# Action areas and recommendations
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To integrate generative AI into your ADM operating model successfully, consider the recommendations in the following action areas. These recommendations can help you navigate your organization's transformation journey and overcome common challenges.

**Governance and strategy **– To establish effective AI governance and align it with overall business strategy, consider implementing these key actions:

1. Establish cross-functional AI steering committees with AI champions.

1. Develop clear AI governance policies, including ethical use guidelines.

1. Align KPIs and business objectives with AI capabilities continuously.

1. Collaborate with regulatory bodies on AI-driven compliance processes.

**AI Center Of Excellence **– To maximize the impact of an AI Center of Excellence (COE) in your ADM practices, focus on these initiatives:

1. Establish and launch a dedicated AI COE to drive adoption, ensure best practices, and provide guidance across ADM.

1. Develop comprehensive COE operating procedures and a service catalog outlining AI-related services and support.

1. Continuously expand COE capabilities through advanced AI research and strategic partnerships.

**Education and culture **– To support a culture of AI adoption and continuous learning across the organization, consider these actions:** **

1. Implement comprehensive AI literacy programs across the organization.

1. Foster a culture of experimentation, learning, and adaptation.

1. Create training programs to upskill platform teams in AI-augmented operations.

**Technology and process **– To integrate AI effectively into your technology stack and processes, prioritize these initiatives:** **

1. Implement AI-driven tools for architecture recommendation and resource provisioning.

1. Develop AI models for predictive capacity planning and performance optimization.

1. Integrate AI-powered observability and anomaly detection systems.

1. Establish AI-assisted compliance checking and security monitoring processes.

1. Implement standardized data collection frameworks across projects.

1. Develop AI models that accommodate both waterfall and agile methodologies.

**Data and security **– To support data quality and security efforts, focus on these actions:

1. Invest in data integration, quality assurance, and security processes.

1. Create feedback mechanisms for continuous improvement of AI systems.

**Change management **– To facilitate smooth adoption of AI technologies, use these change management approaches:

1. Redesign stakeholder communication channels for AI-enhanced collaboration.

1. Implement change management programs to build trust in AI-generated insights.

**Skill development** – To build the necessary AI capabilities, support this skill development initiative:
+ Upskill teams in data science, AI interpretation, and AI-powered tools.

**Partnerships **– To harness external expertise, consider these ideas for partnerships:

1. Make use of application managed services (AMS) partners for AI implementation.

1. Consider infrastructure and/or CloudOps managed services partners for AI integration across platform engineering services.

1. Use IT services management partners for AI integration with service management and governance services.

**Human oversight **– To maintain appropriate human control and accountability, implement the following approach:
+ Establish protocols for human oversight of AI-generated recommendations.

Embracing these AI-driven changes and addressing challenges systematically can help you create a more agile, efficient, and innovative ADM operating model. The key to success lies in balancing human expertise with AI capabilities, aligning IT services closely with organizational objectives. This approach can drive significant business value, enhance an organization's competitive advantage, and position the organization to lead in the next era of ADM.