Reply emails, standard texts, meeting minutes, summaries of long documents: this kind of writing often absorbs 20–30% of working time. Not one big time sink, but many small tasks that add up over the week, and that almost always follow the same pattern.
That pattern is exactly what makes them automatable. AI assistants and no-code tools produce the draft; your team reviews and approves. The work shifts from writing to checking, and checking is much faster.
Where it pays off
Good candidates are texts that recur and follow a recognizable scheme: the reply to the same customer question, minutes built from meeting notes, the management summary of a report. Poor candidates are anything that requires judgment. Making decisions, weighing unclear cases, solving real customer problems: AI cannot do that, and I say so up front. For a broader assessment, see the article AI in the Mittelstand: where it really helps.
Prerequisite: clear rules
An assistant can only write what can be described. If nobody can say what a good answer looks like, which details belong in the minutes, or when a case goes to a human, no useful automation will emerge. So the first step is making those rules explicit. Only then is the tool chosen, judged by cost, integration, security and maintainability.
How I work
As with every AI automation case, we calculate first whether the case pays off; sometimes the answer is no, and that is a result too. If it does, the build proceeds step by step with real texts from your daily work, followed by training and handover to your team. I use these tools myself, most recently while building the parking app myPlace Parking AI-assisted. And if your routine work is documents rather than texts, data processing & document capture is the better entry point.