Managed Evolutionary Change
summary
Argues that organizations get better by starting with what you do now and running small, managed experiments rather than big-bang transformations; uses the Kanban method’s practices — visualize workflow, make policies explicit, limit WIP — and a scientific loop: observe pain, form hypothesis, design the experiment, measure outcomes, then roll forward or back. Cites a rough industry failure rate (58% of Scrum adoptions miss time/budget or upset customers) as the reason to prefer incremental, data-driven change.
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chapters (19)
- 0:00Welcome
- 0:46What is managed evolutionary change
- 3:31Problem with revolutionary transformations
- 7:25Evolution and change management types
- 10:35Principles of managed evolutionary change
- 12:00Evolutionary change formula
- 15:57Practical example
- 18:44Kanban solutions and practices
- 22:15Resistance to change
- 27:29Assessing resistance levels
- 31:21Running experiments
- 34:54Continuous improvement models
- 36:18Kanban misconceptions
- 38:12Predictability and commitment concerns
- 41:27Flow over detailed planning
- 44:17Cultural agility and adaptation
- 46:25Small steps and local improvements
- 47:28Product-centric approaches
- 49:56Closing and Q&A
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