103139Advanced Algorithm Training on European Languages and Edge-Cases in Logistics to Drive Widespread AI Adoption to Build Pandemic ResilienceClosedCollaborative R&DInnovate UKThe World Economic Forum (WEF) highlighted that COVID-19 exposed systemic weakness in logistics' physical and manual data-processing operations, primarily email and trade-document driven workflows. The pandemic compounds existing inefficiencies and lack of scrutiny, costing Fortune 500 companies $81 billion of unnecessary supply chain costs each year (JPMorgan 2017 Trade Outlook). Whilst the WEF recommendation of digitisation innovations offer remedies to improve desperately needed business resiliency, there are challenges to effective adoption. The logistics industry has been notably slow to adopt AI, only 12% of organisations currently leverage AI (MHI Industry report,2020). One major reason for the lack of AI adoption is that the logistics sector has ever-changing, non-standard and complex information, which poses massive algorithm scalability challenges. This project aims to meaningfully enhance our research and development of an automated algorithm-training pipeline in-built into our existing logistics' machine learning workflow automation platform. The no-code pipeline operates in the background, automatically re-training our data-extraction algorithms to customers' evolving email and document content. This project's research will thoroughly and specifically explore academic application of extraction techniques on multi-lingual and edge-case data-samples to bolster our basic training pipeline, thus increasing applicability, encouraging rapid adoption via scaling ease and efficiency.