Oct 09, 2026
AI has made engineering teams faster, but faster is now outpacing stable, and the gap is showing up in incidents, fragile releases, and teams that can't explain what they shipped. This piece gives a practical framework to govern AI adoption before the velocity becomes a liability.
Continue Reading→Oct 09, 2026
Nothing is more frustrating than funding a test automation project that results in endless time and resources to maintain , or worse, funding one that requires re-building after six months. Often a test automation strategy fails because the objectives were not identified early on or the scripting ...
Continue Reading→Oct 07, 2026
DORA scores look great, but production still breaks. In 2027, elite engineering leaders are closing the gap with software engineering metrics that track AI code quality, developer experience, and real business outcomes, not just deployment speed.
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This blog explains how to build a modern test automation strategy for 2026, covering prioritization, maintainability, CI/CD integration, AI-driven testing, and common pitfalls teams must avoid to improve speed, stability, and quality.
Continue Reading→Oct 07, 2026
One study says AI makes developers 56% faster. Another says 19% slower. Working closely with engineering leaders across the US and Europe, we have seen both play out. This blog unpacks what actually determines which side of that gap your organization lands on.
Continue Reading→Oct 07, 2026
AI-assisted development has removed the independence between code and the tests meant to check it. When the same AI writes both in one session, the tests confirm what the code does, not what it should do.
Continue Reading→Oct 07, 2026
This blog explores how AI-driven development is accelerating code output while weakening quality signals, highlighting risks like flaky tests and pipeline decay. It outlines how engineering leaders can reposition QA into a strategic function with guardrails, governance, and observability.
Continue Reading→Oct 07, 2026
This blog breaks down why full automation creates governance risks that engineering leaders can't ignore, and how Human-in-the-loop AI solves that by keeping human judgment at every critical decision point. It covers how a properly implemented HITL model integrates directly into your existing ...
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