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Organization capability layer of an ADM operating model - AWS Prescriptive Guidance

Organization capability layer of an ADM operating model

Traditionally, organizational capabilities such as knowledge management, communication and collaboration, and program or change management tools lack an AI-specific focus. As you integrate generative AI into your ADM practices, your organizational capabilities must evolve. This section outlines key areas for transformation and strategies to make effective use of your AMS partners. This section also explores how AI drives global resource distribution, cultivates essential skills, develops new competencies, establishes AI CoEs, and fosters a continuous learning culture.

Strategic partners and talent development – To build strategic partnerships and develop talent for AI integration, focus on these key initiatives:

  • Implement comprehensive AI training programs.

  • Establish AI Centers of Excellence (COEs).

  • Use AI for improved career planning, recruitment, training, and resource optimization.

  • Implement location-specific AI adoption change management plans.

  • Develop best practices, standards, and point of views (POVs) more efficiently by using AI.

  • Conduct technology evaluation and proof of concepts (POCs) that are aligned with IT architecture roadmaps.

Operating model redesign – The integration of AI necessitates a redesign of the operating model, including the following changes:

  • Redefine roles to incorporate AI-augmented development.

  • Assign AI-driven strategic tasks to onshore teams to maintain close collaboration with key decision-makers.

  • Develop new QA processes for AI-generated code.

Enhanced collaboration and knowledge management – Consider enhancing collaboration and knowledge management through these approaches:

  • Implement AI-powered collaboration tools to reduce time zone dependencies.

  • Make use of AI to catalog and index enterprise knowledge more effectively.

  • Use AI-driven insights from customer feedback, issue resolution, and industry trends for accelerated market research and business requirements analysis.

Governance and compliance – To help ensure proper governance and compliance when integrating AI in an operating model, consider implementing the following measures:

  • Establish a global AI governance framework with location-specific compliance requirements.

  • Address IP ownership of AI-generated assets and mitigate infringement risks.

Infrastructure and tools standardization – Efforts to standardize infrastructure and tools across the organization for effective AI integration involves these steps:

  • Invest in cloud-based AI-augmented platforms that are accessible from all locations.

  • Standardize AI tools and environments globally.

Performance metrics and engagement model adaptation – Adapting performance metrics and engagement models for AI-driven processes includes these key actions:

  • Develop new KPIs that account for AI contributions.

  • Implement AI-assisted project estimation tools.

  • Consider flexible engagement models, including outcome-based pricing.

  • Define consumption-based pricing models for AI assets, covering licenses, infrastructure, and managed services efforts.

Program and change management augmentation – To strengthen program and change management, consider these strategies:

  • Enhance the co-source model between in-house talent, consulting and AMS partners by using AI.

  • Improve knowledge collection, methodology refinement, and experience reuse for new initiatives.

By focusing on these areas, you can integrate generative AI effectively across your global delivery locations and organizational capabilities. This approach helps to accelerate transforming your ADM operating model. It improves decision-making velocity and enhances the delivery of business outcomes while balancing the strengths of each location and addressing the challenges of AI integration.