Tom Jones

Special Guest Blog Post written by Dr. Johannes Hangl with e2open   AI is no longer an experiment in global trade compliance. It’s already being applied in product classification, document-to-declaration workflows, risk targeting, and sanctions screening. At the same time, regulators and customs authorities are adopting AI themselves. This is raising expectations for data quality, transparency, and governance across the entire trade ecosystem. With the EU AI Act set to apply from August 2026, companies that have not yet implemented human-in-the-loop controls, drift monitoring, and defensible audit trails are running out of time to close the gap.   Where AI is already adding real value today: HS and ECN classification   Product classification has become one of the most practical AI use cases. Modern tools can now suggest harmonized system (HS/ HTS) and export control (ECCN) codes, explain the rationale, and attach confidence scores and audit metadata to each decision. This direction mirrors what customs authorities are doing. Administrations such as German Customs have discussed using machine learning to improve targeting and risk detection. It appears both sides of the border are moving toward data-driven decision support. AI does not remove accountability. It changes how accountability is exercised.   Practical…

Companies are rapidly integrating generative AI into procurement and sourcing decisions. The promise is obvious. AI can read thousands of pages of supplier proposals faster than any human team, summarize technical requirements in seconds, and create the appearance of consistency and objectivity in evaluation. But there is an important distinction managers are starting to overlook. The same AI system that performs extremely well at identifying whether a supplier meets minimum requirements may perform much less reliably when judging which supplier is truly better. Recent research published in the Journal of Business Logistics examined this issue by comparing how large language models evaluated supplier bids against evaluations completed by experienced procurement professionals. It analyzed 123 supplier proposals tied to 31 public procurement projects conducted by the State of Ohio between 2023 and 2024. The projects involved complex IT services contracts, many containing large, text-heavy bid packages requiring evaluative judgment rather than simple arithmetic comparisons. The researchers tested three reasoning-oriented AI models: OpenAI o3, Grok-3-Mini, and DeepSeek R1. They then compared their evaluations against human procurement scores. The findings revealed a surprisingly clear pattern. AI performed well when evaluating compliance signals. These are signals tied to baseline qualifications and technical requirements. Does…