There is useful work between receiving an unstructured request and making a business decision. Someone must read the request, find relevant information, identify missing details, and prepare the next step. An AI-assisted workflow can help with that preparation while keeping the final decision with a person who understands the situation.
The design challenge is to make review meaningful. A button labeled “Approve” adds little if the reviewer cannot see the source, understand the proposed action, or correct the result. Good automation prepares a decision clearly enough that a person can take responsibility for it.
Choose a task with a visible result
Start with a bounded activity such as categorizing incoming inquiries, extracting fields from a document, or drafting a response from approved reference material. Define what the output should contain and what the system must leave unresolved. Broad instructions to “handle customer service” are difficult to test because the boundaries are unclear.
Imagine a hypothetical equipment maintenance company receiving service requests by email. A first workflow could identify the equipment reference, reported symptom, customer contact, and requested appointment window. It could prepare a ticket for review. It would not independently promise a technician, approve warranty coverage, or send a repair estimate.
The NIST AI Risk Management Framework treats trustworthiness as a consideration across system design, development, use, and evaluation. For a business workflow, a practical implication is to define responsibility and acceptable behavior before a pilot becomes part of daily operations.
Give the system only the context it needs
Identify which information the task requires and which users are allowed to access it. An assistant helping with one customer's request should not gain unrestricted access to every customer's records. Apply authorization when retrieving information, rather than relying on a prompt to tell the model which records it ought to ignore.
Review the provider's data handling terms and configure retention and logging deliberately. Avoid sending entire mailboxes or document collections when a few relevant fields will do. The audit trail should support investigation without becoming another uncontrolled copy of sensitive information.
Incoming text is also untrusted input. OWASP's prompt injection guidance describes how instructions inside user content or retrieved material can redirect model behavior. An attached document should be treated as something to analyze, not as authority to change permissions or carry out unrelated actions.
Build a review screen that supports judgment
In the maintenance example, the reviewer should see the original request beside the proposed ticket. Each extracted field should have an understandable source. Missing information should remain visibly missing. A fluent sentence is not a substitute for evidence that an equipment identifier or appointment time is correct.
Make the next action explicit. “Create a draft ticket” and “send an appointment confirmation” have different consequences. The interface should show the exact recipient, material details, and action being approved. If those details change after approval, require a fresh review of the changed action.
A useful sequence is:
- Retrieve the permitted source material and preserve its reference.
- Produce a bounded proposal in a predictable format.
- Check required fields and business rules with ordinary application code.
- Show the proposal and supporting material to an authorized reviewer.
- Execute only the approved action and record its outcome.
Keep the authority to execute in the application layer. The model can propose a ticket category; the surrounding software should decide whether that category exists and whether the reviewer can apply it. Give the workflow a clear stop state when a dependency fails or the evidence is insufficient.
Measure corrections before adding responsibility
Build an evaluation set from representative, appropriately handled examples. Include incomplete requests, contradictory details, unusual formatting, and content that attempts to instruct the assistant. Compare outputs with decisions established by knowledgeable staff. A model's confidence statement is not a reliable substitute for checking accuracy.
During a pilot, record the corrections reviewers make and the time required to resolve them. Track repeated failure types. If reviewers regularly rewrite the result, investigate whether the task needs better source material, clearer boundaries, or a different approach. The total review effort belongs in the value calculation.
Keep a manual route available and make pausing the workflow straightforward. Changes to prompts, integrations, or reference material should trigger relevant evaluation again. Reliable assistance comes from maintaining the whole process, including its limits.
Choose one recurring preparation task your team understands well, and define what a reviewer must see before acting. Nerd Works workflow automation services can help turn that task into a controlled pilot with a visible result, a named owner, and an evaluation method grounded in the work itself.