# Building an AI-powered ADM target operating model
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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 driversBusiness goals alignmentAI roadmap | 
| **Organizational structure** | AI Centers of ExcellenceNew AI rolesCross-functional teams | 
| **Talent and skills** | Career pathsContinuous learningAI literacy requirementsSkills gap analysis | 
| **Governance and ethics** | Regulatory complianceData privacy frameworkAI ethics policies | 
| **Performance measurement** | Continuous monitoringBusiness impact reportingFeedback loopsAI-specific KPIs | 
| **Partner ecosystem** | Partner evaluation metricsData sharing protocolsAI capability requirementsCollaborative innovation | 
| **Technology and tools** | Data infrastructureAI tools ecosystemAI platforms selectionLegacy systems integration | 
| **Processes** | AI-enhanced SDLCAI model managementGovernance 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
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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
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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
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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
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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
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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
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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
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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
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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.