You mentioned your coworkers found shortcomings, I assume that implies the presence of bugs (be it in code, documentation, or even just more unnecessary code to maintain)
It was a matter of cost vs benefit ratio which ultimately resulted in net benefits. Stakeholders like designers and product don't see nor care that some mocks are repeated or that the sub optimal API is used in stories. Customers don't care why the application is broken, they care it is and the additional E2Es catched multiple bugs. Juniors would appreciate documentation and stories even if they might be redundant.
I think the biggest fallacy committed in evaluating the benefits of LLMs is in comparing them with the best output humans can generate.
But if those humans do not have the patience, energy or time budget to generate such output (and more often than not they don't) I think one should evaluate leveraging LLMs to lower the required effort and trying to find an acceptable sweet spot, otherwise you risk falling into Luddism.
Even as of 2025 humans outperform machines in tailoring 200 years after Luddism appeared, that doesn't change that it's thanks to machines that lifted humans out of a lot of the repetitive work that we can cloth virtually every human for pennies. That hasn't removed the need for human oversight in tailoring or that very same role behind higher quality clothes.
