Choose a manageable dependency chain for each AI use case
The decision is about the chain, not a single model
Choose an AI use case for production only when the entire chain can be assessed: who acquires, deploys and monitors the solution; which data and external components it contains; and who decides when a change occurs. The NIST passage explicitly addresses the full product lifecycle and processes involving third-party software, hardware and data. This is not a prescription for a specific architecture, but it is a useful scope for the decision: model quality alone does not cover those dependencies. For a product team, this means that access, hosting, costs, security, tooling, maintenance and a replacement path do not end up as separate technical notes. They are part of the same use-case decision. Name the owner for each link and the point at which the team reassesses whether the solution still operates within the agreed boundaries.
Make governance a checkpoint in the workflow
The NIST passage positions governance as a function that runs throughout AI risk management and calls for compliance and evaluation to be integrated into the other functions. Translate that narrowly but concretely: add a checkpoint to the existing release or change workflow for the dependencies that affect this use case. Record what is changing, who assesses the risk and which outcome follows: continue, adapt, fall back or stop. In its Apply AI Strategy, the European Commission describes how organisations can consider AI as a potential solution in strategic or policy decisions, taking both benefits and risks into account. That passage does not support choosing a particular vendor, hosting model or open-source model. It does, however, support a decision-making approach in which expected value and risks are considered side by side.
Use a single decision register for the critical use case
Start with the use case for which an outage, changed access or a different data route would have the greatest operational impact. Then complete this register and review it with every relevant change: "For each critical AI use case, document: owner, model and tool access, data route, hosting location, cost or usage limit, security check, evaluation point, alternative, and the decision in the event of an outage or change." This does not guarantee continuity, but it does provide a visible starting point for discussing replaceability and management. "This map does not assess model performance, contractual terms, legal compliance or security measures in isolation; current testing, contract review and specialist assessment are required for that." The supplied passages also provide general frameworks for AI risk management and European strategy, not evidence about the reliability, costs or compliance of an individual implementation.
