Meridian's customer-service AI program combines a conversational assistant for passenger questions, an anomaly-detection model for loyalty fraud, and a predictive model for cargo demand, all reporting into one shared operations dashboard. From a CPMAI standpoint, what should the PM keep in mind when integrating multiple AI patterns into one comprehensive application?

CPMAI Task 2's enabler on integrating multiple AI patterns for comprehensive applications is about assembling complementary capabilities into one coherent program, not merging their underlying technical requirements. Each pattern here — conversational, recognition/anomaly-detection, and predictive-analytics — still needs its own appropriately sourced data, its own success metrics, and its own model type; a shared dashboard is a reporting and operational-visibility layer on top, not a technical unification. The 'becomes a single AGI-level system' distractor dramatically overstates what integration accomplishes; three narrow-AI systems reporting to one dashboard remain three narrow systems, nowhere near AGI. The 'apply the least mature pattern's data requirements uniformly' distractor is a real project-management trap: it would degrade the better-resourced systems to match the weakest one's data quality rather than raising each to its own appropriate standard. The 'CPMAI discourages combining patterns' distractor is directly contradicted by the outline, which explicitly lists integrating multiple patterns as a Task 2 skill — comprehensive applications combining patterns are expected and common. The PM's real job is coordinating separate workstreams under one governance umbrella, not collapsing them into one undifferentiated effort.