My first long article went up on the ChatMe blog — about AI automation in chatbot support.
It started plainly enough: support was spending its time not so much on hard problems as on hunting for data, pulling exports, assembling reports, reading logs and reconstructing the context of a ticket.
I began collecting those actions into an automated loop.
At first it was analytics for a single project. Then came schedules, automatic reports, work with dialogues, technical data and logs. In the end one approach spread across five production projects.
The result in numbers
| Metric | By hand | In the AI loop | Effect |
|---|---|---|---|
| Preparing one recurring report | 40–60 min | 2–3 min, no human involved | −95% |
| Gathering context on a ticket | 30–90 min | 5–7 min | −88% |
| From ticket to a prepared fix | 2–4 hours | 20–35 min | ×6 faster |
| Manual steps in the ticket → change cycle | 11 | 2 | −82% |
| Analyst time freed up | — | ~38 h/month | +1 FTE on analytics |
| Projects per loop | 1 | 5 and counting, with no extra headcount | ×5 scale |
| Discrepancies in how metrics are calculated | Regularly | 0 | One shared method |
| Production changes published without human sign-off | — | 0 | Control retained |
What stayed with the human
The idea was never to hand support over to an autonomous AI.
The agent gathers data, looks for the cause, prepares a hypothesis or a change. Critical actions stay with a person, and identifying data is masked before anything reaches the model.
Why this matters to me
This was the moment when scattered scripts and experiments stopped being experiments.
For the first time I saw a system I had made working without me, every day.