Bottleneck Scoring Exposes Flaws in AI Readiness Assessments

Traditional weighted-average scoring masks critical weaknesses like missing version control, giving falsely high readiness scores. A new model caps total scores when fatal preconditions are absent, reducing to a simple Math.min() function. This approach, grounded in Liebig's law of the minimum, ensures that a team without Git cannot score above 49 out of 100.