What 'Done' Really Means: The Definition That Changes Everything
A static Definition of Done produces static results. An evolving one creates productive crises that force teams to improve sustainably.
What Happens After Your Two-Year Agile Transformation (And Why Lasting Change Takes Longer Than You Think)
Two years of agile transformation delivers the structure. Lasting change requires building the capability to keep improving. Here’s the difference.
From Scope Arguments to Conversations: What Sprint Planning Should Actually Achieve
When sprint planning focuses on scope instead of value, mid-sprint becomes scope arguments. Here’s what planning should actually achieve.
Momentum Lag: When "We're Doing Scrum" Becomes the Ceiling
Momentum Lag occurs when framework adoption is treated as destination rather than foundation, causing productivity gains to erode without continued systematic improvement.
Value Lag: When Process Conformance Replaces Problem Solving
Over the past two weeks, I’ve explored patterns that signal agile transformations have plateaued. Week 1 introduc…
Designing a Communication Infrastructure That Reduces Friction
How to reduce communication friction with availability windows, distributed decisions, and distinguishing real constraints from behavioral patterns you’re compensating for.
Communication Friction: Are Your User Stories Growing?
When user stories grow from cards to paragraphs, you don’t have better requirements—you have communication friction you’re compensating for with text.
The Crisis Management Addiction: Why Your Team Prefers Chaos to Planning
Why teams prefer crisis work to planning, the neurochemistry of firefighting, and how to break the addiction through disciplined leverage points.
The AI Velocity Trap: When 'Shipping Faster' Actually Means Delivering Less Value
How AI coding assistants create false productivity gains while teams unknowingly sacrifice code quality and business value.
How to maintain quality and velocity when AI assistants become part of your development workflow
Why development fundamentals become critical when AI assistants join your workflow – and how to avoid common integration pitfalls.