View a markdown version of this page

Building an AI-powered ADM target operating model - AWS Prescriptive Guidance
Strategic alignment componentOrganizational structure componentTalent and skills componentGovernance and ethics componentPerformance measurement componentPartner ecosystem componentTechnology and tools componentProcesses component

Building an AI-powered ADM target operating model

As you consider your ADM practices with generative AI, it's important to design a comprehensive target operating model (TOM). A TOM describes the desired state of an organization's operating model. Your organization's ADM TOM should align its people, processes, technology, organization, and governance with its strategic vision.

The following table lists the eight components of a TOM.

TOM component

Component elements

Strategic alignment

  • Value drivers

  • Business goals alignment

  • AI roadmap

Organizational structure

  • AI Centers of Excellence

  • New AI roles

  • Cross-functional teams

Talent and skills

  • Career paths

  • Continuous learning

  • AI literacy requirements

  • Skills gap analysis

Governance and ethics

  • Regulatory compliance

  • Data privacy framework

  • AI ethics policies

Performance measurement

  • Continuous monitoring

  • Business impact reporting

  • Feedback loops

  • AI-specific KPIs

Partner ecosystem

  • Partner evaluation metrics

  • Data sharing protocols

  • AI capability requirements

  • Collaborative innovation

Technology and tools

  • Data infrastructure

  • AI tools ecosystem

  • AI platforms selection

  • Legacy systems integration

Processes

  • AI-enhanced SDLC

  • AI model management

  • Governance workflows

Building an ADM TOM is a transformative process that affects every aspect of an organization. Consider each ADM component and its interdependencies carefully to create a robust foundation for your AI-powered SDLC.

Implementing an ADM TOM should be tailored to an organization's specific needs and context. As you implement this model, continuously assess and adjust it based on your organization's unique challenges and opportunities.

The following sections provide more details about the components in the ADM operating model, including their interactions.

Strategic alignment component

The strategic alignment component defines strategic objectives for AI-powered ADM, aligning AI initiatives with business goals. This component articulates AI's value in ADM processes and sets success criteria for AI integration. This component interacts with other components as follows:

  • Value drivers influence AI-specific KPIs in the performance measurement component.

  • Business goals alignment informs the creation of new AI roles in the organizational structure component.

  • The AI roadmap guides AI platforms selection in the technology and tools component.

Organizational structure component

The organizational structure component addresses the design of an ADM organization that supports AI-augmented development with new roles. This component establishes an AI Center of Excellence (COE) and evolves existing roles for AI integration.

  • The AI COE supports continuous learning in the talent and skills component.

  • New AI roles influences new AI capability requirements in the partner ecosystem component.

  • Cross-functional teams enables agile integration with AI-enhanced SDLC in the processes component.

Talent and skills component

The talent and skills component identifies required AI skills and competencies across ADM roles and personnel. This component defines AI literacy requirements and creates AI-focused career paths.

  • Career paths aligns with new AI roles in the organizational structure component.

  • AI literacy requirements supports AI ethics policies in the governance and ethics component.

  • Skills gap analysis informs the AI tools ecosystem in the technology and tools component.

Governance and ethics component

The governance and ethics component establishes an ethical framework for AI use in ADM, including policies and review boards. This component defines data privacy and security requirements for AI-powered ADM practices.

  • Regulatory compliance affects value drivers in the strategic alignment component.

  • The data privacy framework influences data sharing protocols in the partner ecosystem component.

  • AI ethics policies guides AI model management in the processes component.

Performance measurement component

The performance measurement component designs a new framework with AI-specific KPIs for ADM performance measurement. This component outlines the methods to measure, report, and optimize AI impact in ADM.

  • Business impact reporting influences partner evaluation metrics in the partner ecosystem component.

  • Feedback loops supports continuous learning in the talent and skills component.

  • AI-specific KPIs informs business goals alignment in the strategic alignment component.

Partner ecosystem component

The partner ecosystem component defines expectations for AI capabilities in AMS partners and collaborative processes. This component establishes data sharing and model ownership principles for partner interactions.

  • Partner evaluation metrics informs AI-specific KPIs in the performance measurement component.

  • AI capability requirements influences skill gap analysis in the talent and skills component.

  • Collaborative innovation supports the AI tools ecosystem in the technology and tools component.

Technology and tools component

The technology and tools component specifies AI technologies and tools to support transformed ADM processes. This component identifies integration points and data requirements for AI-powered ADM.

  • Data infrastructure supports business impact reporting in the performance measurement component.

  • Legacy system integration affects AI-enhanced SDLC in the processes component.

  • AI platforms selection influences collaborative innovation in the partner ecosystem component.

Processes component

The processes component redesigns the SDLC to incorporate AI, enhancing each stage with AI capabilities. This component develops new processes for AI model management and governance in development.

  • AI-enhanced SDLC affects continuous monitoring in the performance measurement component.

  • AI model management relates to data infrastructure in the technology and tools component.

  • Governance workflows supports the data privacy framework in the governance and ethics component.