Useful AI starts with an understood task, reliable context, and a human review path.
Describe the decision, connect the right source information, and decide when a person must review the result.
The result is a smaller, more testable AI use case that teams can improve over time.
An AI feature earns its place when people can understand, use, and correct it in their normal work.
Look at where information enters, where judgment is needed, and where delays happen. This shows whether AI is useful at all.
Look at the handoffs, information sources, and judgment calls in the current process before choosing a model. Some tasks benefit from search, a better form, or a simple rule. AI becomes useful when it can help with ambiguity, synthesis, or drafting while staying inside a clearly owned workflow.
Fresh, relevant, permission-aware information matters as much as model choice.
Context needs an owner. Teams should know which sources are trusted, how fresh they are, and who can access them. Retrieval should respect permissions and show enough source detail for a person to check an answer. Without that foundation, even a capable model can produce polished but unreliable output.
Corrections and escalations are valuable signals. Capture them so the system becomes more dependable.
Make review easy enough that people actually do it. Editing a suggestion, marking it unhelpful, or escalating a case should take less effort than working around the system. Analyze those signals regularly and separate model problems from missing context or unclear product rules.
