Opinion: Are We Modernizing Trade Compliance Or Just Digitizing It?
Digital technology promises to make trade compliance lighter by reducing paperwork, errors and processing time. Individual tasks can now be automated. Yet the work between them often remains manual, with information moving between systems through spreadsheets, emails and repeated data entry. This raises a question. Are we modernizing trade compliance to improve the end-to-end process, or simply digitizing the tasks within it?
The distinction matters as requirements change. Changes in filing requirements can mean revisiting product data, classifications and supporting documents across affected shipments.
Meanwhile, customs authorities are developing their own technological capabilities. A 2025 report produced under the World Customs Organization's Smart Customs Project discusses applications of AI and machine learning in customs risk assessment and fraud detection. For businesses, this reinforces the importance of consistent, reliable information that supports data-driven assessments.
Advance-filing regimes also require reliable information before cargo arrives, and in some cases before loading or departure. US Air Cargo Advance Screening and Japan’s maritime advance-filing rules illustrate why businesses need to resolve data gaps earlier in the shipment journey.
Heavy reliance on spreadsheets and email makes that harder as requirements and shipment volumes grow. Instead of focusing on complex cases and unfamiliar requirements, specialists spend time transferring data manually and resolving familiar discrepancies.
Technology, including AI, can reduce that burden. But a new application may improve one task while leaving teams to reconcile information manually with the systems around it, adding complexity instead of removing it.
While it is tempting to measure the progress of modernization by the number of tools deployed, what matters is whether teams can act on reliable information without rebuilding the same record at each stage of the shipment journey.
Businesses can test their own trade compliance workflows against that standard by asking the following.
Who is accountable for each critical data element?
Product descriptions, origin information and classification decisions need identifiable owners and supporting evidence. A tool can reveal conflicting records, but visibility alone does not establish which is right. Businesses must define who resolves discrepancies and approves changes, before using technology to route issues and record decisions.
Does an approved correction reach the source record and downstream workflows that rely on it?
A fix that stays in an email may resolve one filing while leaving the next shipment to trigger the same query. Changes should retain a traceable history, and information should be reassessed when relevant circumstances change.
Is recurring work declining?
Faster processing is valuable, but repeatedly flagging the same discrepancy may leave its root cause untouched. Track repeat queries, manual corrections and the effort required to implement changes. These show whether the operation is improving.
AI should face the same questions. It can identify incomplete information and suggest classifications for professional review. Its value increases when those findings inform verified decisions and updates to the relevant records. AI alone cannot establish which conflicting record is authoritative or who should approve a correction. That accountability remains with people.
The return on modernization should be visible in daily operations. When a requirement changes or a record is corrected, teams should be able to act without rebuilding the same information across systems. That is how modernization makes trade simpler and compliance smarter.
Siddharth Priyesh is Senior Vice President & Head of Group Commercial at CrimsonLogic. The opinions expressed do not necessarily represent those of The Supply Chainer.
