This week had a clear throughline: AI doing the tedious, time-consuming work that engineers hate but can't skip — and the numbers were actually surprising.
Midway through the week, a service I was responsible for had a module with 23 functions, zero unit tests, and a refactor deadline approaching fast. Historically, writing that test suite from scratch would eat up most of a day — probably 5–6 hours of mechanical, low-satisfaction work.
Using an AI-assisted workflow to generate the test scaffolding function by function, I had a working first draft of the full suite in under 90 minutes. That's not a rounding error. That's the difference between shipping the refactor this sprint and punting it to the next one. The output wasn't perfect — about 20% of the generated tests needed meaningful corrections, mostly around edge cases and mock setup. But starting from a structured draft instead of a blank file changed the whole shape of the work. I was reviewing and refining, not grinding.
The second major theme was debugging. A gnarly stack trace came in from a service in a part of the codebase I hadn't touched in months. The kind of error where you'd normally spend 45 minutes just reorienting yourself before you even start diagnosing.
Feeding the trace to an AI model with the right context got me to the probable root cause in one structured pass. Not magic — it still took judgment to confirm the fix — but the triage phase that usually burns the most time was compressed dramatically.
These weren't toy examples or tutorials. Both were real work, under real time pressure. The pattern that held across both:
The engineers who are winning with AI right now aren't the ones with the cleverest prompts. They're the ones who've figured out exactly which part of their workflow to hand off.
I break down one workflow like this every week in The AI Leverage Weekly — practical, no fluff, free. Subscribe: https://theaileverageweekly.beehiiv.com/subscribe?utm_source=devto&utm_medium=article&utm_campaign=roundup_w7