Retrospectives
Regular reflection on how work is done to turn experience into learning and small, compounding improvements.
What is this practice?
A retrospective is a recurring practice where a team reflects on how work has been going and identifies small changes to improve how they operate.
The focus is on patterns, constraints, and system behavior—what helped, what hindered, and what should change next.
Why does this matter in this transformation?
Migration work surfaces new friction: dependencies, unclear ownership, and changing assumptions. Without reflection, those frictions repeat and harden into habits.
Retrospectives support the transformation by creating a steady learning loop that turns experience into adjustments in process, coordination, and technical practice.
What does “good” look like?
When retrospectives are working well, they feel safe and practical. Teams surface real issues without blame and leave with a small number of concrete experiments or changes they actually follow through on.
Over time, the team becomes better at noticing patterns earlier and adjusting before problems compound.
What gets in the way?
Common challenges include turning retrospectives into complaint sessions, skipping follow-through, using them to evaluate individuals, or running them only when things are on fire.
Teams may also overreach—trying to fix everything at once—instead of choosing small, actionable improvements.
How might someone begin?
Teams often begin by setting a simple cadence (for example, every two weeks), asking a few consistent questions, and selecting one small experiment to try before the next retro.
Starting small, tracking follow-through, and protecting psychological safety tends to matter more than the specific format used.
Explore deeper with your AI assistant
Use your AI assistant to reason through this practice in your own context.
Prompt:
I’m exploring the practice of retrospectives in the context of a cloud migration and broader organizational change.
Help me reason through this practice by:
- explaining it in plain language without assuming specific tools or frameworks
- highlighting the tradeoffs and tensions it introduces
- describing what “good” tends to look like in real teams
- calling out common failure modes or misunderstandings
- suggesting small, low-risk ways teams often begin experimenting
- articulating who are the vendor-neutral thought leaders in the space
Please keep the discussion exploratory and context-aware rather than prescriptive.