I look through the backlog for my team consisting of 9 trillion ill-defined (if defined at all) tickets that tells you basically nothing.
The large, overwhelming majority of my team's time is spent on combing through these tickets and making sense of them. Once we know what the ticket is even trying to say, we're usually out with the solution in a few days at most, so implementation isn't the bottleneck, nowhere near.
This scenario has been the same everywhere I've ever worked, at large, old institutions as well as fresh startups.
The day I'll start worrying is when the AI is capable of following the web of people involved to translate what the vaguely phrased ticket that's been backlogged for God knows how long actually means
At my workplace we now use Claude Code to parse written specs and source code, search through JIRA, and draft, refine and organize tickets (using the JIRA API via a CLI tool). Way faster than through the UI.
However as you point out we have no program-accessible source of data on who stakeholders, contributors, managers, etc. are and have to write a lot of that ourselves. For a smaller business perhaps one could write all of that down in an accessible way to improve this but for a large dynamic business it seems very difficult.
The large, overwhelming majority of my team's time is spent on combing through these tickets and making sense of them. Once we know what the ticket is even trying to say, we're usually out with the solution in a few days at most, so implementation isn't the bottleneck, nowhere near.
This scenario has been the same everywhere I've ever worked, at large, old institutions as well as fresh startups.
The day I'll start worrying is when the AI is capable of following the web of people involved to translate what the vaguely phrased ticket that's been backlogged for God knows how long actually means