Models are becoming systems
A useful AI architecture depends on more than model choice: governed data, defined roles, human oversight, permissions, evaluation, and workflows all shape the deployed system. This is operating-design guidance, not a claim that one architecture fits every organization.
Smaller can be appropriate
Research and product releases show that smaller or specialized models can be competitive on selected tasks. That makes them candidates for bounded workloads, but speed, cost, and governance advantages should be measured in the actual deployment context rather than assumed.
Design the operating model
Define owners, roles, feedback, evaluation criteria, human-oversight boundaries, and a controlled path from experiment to wider use. NIST risk-management guidance treats these governance and oversight choices as part of the AI lifecycle.