The problem. A multi-step approval or intake process — a work order, a change request, a vendor onboarding — is spread across email, spreadsheets, and manual handoffs between departments. No single person can see where a given request actually is at any moment, so status updates mean someone stopping their work to go find out. Requests stall in someone's inbox, not because the work is hard, but because nobody was tracking it.
The solution. An AI agent is built to handle the repetitive steps of the process end-to-end: collecting the required information, routing it to the right department, checking it against standard criteria, and moving it to the next step automatically when nothing requires a judgment call. When something does require a judgment call — an exception, a borderline case, a decision with real consequences — the agent routes it to a person with the context already assembled, rather than making the call itself. Status is visible at every stage, so nobody has to ask where a request stands.
The outcome. Requests move through the routine steps without waiting on a person to notice and act on them. Staff spend their time on the judgment calls the process actually needs them for, not on chasing status or manually pushing paperwork from one stage to the next. Stalled requests become visible instead of invisible.
Manufacturing and engineering firm Jabil used AI agents built on Amazon's Quick Suite platform to automate accounts collection and request-for-quote submissions — two workflows that previously required significant manual handling. Per Computerworld's reporting, Jabil's CIO credited the automation with roughly $400,000 in annual savings.
