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2. Kaizen Logs

Purpose

Continuous log for small, targeted improvements to the AI system so that changes are traceable and repeatable.

1. Objective

We record every small, targeted improvement to the AI system in a continuous Kaizen Log so that improvements are traceable, repeatable and aggregately visible.

2. Entry Criteria

  • The system is in production and actively in use.
  • The retrospective cadence is operational.
  • A shared document or backlog is available for the team.

3. Core Activities

Recording a Kaizen Entry

Every improvement — however small — is logged with a fixed structure:

Field Description
ID Unique sequence number (e.g. KZ-2026-001)
Date Date on which the problem was identified
Owner Who is responsible for implementation?
Problem What is not working well or could be better? (max 2 sentences)
Measure What is the concrete improvement?
Result What is the measured effect after implementation?
Status Open / In progress / Closed

Example:

KZ-2026-007 · 15-03-2026 · Data Scientist · Accuracy in category X drops structurally 3% per month. · Supplement Golden Set with 20 new edge cases and retrain. · Accuracy restored to baseline +1.2%. · Closed.

Monitoring the Kaizen Cycle

  • Weekly: Discuss status of open entries in the stand-up.
  • Monthly: Overview of closed entries and measured effects to the team.
  • Quarterly: Aggregated Kaizen analysis as input for the Model Retrospective.

Distinction Kaizen Log vs. Incident Log

Kaizen Log Incident Log
Proactive improvements Reactive outages and incidents
Focused on quality improvement Focused on recovery and root cause
No time pressure SLO-bound response times
Owner: AI PM / Data Scientist Owner: MLOps Engineer

4. Team & Roles

Role Responsibility R/A/C/I
AI Product Manager Manages the Kaizen Log, prioritises entries A
Data Scientist Records and analyses model-related improvements R
MLOps Engineer Records infrastructure and pipeline improvements R
Guardian Assesses whether improvements affect Hard Boundaries C

5. Exit Criteria

  • All open entries older than 30 days have a status update or have been escalated.
  • Monthly overview has been shared with the team.
  • Quarterly analysis has been included in the Model Health Report.

6. Deliverables

Deliverable Description Owner
Kaizen Log Living overview of all improvements AI PM
Monthly overview Summary of closed entries and effects AI PM
Quarterly analysis Aggregated insight into improvement trends Data Scientist

Related modules:

Next step: Set up KPIs and dashboards via Metrics & Dashboards → See also: Retrospectives