u/Suspicious_Orchid770

Ask developers first, then remove friction.

The dashboard was right!

Same tools. Different rules.

"Eight months into building an internal machine learning (ML) platform, I asked three senior engineers to show me how they deployed a model. None of them used the platform."

"It was in May when one of our staff engineers tossed out a number in our Slack channel that put an end to an internal debate I’d been having with myself. Roughly only 25% of the runs on our merge train were completing successfully. A majority were failing due to flaky tests or got canceled since another run that was ahead of them failed."

https://leaddev.com/ai/you-bought-the-ai-tool-are-your-engineers-using-it

LeadDev’s AI Impact Report 2026 found that buying an AI tool doesn’t mean engineers will make it part of their daily workflow.

As AI pulls content from anywhere and everywhere, and when success with AI relies on unified, accurate data, organizations struggle to decide who is in charge of that data.

A lot of engineering leaders I talk to are having some version of the same experience right now. The AI tooling is working, and it’s exciting. Teams are shipping things in days that used to take weeks. At the same time, everything feels like it’s falling apart – more bugs than they’ve ever had, more incidents, and engineers who are busier and more stressed.

If that’s you, it might honestly be more of an arithmetic problem than a quality one.

AI-coding tools have swept through organizations because of their speed: you type in a prompt, and it spits out code far faster than a human ever could. However, a year-long study of 3.52 million changes inside a large unnamed technology company suggests that saving time at the keyboard can create costs elsewhere.