AI Implementation Change Log
This is a practical guide to building a change log for an ai implementation project — a record of change requests and their decisions, adapted to the realities of delivering an AI/ML capability from data to production value.
What a Change Log is
A change log is a record of change requests and their decisions. For the full concept and how it works in general, see Change Log. On an ai implementation project it plays the same role, tuned to this kind of work.
Why it matters for an AI Implementation project
AI Implementation projects live or die on delivering an AI/ML capability from data to production value. A well-built change log gives the team a shared, explicit reference for exactly that — reducing ambiguity, aligning stakeholders, and making problems visible early enough to act. Skipping it, or doing it generically, is how ai implementation projects drift into avoidable delay and cost.
What to include
- Change ID and description
- Impact assessment
- Decision and date
- Updated baselines
AI Implementation-specific considerations
Tailor the change log to the risks that most often derail ai implementation projects:
- Poor or insufficient data
- Models that don’t generalise
- Unclear ROI and adoption
Example
On a real ai implementation project, the change log would be shaped by delivering an AI/ML capability from data to production value. In particular, it should explicitly account for the project’s biggest risks — poor or insufficient data, models that don’t generalise, unclear ROI and adoption — rather than treating them as afterthoughts.