The organization should identify and document all necessary resources for the successful design, development, deployment, and monitoring of AI systems across their entire lifecycle, including the retirement phase.
This includes human resources (e.g., personnel with specific AI expertise), AI system components, data resources (i.e. data used at any stage in the AI system life cycle), tooling resources (e.g. AI algorithms, models or tools), system and computing resources (e.g. hardware to develop and run AI models, storage for data and tooling resources), and data assets. Furthermore, resources needed for any other AI-related organizational activities, such as AI governance, risk management, or ethical reviews, should also be clearly documented.