<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Developer Productivity on schristoph.online</title><link>https://schristoph.online/tags/developer-productivity/</link><description>Recent content in Developer Productivity on schristoph.online</description><generator>Hugo</generator><language>en-us</language><copyright>Stefan Christoph. All rights reserved.</copyright><lastBuildDate>Sat, 02 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://schristoph.online/tags/developer-productivity/index.xml" rel="self" type="application/rss+xml"/><item><title>The Post-Agile Operating Model: How AI Changes How Teams Ship</title><link>https://schristoph.online/blog/post-agile-operating-model/</link><pubDate>Wed, 13 May 2026 00:00:00 +0000</pubDate><guid>https://schristoph.online/blog/post-agile-operating-model/</guid><description>&lt;h2 id="the-7x-gap">The 7x Gap&lt;/h2>
&lt;p>Last month at the AI Engineer Summit, McKinsey presented findings from a survey of roughly 300 enterprises. The headline number was sobering: most organizations see only 5–15% productivity gains from AI coding tools. That&amp;rsquo;s it. After the licenses, the hackathons, the executive memos about &amp;ldquo;AI transformation.&amp;rdquo; A rounding error.&lt;/p>
&lt;p>But buried in the same data was a different story. Top performers weren&amp;rsquo;t just doing slightly better. They were 7x more likely to have AI-native workflows spanning the entire development lifecycle, and 6x more likely to have restructured their teams around new roles. Their time to market improved 5–6x. One bank case study showed a 51% increase in code merges and a 60x increase in agent consumption after restructuring.&lt;/p></description></item></channel></rss>