Business layer of an ADM operating model
The business layer forms the strategic foundation of the ADM operating model. Generative AI is transforming business strategy, stakeholder roles, and key areas such as enterprise architecture, reporting, governance, and budgeting.
Strategy and key stakeholders
The ADM operating model includes both internal and external stakeholders focused on aligning business strategy and goals with organizational operations and outcomes. Traditionally, these stakeholders prioritized application reliability, release velocity, operational efficiency, cost reduction, and application rationalization.
In a shift from traditional methods to AI-enhanced processes, the following key changes occur in stakeholder roles and priorities:
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Strategic focus – Shift from cost management to value creation and innovation.
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Collaborative decision-making – AI-driven insights inform cross-functional strategies.
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Agile responsiveness – Faster adaptation to market changes and user needs.
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Customer-centric approach – Enhanced focus on user experience and satisfaction.
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Continuous learning – Emphasis on AI literacy and ongoing skill development.
These changes ripple through various aspects of the business and service integration layers, affecting the following key areas:
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Enterprise and IT architecture
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Dashboards and reporting
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Governance, risk, and compliance
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Budgeting and forecasting
Enterprise and IT architecture
The following table provides the current state and a corresponding future state with generative AI for key issues related to enterprise and IT architecture.
Current state |
Future state with generative AI |
|---|---|
Manual creation and updating of architecture documentation |
Automated architecture documentation and reviews |
Static impact analysis of architectural changes |
Real-time impact analysis of architectural changes |
Fixed roadmaps with infrequent updates |
Adaptive roadmaps responding to market changes |
Technical jargon-heavy communication of architectural concepts |
AI-powered natural language interfaces for architectural concepts |
Dashboards and reporting
The following table provides the current state and a corresponding future state with generative AI for key issues related to dashboards and reporting.
Current state |
Future state with generative AI |
|---|---|
Static dashboards with generic insights |
Real-time adaptive dashboards with user-specific insights |
Reactive issue management |
Predictive analytics for addressing issues proactively |
Technical query languages for data access |
Natural language querying for non-technical stakeholders |
Manual report generation and key performance indicator (KPI) tracking |
Automated report generation and intelligent KPI suggestions |
Governance, risk, and compliance
The following table provides the current state and a corresponding future state with generative AI for key issues related to governance, risk, and compliance.
Current state |
Future state with generative AI |
|---|---|
Manual policy checking and compliance audits |
Automated policy checking and compliance monitoring |
Periodic risk assessments based on historical data |
Intelligent risk assessment with early warnings and mitigation strategies |
Static compliance documentation |
Dynamic compliance documentation generation and updates |
Budgeting and forecasting
The following table provides the current state and a corresponding future state with generative AI for key issues related to budgeting and forecasting.
Current state |
Future state with generative AI |
|---|---|
Historical data-based manual cost modeling |
Predictive cost modeling based on historical data |
Periodic resource allocation adjustments |
Dynamic resource allocation in real time |
Limited scenario planning due to time constraints |
Automated scenario planning for budget evaluations |
Subjective project prioritization |
Intelligent project prioritization aligned with business objectives |
