AI Implementation Decision Log
This is a practical guide to building a decision log for an ai implementation project — a record of the key decisions made, by whom and why, adapted to the realities of delivering an AI/ML capability from data to production value.
What a Decision Log is
A decision log is a record of the key decisions made, by whom and why. For the full concept and how it works in general, see Decision 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 decision 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
- Decision
- Date and owner
- Rationale
- Alternatives considered
- Impact
AI Implementation-specific considerations
Tailor the decision 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 decision 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.