. Implement AI-driven tools for architecture recommendation and resource provisioning.
3.28. Integrate AI-powered observability and anomaly detection systems.
3.29. Establish AI-assisted compliance checking and security monitoring processes.
3.30. Implement AI-powered market analysis and requirements gathering tools.
3.31. Adopt AI-powered solution design tools and methodologies.
3.32. Adopt AI-powered full-stack code generation and optimization tools.
3.33. Implement AI-driven API design and integration systems.
3.34. Set up automated performance tuning across the stack using AI.
3.35. Adopt AI-powered application lifecycle management tools.
3.36. Invest in cloud-based AI-Augmented platforms accessible from all locations.
3.37. Standardize AI tools and environments globally.
