You get an initial draft and then keep generating new versions. At some point, it is no longer clear which version is needed. Instead of collecting as many variations as possible, define the role of AI support in the process and a sensible stopping point.
Your goal for this article
You describe the input, a limited generation step, review, and completion for a specific preparation task.
Understand: Put the work question in the right context
Generative AI can create new content; the BSI describes both potential applications and IT security risks. Generating more variations is not general evidence of higher quality or less effort.
The additional work involved in preparation, review, and adoption must suit the task. A short generation time alone does not tell you whether the overall process is useful. This is a practical assessment, not a substantiated productivity claim.
Your own example: Shorten a public information text
For an internal exercise, you want to prepare an approved public information text in a shorter form. First, determine the required length and which statements must be retained. The AI provides an initial suggestion, which you compare with the source text.
If a specific section does not work, you can revise that part or correct it yourself. Another complete draft is not automatically necessary. After sufficient review, a definitive working version is adopted; the next variation is not created merely because it is easy to produce.
Decide: Generate again or end the step?
A specific requirement is still unmet
Describe the discrepancy and choose a targeted revision. Check again that the necessary content has been retained.
The result meets the limited purpose
End the round of variations and take over the reviewed version in the designated place. More alternatives are not always necessary.
The review effort is disproportionate or approval is missing
End the trial and choose another suitable process. An AI step does not have to be retained at all costs.
Describe a process with a clear outcome
Note which approved input goes into the step and what limited output is needed. Decide how the output will be checked and who will take over the sufficiently reviewed version.
After a suitable trial, compare the total effort with the previous approach. Include preparation, follow-up work, and coordination. Do not use information that has not been approved just to make a test seem more realistic.
Your limited AI process
Input: What source material is approved for use?
Output: What specific draft is needed?
Review: How will it be checked, and what follow-up work remains?
Stop: When does the step end or continue without AI?
Next: Check the agreement in practice
A clear stopping point is just as important as clear input. If the trial requires more maintenance and review than the original task, you can choose the simpler solution without AI.
Limitations and support
This article offers general guidance for work, not a complete risk analysis or organizational approval. Use only tools and inputs approved for the task. Keep login credentials and confidential information that has not been approved out of the input. Review, adoption, and any subsequent actions have their own responsibilities. If the prerequisites are unclear, pause use and clarify with the responsible parties first.
Frequently asked questions
Are multiple drafts inherently bad?
No. They can serve a purpose. However, the number should be determined by the task, not by unlimited availability.
Is a shorter draft automatically better?
No. The scope and necessary content must suit the purpose.
Does AI need to remain part of every process permanently?
No. If there is no benefit, approval, or appropriate oversight, another process makes sense.
Expert source and context
BSI: Artificial Intelligence – Generative AI section →
accessed on October 16, 2026. German-language source.
The BSI overview page describes generative models and security risks. The process with a stopping point is a practical template, not a proven method for guaranteed time savings.
The practical example and template are practical interpretations, not a scientifically validated program.
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