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An internal assessment when implementing AI primarily focuses on the resources and capabilities that an organization has in place to support AI initiatives. Reviewing existing data infrastructure is crucial because AI systems rely heavily on quality data and robust frameworks for data management. This involves examining how data is collected, stored, and processed within the organization, ensuring that it is suitable for training AI models.

A comprehensive evaluation ensures that the organization can effectively leverage AI technologies and identifies any gaps in their current infrastructure that might hinder successful implementation. Ensuring that data systems are optimized and compatible with AI processes is a foundational step that directly affects the quality of AI outcomes. Thus, conducting an internal assessment centered on existing data infrastructure aligns with best practices for implementing AI in a business context.