Running a charging network means handling a steady stream of alerts, failed sessions and data obligations across chargers from different manufacturers. Automation and AI assistance can take a real share of that work, but only where the data is sound and a person stays accountable. This article describes what changes in day-to-day operations, and what does not.
What operations actually look like
A charge point operator spends its time on a short list of tasks: noticing that a charger is unhealthy, deciding whether it can be fixed remotely, dispatching a technician when it cannot, adjusting load on constrained sites, and publishing accurate data. The AFIR makes part of this explicit. Public points must be digitally connected, meaning they can be monitored and controlled remotely, and operators must publish operational status and availability as dynamic data (Regulation (EU) 2023/1804, art. 5(7) and art. 20).
Those obligations are about the quality of your operational data. They do not ask for automation, but they make it far easier to justify.
Where automation helps today
Not everything needs AI. Much of the value comes from plain rules and good supervision:
Where AI assistance adds something
Machine learning earns its place where patterns are hard to write as rules: spotting a charger whose behaviour drifts before it fails, ranking open tickets by likely cause, or summarising a long fault history for the technician who goes on site. In each case the output is a recommendation that a person checks.
Two cautions apply. First, a prediction is only as good as the history behind it, and that history depends on consistent data from every charger, whatever its OCPP version. Second, availability figures can mislead: a report on charging reliability published by a maintenance provider found that charger uptime of around 99 % coexisted with a session success rate of 71 %, so uptime alone does not tell you whether drivers can charge (FreightWaves). The source has a commercial interest and covers the United States, but the lesson carries: measure successful sessions, not only connectivity.
What it does not change
Grid and energy data
The RED III directive requires grid operators to make the renewable share and greenhouse gas content of electricity available by delivery area, at intervals of at most one hour (Directive (EU) 2023/2413). Such a signal can feed charging schedules, and it is a good example of automation that rests on an official data source rather than on a forecast you built yourself. Check how, and when, your national grid operators publish it, since the national transposition status was not verified for this article.
A prudent way to start
1. Pick one repetitive task, such as alert grouping or availability reporting, and automate it with rules first.
2. Keep a human approval step for any action that changes a price or a power limit on a live site.
3. Log what the system recommends and what the team decides, so you can see whether the recommendations are right.
4. Fix data quality before adding models: consistent identifiers, comparable error codes, complete session records.
Conclusion
This week, take your last month of tickets and sort them into those that were resolved remotely and those that needed a visit. That split tells you where automation pays and where it does not. GFO Omni is a CSMS that supervises charge points over OCPP 1.6, 2.0.1 and 2.1.