An AI workflow in a client project: Numbers from practice

Before the rebuild, article production ran the classic way. An idea was discussed in the team, one person researched and wrote a draft, a second edited it, then the text went through a subject-matter approval and finally into the content management system. Nobody had ever measured how long this took, because it never seemed necessary.

That was exactly the first step, and the most uncomfortable: for two weeks, note the pure working time per article, split by research, writing, editing and publishing. Without this before-measurement, we would have ended up pitting gut feeling against gut feeling. Anyone who wants to assess a workflow honestly cannot avoid this effort.

The new workflow did not replace the human, it shifted their work. The AI took over the first draft, and the human moved from first author to editor and reviewer.

Three building blocks carried the whole thing. First, access to the company's own knowledge, so the AI draws not from the general internet but from the client's approved content (the principle behind this is explained in our knowledge article on RAG). Second, a set of proven templates for the recurring article types, so that structure and tone did not have to be renegotiated every time; why the wording of the instruction matters so much is covered in the article on prompt engineering. Third, a fixed, non-negotiable approval step by a human before anything was published.

The third building block sounds obvious and is not. The temptation to wave through a good-looking AI draft "quickly" is real. The step was built precisely against that.

After eight weeks of parallel operation, we had enough data points for an honest comparison. The table below shows the orders of magnitude per standard article. As said, these are anonymised values from this one project and should be read as a range, not a promise.

The most important value is in the third row, and it goes up. Editing became more demanding, not easier. Anyone who leaves that out of the calculation arrives at the dream numbers from the demos. Anyone who counts it in lands at a real relief of about a third of the turnaround time. That is far less spectacular and far more reliable.

The new editing work had concrete reasons. Three types of error appeared regularly, and the review step was aimed precisely at them.

First, invented evidence. The AI occasionally produced plausible-sounding studies or figures that did not exist. Rare, but expensive every time you miss it. Every external statement therefore had to be checked against a real source.

Second, the wrong level of specificity with company knowledge. Despite access to its own content, the system now and then mixed up details from similar products. No prompt helped here, only a human who knows the product range.

Third, a smooth, slightly generic tone. The drafts read cleanly but interchangeably. The character by which readers recognise the sender had to be written back in by the human. That is not a weakness of the tool, it is a sensible division of labour.

The most important lesson is methodical: without the uncomfortable before-measurement, we would have either over- or underestimated the success, and in both cases made the wrong decision. The measured effect, about a third less turnaround time at unchanged quality, clearly justified the rebuild. The demo figure of 80 percent would have toppled it at the first disappointment.

In terms of content, what we also see in other projects was confirmed, and described in more detail in the article on AI content workflows in Drupal: the AI is strong in drafting and weak in responsibility. It shifts work from the front to the back, from writing to checking. Anyone who plans for this shifted effort and obliges a human to approve gets a solid relief. Anyone who saves it away gets fast text and a slow trust problem.

Before you talk about tools, measure the pure working time of a typical content process for two weeks, split by phase. These baseline values are the foundation for every later decision, and they cost nothing but discipline. Only then is it worth asking which phase an AI draft really addresses.

If you do not want to take this step alone, we will look at it together. A Future Check maps out where a measured use of AI genuinely holds up in your processes, and where the rework eats the advantage.