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Five narrow AI projects that fit a few weeks rather than a few quarters: intake triage, a grounded support assistant, drafting, reporting and mention monitoring.
Hassan Sarwar ·
A quick win is a narrow AI project with one input, one output and one system to write to. Five of them hold up in practice: inbox and intake triage, a support assistant grounded in your own documents, first-draft generation, automated reporting, and monitoring mentions of your brand.
None of these needs a platform decision or a data warehouse. Each one can be measured against the way you do it today, which is the only honest way to tell whether it worked.
Triage is the safest first project because the model classifies rather than decides. An inbound email, form or ticket is read, given a category and a priority, and filed into the right queue with the obvious fields already populated. A person still owns the response.
Measure it by comparing the model's category against the one a human picks, on a recorded set of past items, before you let it write anything.
An assistant that answers from your policies, product documentation and records is a retrieval problem before it is a model problem. The work is getting the right passage in front of the model and refusing to answer when nothing relevant comes back, which is what stops confident invention.
Build the evaluation set first: twenty to fifty real questions with known-correct answers, scored on every change. Without it you cannot tell a model upgrade from a regression.
Drafting is the lowest-risk use of a model because a human reads every output before it ships. It works best where the format is fixed and the facts come from somewhere else: a summary of a call transcript, a follow-up email built from CRM fields, a description generated from a spec sheet.
The failure mode is treating output as final. Keep a person in the loop and the quality question answers itself.
If somebody on your team rebuilds the same spreadsheet every week, that is a workflow, not an analysis. The automation pulls the numbers on a schedule, applies the rules that person applies, and writes a short narrative summary of what changed and what looks unusual.
Ship the numbers and the narrative separately. The numbers must be produced by code you can audit; only the narrative comes from the model.
Monitoring is a good fit because the cost of a mistake is low and the volume is high. Collect mentions across the channels you care about, group them into themes, and alert a person when a theme spikes or the tone of a thread turns.
Keep the alert threshold conservative at the start. An alerting system people learn to ignore is worse than no alerting system.
Pick the one with the clearest baseline. Count the volume, time one instance end to end, and write down what a wrong answer costs. Then scope the smallest version that touches production, because a project that never reaches a real system never produces a real answer about whether it works.
Our assessment phase typically runs one to two weeks and exists to make exactly this choice, in writing, before anyone builds.
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