KINSMCTo triage incoming requests in monday.com, let an AI block suggest a label from the request text and put the suggestion in a separate column. A team member can then approve it or choose a different route. Start with one queue and a handful of clearly defined categories. You can learn where the suggestions help before allowing them to move work between teams.
Make your categories easy to tell apart
List the decisions someone actually makes when a request arrives. For a small online store, those might be shipping, returns, product questions and internal review. Define each category with a short description and a contrasting example. “Where is my order?” is a shipping question. “I received the wrong item and want to send it back” may belong in returns. If those cases go to different teams, decide how to handle requests that contain both. Give the reviewer an option for uncertain cases instead of forcing every item into a specific queue.
A category should answer who looks at a request first. Urgency is a separate question. A calm message about a parcel delivered to the wrong address may need more attention than an angry message about a discontinued color. Keep urgency in a separate field and decide who can set or override it. This also helps you see whether the tool is confusing emotional wording with a deadline.
Set up the board for a reversible trial
Keep the original request in one column. Add a separate suggested category and a confirmed category. You can also add a suggested summary if the source messages tend to be long. monday.com documents an Assign labels block that reads a designated field and selects a status label or dropdown labels, depending on the destination column. It also documents a Summarize block for shortening long text. Check the options available in your workspace and keep any rule that assigns ownership turned off during the first trial.
Use fictional or appropriately de-identified examples when testing. Keep only the text required to make the routing decision. For example, an order number is rarely necessary to distinguish a shipping question from a return. Review your organization’s settings and data-handling requirements before applying the workflow to an active board.
Compare suggestions with a human answer
Build a small sample with both ordinary and difficult messages. Include a short request, a long complaint, a typo, a message with two issues and an item missing enough detail to classify. Have a reviewer assign each example an expected category before looking at the suggestions. Track where the tool agrees, where it differs and where reviewers disagree with each other. The last case may point to an unclear category definition rather than an automation problem.
Read every suggested summary next to the original during the trial. Check dates, product names and whether the message reports a problem or asks a hypothetical question. “Can I return it if it arrives late?” and “It arrived late and I need to return it” require different responses. Keep the full text visible so the reviewer can catch that difference without opening a second system.
Decide what happens when a suggestion is wrong
Define who checks the queue, how they correct the label and what happens if an AI action returns no result. Have requests without a confirmed label stay visible in one place. If one category accounts for many corrections, look at a few misrouted examples and clarify the boundary. A short weekly review with the people doing the work can reveal issues a single accuracy percentage misses.
Consider limited automation only after the categories are stable and reviewers have tested the failure path. For instance, a confirmed label might move an item to the right queue while a suggested label remains advisory. Keep unusual cases and decisions that would affect a customer’s account with an authorized person. Before expanding the workflow, run the same test cases again after changing the categories or the input field.
Measure the time saved in the whole process
Compare the time it takes to read and confirm a suggestion with the time it took to triage manually. Count follow-up work for misroutes and review time alongside the initial speed. A team may find that automatic summaries help while automatic routing still needs oversight. If requests come from a storefront assistant, KINSMC’s store chatbot testing guide explains how to verify what reaches the team in the first place.