Where AI agents earn their keep in the back office

Most AI automation programs start with the process an executive can see: the customer-facing chatbot, the flashy assistant. Most of the value sits somewhere less photogenic — in accounts payable, in claims intake, in the onboarding team that reads the same forms every day and re-keys them into three systems.

Four traits of a good first candidate

• High volume of similar cases, so a modest per-case saving compounds.
• Unstructured input — PDFs, emails, scans — that currently requires a person to read and decide.
• A clear system of record to write into, so the agent's work lands somewhere useful.
• Tolerance for review: a human can check the exceptions without checking everything.

Notice what is not on the list: novelty, complexity, or a requirement that the agent be right every time. The workflows that succeed are designed so the agent handles the confident majority and routes the rest to a review queue with its reasoning attached. Straight-through rate becomes the metric; it starts modest and climbs as the model and the rules learn from the exceptions.

Design the process before the prompt

An agent dropped into a broken process automates the breakage. The discipline that matters is the one that predates AI: map the work as it actually happens, measure it, remove the steps that exist only because a person had to pass paper to another person, and then automate what remains. Prompting is the easy part.

An agent dropped into a broken process automates the breakage.

Instrument everything. Hours removed, cycle time, exception rate, time to resolve an exception — recorded from the first week, so the program justifies itself with numbers rather than anecdotes. That evidence is also what earns the mandate to do the next process, and the one after that.