AI is easier to evaluate when the task has a visible input and a result someone can check. The following are illustrative operational examples, not claims about HATT customer deployments.
Triage incoming requests
Input: an incoming message and a small, documented set of request categories. The model proposes a category and highlights the words supporting that choice. An operator confirms the category before the request reaches a queue.
Test ambiguous messages, requests containing several topics and messages that fit no category. Provide an unclassified route instead of forcing a confident-looking answer. Measure corrections against a manually reviewed sample before expanding routing automation.
Extract fields from documents
Input: a document plus a definition of the fields needed downstream. Output: proposed values with references to their source passages. The receiving workflow should validate required fields and allowed formats independently of the model.
A missing purchase reference must remain missing; it must not be guessed from an unrelated number. Have a person verify uncertain fields against the original. If a stable export or deterministic parser already provides the data, compare that simpler option first.
Summarise an exception queue
Input: a bounded collection of open issues, their timestamps and recorded actions. Output: a draft summary grouping the issues and linking each statement to its records. A responsible operator reviews it before sharing it.
A useful summary distinguishes an action already taken from one merely proposed. It should not infer that an issue is resolved because nobody has commented recently. Keep the source records accessible so readers can inspect the details.
Decide what the model may do
Start with suggestions rather than irreversible actions. Define which data may enter the system, who reviews an output and what happens when the service is unavailable. A generated recommendation should not quietly become an approval or a change to a financial record.
Compare the proposed workflow with the current task using the same examples. Record review effort, corrections and missed cases. A faster draft is only useful if checking it does not introduce more work or hide errors. The broader AI workflow decision guide helps define these boundaries.