AI agents that take over work

In most companies a substantial share of working time goes into cases that follow a pattern: sorting enquiries, updating data, assembling quotes, answering standard questions. None of it needs experience, but all of it needs someone.

The obvious attempt was a chatbot. It answers questions as long as they are in its script and hands over as soon as things get concrete. What it cannot do is bring a case to completion. Which is exactly why many of these projects stalled after the pilot.

The difference is not the language model but the access: an agent that may read and write in the CRM can actually qualify a lead, prepare a quote, close a ticket. Without that access, any AI remains an information desk.

The calculation rests on three figures: how many cases of a type occur per month, how much time one of them costs today, and what share follows a pattern an agent covers reliably. The first two numbers usually already sit in the system; the third we estimate together on real cases.

Against that stand two costs: building the agent and running it, the latter essentially model costs per case plus monitoring. Both are predictable because they scale with volume, not with headcount.

We do this calculation before the build, not after, and if a use case does not carry itself, we say so. Percentages without your data would be guesses.