A telecom is scaling an AI-based outage-prediction tool from one region to a national deployment. During scaling, engineering teams in newly onboarded regions report the tool's predictions are less reliable than in the original region. What should the transformation lead evaluate first?

Reliability differences showing up specifically in newly onboarded regions point directly at differences in local conditions — regional network infrastructure and data quality — as the factor most likely affecting performance as the tool scales, which is exactly what needs evaluation first. Reverting the original region, the one that's actually working correctly, to manual prediction doesn't fix the reliability gap in the new regions at all; it discards a proven success instead of addressing where the actual problem lives. Renaming the tool to improve engineering confidence is irrelevant to the underlying performance issue — a new name doesn't change how well the model predicts outages given different infrastructure or data conditions. Reducing internal communications about the rollout similarly has no bearing on the technical and data factors actually driving unreliable predictions; it addresses messaging, not the mechanism behind the complaint. The operating principle: when scaling performance issues appear only in newly added regions, the first factor to evaluate is what's different about those regions' underlying conditions, not the tool's branding, messaging, or the performance of the region that's already working. Operational check: before treating this as a broad tool failure, compare data quality and infrastructure characteristics between the reliable region and the newly onboarded ones.