A logistics company is operationalizing an AI solution to optimize delivery routes.

In CPMAI and PMI-aligned AI lifecycles, integratingdiverse data types from multiple operational systemsis typically handled through robust data engineering pipelines, most commonly implemented asETL (extract, transform, load)or closely related ELT patterns. For a logistics optimization use case, the AI system needs to bring together traffic patterns (often from external or sensor feeds), internal delivery schedules, and vehicle performance/telematics data into a consistent, analyzable structure.

An ETL pipeline is designed precisely for this: itextractsdata from heterogeneous sources,transformsit into common formats and schemas (handling units, timestamps, geocodes, data quality rules), andloadsit into a target store (data lake, warehouse, or feature store) that downstream AI components can consume. CPMAI emphasizes that this integration work is a core part of the Data Understanding and Data Preparation phases, because AI models depend on unified, high-quality inputs rather than fragmented, siloed feeds. While real-time frameworks, federated models, or warehouses may play additional roles, the primary method explicitly focused on integrating diverse data sources into a coherent whole is anETL pipeline, making option B the best fit.