Tag archive
Where AI actually changes a travel agency's workflow: the rebooking decision inside a disruption is automating. Reconciling that change across every other supplier on the trip still isn't.
AI for insurance agencies works best as a coordination layer that removes rekeying across carrier portals during submissions and renewals, not a client-facing chatbot.
A pre-build audit that checks whether a proposed AI automation will survive real change: what breaks first, how fast failures surface, and who is named to own it.
A custom AI agent needs a bounded decision surface, an escalation path, an evaluation loop, and proof a simpler rule wouldn't do the job. Here's the checklist before you build one.
AI integration means wiring a model into a workflow's data and approval points, not buying another tool. Here's the difference, with a worked example.
Most agent demos show a model booking a fake meeting. In a real business the agents that pay run inbound handling, follow-up, research, internal answers and record keeping, each with a pause list. Here are the five.
A small business does not need an AI strategy. It needs one workflow where a capable person is the bottleneck. Here are the four that pay first, what each costs to run, and how to pick.
Five AI workflows a small business can run on a low-code tool and a model API: enquiry triage, follow-up, invoice extraction, review replies and a document assistant. Each with the rule steps, the model steps and the review point.
AI automation is automation where one or more steps need a model because a rule cannot do them: reading, classifying, drafting, deciding within limits. Here is the definition, where it applies, and where plain automation is still right.
AI for business means using models to do specific steps inside a workflow: reading, drafting, classifying, deciding within limits. Here is what that covers, where it pays, and where it does not.
