Customer-facing
Support assistants that handle common questions, triage tickets by topic and urgency, then hand off the hard ones to a human with the conversation already summarised. Sales assistants that qualify inbound leads, answer product questions from a spec sheet, and draft follow-ups a rep edits before sending. In retail and travel, the same pattern shows up as order-status and booking help. These work when you have a body of past conversations to learn from and a clear escalation path for when the model is unsure — and they fail when the assistant is told to answer everything and never say "I'll get a person."
Operations and back-office
This is where most of the quiet money is. Document processing pulls fields from invoices, contracts, claims and onboarding forms so a person checks rather than retypes. Internal knowledge assistants answer staff questions — HR policy, IT runbooks, product specs — from your own documents instead of a buried wiki. Add drafting of routine replies, meeting notes and first-pass reports. None of it is glamorous. But it removes hours of repetitive work a week per team, and it's usually the lowest-risk place to start because a human stays in the loop on every output.
Data and decisions
Forecasting demand, detecting fraud or anomalies, scoring leads or risk, and prioritising a queue — the cases where there's enough history for the patterns to be real rather than wishful. A bank flags an odd transaction; a warehouse predicts next month's stock; a lender ranks applications. These need clean, labelled data and honest evaluation against a holdout set, not a demo on cherry-picked rows. The upside is large, and so is the cost of shipping a model nobody measured.
Where AI projects actually go wrong
The model is rarely the problem. Projects stall on the boring layer around it. The data is scattered across systems, half of it stale, and nobody owns it — so the assistant answers confidently from the wrong source. The pilot works in a notebook and then has nowhere to run, because deployment, access control and monitoring were never scoped. And no one decided what the AI is allowed to do on its own versus what a human signs off. So pick a use case where the data already exists and someone owns it. That single filter kills more bad projects than any model choice.
How to tell a use case is working
Decide the number before you build, then watch it. For a support assistant it's deflection rate and how often a human had to step in — a tool that escalates everything saved nothing. For document processing it's the share of documents that clear without a correction, and the error rate on the ones that don't. For a forecast it's accuracy against what actually happened, not against the test set you tuned on. My rule: if you can't say in one sentence what would prove the thing is working, it isn't ready to build yet.