This viewpoint presents a structured representation of the strategic objectives and desired outcomes associated with the organization’s AI strategy. It illustrates how enterprise-level AI ambitions are decomposed into measurable objectives that collectively enable the institutionalization and operationalization of AI as a core business capability. The viewpoint focuses on the cause-and-effect relationships between strategic intent and operational outcomes, providing traceability from high-level AI goals to enabling delivery models, governance structures, and value realization mechanisms. At the highest level, the viewpoint identifies enterprise AI objectives such as: • Establishing an Enterprise AI Center of Excellence • Enabling trustworthy AI at scale • Operationalizing AI across value streams • Institutionalizing AI as a core enterprise capability These objectives are supported by intermediate outcomes, including repeatable AI delivery models, governed AI portfolios, production-grade AI solutions, operationalized MLOps pipelines, and improved decision quality. The viewpoint further emphasizes measurable business value and reduced model risk as critical success factors for sustainable AI adoption. By explicitly modeling objectives and outcomes, this viewpoint: • Enables alignment between AI strategy and enterprise execution • Supports prioritization of AI initiatives based on business value and risk reduction • Provides a foundation for defining AI-related capabilities, principles, and requirements • Facilitates governance, measurement, and continuous improvement of AI investments This viewpoint is primarily concerned with the strategy and motivation layers of the architecture and precedes capability-based planning, solution design, and implementation. It serves as a bridge between strategic drivers and constraints and the downstream realization of AI capabilities within business and technology architectures.