Practical AI
Build an AI Workflow That Gets the Work Finished
A practical way to design the handoffs, checks, and completion evidence that turn an AI draft into useful business work.
Consider a hypothetical customer request for an updated product specification. AI produces a polished reply in seconds. But the attachment is an old revision, the customer’s question about case quantity is unanswered, and nobody knows whether the reply was sent. The writing task is finished. The customer’s request remains open.
This is a useful place to start when putting AI into a small or midsize business. Look at the complete piece of work: what arrives, what must be checked, who can decide, and what proves the request was handled. A faster step is valuable only if the surrounding process can use it.
Choose a finish line that someone else can verify
Write the finish line before designing the prompt. For a product-information request, it might be: “The customer received the current approved specification, every question has an answer or a named follow-up owner, and the account record contains the response.” That definition exposes several separate jobs.
Retrieving a document, checking its revision, drafting a response, approving an unresolved claim, sending the message, and recording the outcome each need a clear owner. AI might help with some of them. Existing software or a person may be better for others.
Use a single page to record the trigger, required inputs, output, responsible person, approval point, and evidence of completion. If two departments disagree about what “done” means, resolve that before connecting a model to the workflow.
Separate predictable checks from judgment
A program can compare revision dates, check whether required fields are present, and route a request to a known owner. An AI model can help interpret varied wording or draft an explanation. A product specialist may need to resolve conflicting specifications. Assign each part deliberately.
For example, an empty case-weight field should fail a required-field check. It should not become an invitation for the model to estimate a plausible weight. A request to promise a delivery date should reach the person or system authorized to make that commitment.
NIST’s AI Risk Management Framework core emphasizes defining the task and context, assigning responsibilities, and evaluating risk. The workflow outline here is a practical application of those ideas, not a certification or a claim that one template satisfies every organization’s obligations.
A hypothetical distributor workflow
Illustration only: Imagine a distributor receives repeated requests for product sheets. The following is a proposed workflow, not a customer deployment or measured result.
- Receive: A shared queue records the request, account reference, item identifier, and questions. Requests with an uncertain item match go to a person.
- Retrieve: The system finds the approved specification for that exact item and market. It preserves the source reference and revision.
- Prepare: AI drafts a reply using the retrieved facts. Unsupported questions are listed explicitly instead of being answered from general knowledge.
- Review: A staff member checks the attachment, recipient, statements, and outstanding questions. Pricing or delivery commitments follow their existing approval process.
- Complete: The approved response is sent through an authorized channel. The queue records the sent-message reference and any remaining follow-up.
The first trial could stop at preparation. Staff would continue sending messages themselves while the business learns whether the drafts and source selection are dependable. That keeps the experiment useful without assuming that a good draft justifies permission to send.
Test the awkward requests
Build a small test set from authorized, appropriately protected examples or synthetic requests. Include the everyday case and the cases most likely to cause trouble: two similar item numbers, a missing attachment, an obsolete specification, a request combining several products, and a customer asking for a claim the documents do not support.
Agree on the expected behavior for each case before running it. “Escalate with the conflicting revisions attached” can be the correct answer. Test whether the workflow keeps working when a source system is unavailable and whether a repeated request creates duplicate work.
Record every test, including failures. A small test set can uncover defects; passing it does not establish production reliability. Expand the evidence as the range of requests expands.
Measure the whole job
Track elapsed time from receipt to verified completion, staff review time, corrections, reopened requests, and unresolved exceptions. Keep the denominator visible: ten successful drafts out of ten attempted requests tells a different story from ten successes selected from fifty attempts.
Include setup, maintenance, software, and review costs when deciding whether to continue. Time released is useful capacity; it becomes a financial benefit only when the business can explain how that capacity is used or which expense actually changes.
Before the pilot starts
- One process owner can accept or reject the finished work.
- Approved sources are available, and confidential data handling has been reviewed.
- The system’s read, edit, and send permissions match the trial’s scope.
- A person can see failures and take over using the normal process.
- The team has agreed what evidence would justify expanding, revising, or stopping the trial.
The next useful step is to map one real request from arrival to completion. That map will often tell you more about the right AI project than a long list of possible tools.
Sources & further reading
Primary references checked October 7, 2026. The practical recommendations and hypothetical examples are Wheeler IS editorial guidance; they are not customer results or vendor endorsements. Product capabilities and requirements can change.