A 95% confidence interval under Neyman-Pearson is defined as the interval upon which if we took many samples of size n from the population, 95% of the intervals formed around the sample means would contain the population mean.
In the circumstance where you have knowledge of the population variance, this interval will have the same range for each sample, assuming each sample is of size n.
However, in the circumstance where you don't have knowledge of the population variance, each sample of size n will use its sample standard deviation and therefore the interval range will vary across the samples as a result.
With this in mind, I am struggling to see the material benefit, as a part of a piece of analysis, to provide a confidence interval when the population variance isn't known. It feels as though I am presenting a metric which a) requires the reader to consider an almost-abstract number of samples, b) has a range which is going to vary across those samples.
Are there any benefits to presenting a confidence interval formed using the sample variance?
Edit - to clarify - I am mainly focusing on presenting to non-statistical audiences, such as decision makers within an organisation.