### 2.1 Integration Assessment The **all_ai_models** table operates as a self-contained catalog with 3,574 AI model records and no dependencies on external tables within this dataset. This structure is appropriate for a reference inventory but limits cross-domain analysis—there are no connections to deployment metrics, cost data, usage logs, or organizational ownership that would enable strategic decision-making. To unlock business value, this catalog should be enriched with operational and financial datasets that contextualize which models drive outcomes, consume resources, or require governance attention. ### 2.2 Recommended Actions - **Link to deployment and usage telemetry** to identify which of the 3,574 models are actively serving production workloads versus sitting dormant, enabling rationalization of licensing and infrastructure costs - **Connect to project or product hierarchies** to map models to business units, revenue streams, or customer segments, transforming the catalog from a technical inventory into a strategic asset portfolio - **Integrate vendor and licensing metadata** to track commercial dependencies, renewal dates, and compliance obligations across proprietary and open-source models - **Append performance benchmarks or evaluation scores** from MLOps platforms to support model selection, retirement decisions, and risk assessments based on accuracy, bias, or drift metrics