Honestly, after 20 years in the field: optimising the workflow for when you can already reliably reproduce the bug seems misapplied because that's the part that already takes the least amount of time and effort for most projects.
Tick tock. You need competence in depth when you have SLIs.
For more subtle bugs, where there's no hard error but something isn't doing the right thing, yes bisect might be more helpful especially if there is a known old version where the thing works, and somewhere between that and the current version it was broken.
In general, this takes human-interactive time. Maybe not much, but generally more interactive time than is required to write the bisect test script and invoke `git bisect run ...`
The fact that it's noninteractive means that you can do other work in the meantime. Once it's done you might well have more information than you'd have if you had used the same time manually reducing it interactively by trying to reduce the scope of the bug.
Before you find even that, your fire drill strategy is very very important. Is there enough detail in the incident channel and our CD system for coworkers to put their dev sandbox in the same state as production? Is there enough if a clue of what is happening for them to run speculative tests in parallel? Is the data architecture clean enough that your experiments don’t change the outcome of mine? Onboarding docs and deployment process docs, if they are tight, reduce the Amdahl’s Law effect as it applies to figuring out what the bug is and where it is. Which is I. This context also Brooks ‘s Law.