Implementing an AI-powered ADM target operating model

Use a structured, phased approach to implement a generative AI application development and maintenance (ADM) target operating model (TOM). The following approach balances quick wins with long-term transformative changes while minimizing disruption to current operations. Each phase addresses specific components of the TOM, highlighting their interdependencies and evolution throughout the implementation process.

As shown in the following diagram, the implementation strategy consists of phases that progress from basic to advanced complexity over a 12-month period:

• 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.

• Phase 2: Capability building – This phase occurs in months 3-6. It expands AI adoption and addresses processes of medium complexity. Launch your AI COE, expand AI adoption to project management and operations roles, and collaborate with your ADM partners to redesign key SDLC processes using generative AI.

• Phase 3: Transformation scaling – This phase occurs in months 6–12 (and beyond). It implements advanced solutions and tackles higher complexity challenges. For example, implement advanced AI solutions for architecture design, full-stack development, and security monitoring. Mature your AI governance to an enterprise level, and evolve your contractual relationships with ADM partners to reflect the new AI-powered reality.

Note

Before beginning implementation, conduct an AI-powered SDLC readiness assessment to establish a baseline of your organization's current SDLC capabilities and identify key areas for improvement. For more details, see Next steps.

Actual timelines can vary based on organizational context, implementation approach, and other factors such as the size and scale of implementation. Some organizations might achieve results in a shorter or longer time span, depending on their specific circumstances and maturity levels.

By progressing through these phases, you can transform your organization's ADM practices systematically, using AI to drive innovation, efficiency, and competitive advantage. For more information about using a phased approach in your organization, see Roadmap for implementing an AI-powered ADM TOM and Best practices for all implementation phases.

Organizations can enhance their in-house capabilities through this transformation journey. This journey also requires continuous adjustment and clear communication with all stakeholders. The result is an integrated, global ADM target operating model for AI-powered software development and maintenance with your consulting and technology service providers.

Roadmap for implementing an AI-powered ADM TOM

The following table provides a reference roadmap that uses a phased approach to implement an ADM TOM while minimizing disruption to current operations. For each ADM component, the roadmap describes the relevant activities that occur in each implementation phase.

ADM component	Foundation setting: Months 1-3	Capability building: Months 3–6	Transformation scaling: Months 6–12 and beyond
Strategic alignment	Enable AI steering committee. Set vision, mission, and goals with business alignment. Develop AI technology and tools strategy and roadmap.	Continuously align KPIs and business objectives with AI capabilities. Maintain clear stakeholders communication on AI initiatives with impact. Review business outcomes and ROI.	Continuously align KPIs and business objectives with AI capabilities. Maintain clear stakeholders communication on AI initiatives with impact. Review business outcomes and ROI. Integrate AI governance with EA. Establish cross-functional AI governance with AMS partners. Standardize AI tools globally across in-house and AMS partner teams.
Organizational structure	Identify cross-functional AI champions. Identify key roles for AI integration.	Launch AI COE with dedicated team.	Implement AI-driven organization and continuous optimization.
Talent and skills	Implement basic AI training program. Adopt AI tools for high propensity roles such as software developers and test engineers. Implement advance AI training program. Implement role-specific AI training program.	Implement role-specific AI training program. Develop AI-focused career paths and progression. Implement shared training programs for onshore and offshore teams.	Implement role-specific AI training program. Extend AI adoption to product owners, BA, SA, and domain SMEs. Establish AI innovation incentive program. Establish mechanisms for ongoing AI knowledge sharing between your organization and AMS partners.
Governance and ethics	Develop AI ethics guidelines. Establish guidelines for AI-related IP and data usage. Create risk assessment framework. Collaborate with regulatory bodies for compliance.	Implement AI governance policies and procedures. Balance AI automation with human oversight to ensure quality and maintain control.	Balance AI automation with human oversight to ensure quality and maintain control. Develop AI-specific project and contract templates and SLAs for AMS partners. Continuously review and address data privacy and security concerns in AI usage part of the ADM.
Performance measurement	Establish AI goals and key success metrics for ADM. Establish key success metrics for large language models (LLMs).	Develop AI-specific KPIs for ADM processes. Develop AI-specific KPIs for ADM partner performance. Implement AI cost allocation and ROI tracking.	Establish KPIs and implement an ADM and SDLC performance dashboard. Implement AI-driven insights for continuous improvement of the ADM global delivery model. Continuously monitor and adjust based on feedback and results.
Partner ecosystem	Engage AMS partner for transformation planning. Align AI integration roles with AMS partners. Assess AI readiness with AMS and CloudOps partners. Review existing AMS contracts for AI integration.	Establish joint AI COE with AMS and CloudOps partners. Work with ADM partners to integrate AI in the TOM. Collaborate with AMS partners to implement advanced AI solutions for ADM.	Collaborate with AMS partners to implement advanced AI solutions for ADM. Standardize AI tools and environments with AMS partners. Regularly assess the impact of AI on the AMS outsourcing value proposition. Consider flexible engagement models and outcome-based pricing for AI-enhanced services.
Technology and tools	Implement AI-powered knowledge bases for faster issue resolution. Implement AI-powered collaboration tools. Adopt AI-assisted coding and testing tools.	Integrate AI-driven project planning and risk assessment tools. Implement AI-powered release management and predictive maintenance. Implement AI-assisted project estimator tools.	Implement AI-driven architecture decision support tools. Adopt AI-powered full-stack code generation and optimization tools. Implement cloud-based AI-augmented platforms for all delivery locations.
Processes	Establish guidelines for integrating AI-generated and manual code. Establish process and SOPs for AI-powered tools. Establish feedback loop for continuous improvement of LLMs.	Redesign ADM processes to incorporate AI in the TOM. Develop AI-driven SOPs between onshore, nearshore, and offshore locations.	Establish processes for AI-driven architecture decision and full-stack code generation. Establish AI-assisted compliance check and security monitoring processes. Establish mechanism for process improvements on AI-powered ADM operating model.

For information about a framework of an AI vision for ADM that includes a mission statement, objectives, and strategic initiatives, see Appendix A: Sample framework of AI vision for ADM. For a detailed implementation checklist covering governance, organizational structure, roles, processes, and tools across all three phases, see Appendix B: Implementation checklist for an ADM TOM.

Best practices for all implementation phases

The following best practices are important to keep in mind through all implementation phases. For each best practice, its related operating model component is shown, indicating which aspect of the model is most affected:

• Monitor and adjust the approach continuously based on feedback and results. (Performance measurement)

• Communicate clearly with all stakeholders about various AI initiatives and their impact. (Strategic alignment)

• Balance AI automation with human oversight to help ensure quality and maintain control. (Governance and ethics)

• Assess regularly the return on investment (ROI) of AI initiatives and adjust strategy accordingly. (Performance measurement; strategic alignment)

• Address data privacy and security concerns that are specific to AI usage in a global delivery model. (Governance and ethics)

• Evaluate regularly the impact of AI on the outsourcing value proposition and adjust the engagement model as needed. (Partner ecosystem; strategic alignment)