AI Implementation Lessons Learned
This is a practical guide to building a lessons learned for an ai implementation project — what went well and badly, captured to improve future projects, adapted to the realities of delivering an AI/ML capability from data to production value.
What a Lessons Learned is
A lessons learned is what went well and badly, captured to improve future projects. For the full concept and how it works in general, see Lessons Learned. 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 lessons learned 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
- What happened
- Impact
- Root cause
- Recommendation
AI Implementation-specific considerations
Tailor the lessons learned 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 lessons learned 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.