If you’ve followed me for a while, you’ll know how often I say this – especially since the rise of AI-assisted coding. Here’s an example.

This is a “cumulative flow diagram” from Jira for a typical software delivery team. It shows only about 30% of the time is spent in the value-creation stage (Development). The other 70% is taken up by non-value-adding activities – work sitting idle in queues or labour and time-intensive manual inspection steps such as code reviews and manual feature and regression testing.

This pattern is the norm. It’s not even a bad case (at least this team look to be shipping to prod once or twice a week).

AI-assisted coding may speed up the Development bit, but it will either push work out of Development faster or increase the amount of work in Development. In both cases it increases arrival rates into downstream stages which – in this or similar scenarios – leads to longer wait times, bigger queues, and slower overall delivery.

The solution is Continuous Delivery – building in quality and automation so you can reliably ship to production in tiny chunks, daily or even multiple times a day – one feature, one bug at a time – without having to batch work because testing and deployment take so long.

If your chart looks anything like this, you need to turn the value-creation ratio on its head – otherwise you’ll see negative returns from AI-assisted coding, not gains