AI Operations
Choosing AI models for business workflows: a practical evaluation template
A repeatable evaluation method for deciding which AI model should power support, research, marketing, planning, and engineering workflows.
Build the task set from real work
A generic benchmark can help with orientation, but production choice should come from real work. Collect representative prompts, documents, user questions, product constraints, and bad examples.
Include tasks where the right answer is to refuse, ask for clarification, cite uncertainty, or escalate to a human.
Score operations, not only answers
Teams often score the generated answer and ignore the cost of using it. A business workflow should measure edit time, review effort, latency, failure mode, handoff quality, and how easily the system can be debugged.
A slightly weaker first answer can be better if it is easier to constrain, explain, and monitor.
Keep the decision reversible
A model adapter, provider-neutral test cases, and clean logging make it easier to revisit model choice as providers improve.
The goal is not to switch models every week. The goal is to avoid locking the business into an expensive or poorly fitting workflow because the first prototype worked once.