> have been able to successfully track the boost.
lets get nitty gritty on this - can you say how you did this? because a lot of people think this is an unsolved problem
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For my team, it has been easy. We deal with infrastructure for the entire org, so have tickets created for every request. We also gave our own backlog for internal project, so can see burn rate, and etc. Team hasn’t changed, a lot of similar/same tasks that have taken half a day has been completely automated to a point where we just do PR review after an initial ticket is created by other teams.
There are a lot of little things we’ve tracked, and it’s just faster to implement things now. To be fair, everyone on my team has decade+ professional experience (many more non-prodessional), and we understand limitations of AI fairly well.
What kind of code is infrastructure in this context? Devops in a software company? Internal tooling in a software org?
Thank you for sharing any info at all.
> to be fair, everyone on my team has decade+ professional experience (many more non-prodessional), and we understand limitations of AI fairly well.
I see this appear quite often in discussions on productivity, to the point that a conclusion may be made regarding its centrality for productivity gains.
What is your definition of faster to implement? Is it producing a plausible implementation, or is it faster at producing a correct and high quality implementation? Are you including time spent refactoring and fixing bugs in your metrics? If not, I think you are tracking a gut feeling rather than cold hard facts. I’m not saying this is easy to track, just saying that it’s hard to know for sure that you are really more productive with AI.
Not the same person, but it really depends on projects. E.g. I have some projects that involve working to large specification sets where we can measure rate of delivery against the spec. If your spec is fuzzy and incomplete, then it gets hard, but then you have little insight into human productivity for those projects either.