Customs AI Can Speed Declarations While Agents Retain Control

Customs AI can help brokers handle more declaration work by checking documents and surfacing inconsistencies. The key operational question is where its role ends: the tool can prepare and verify information, while a qualified person remains accountable for what gets submitted.

That division matters because a declaration error can have practical consequences. An incorrect commodity code may lead to underpaid duty, a later audit or a penalty. Missing certification can leave freight waiting at a border for days. In this work, an answer that is nearly right can still create costly follow-up.

Under Title I, Chapter 2, Article 15 of the Union Customs Code, the person lodging a declaration is responsible for its accuracy and completeness, as well as the authenticity of its supporting documents. The UK applies the same principle after Brexit. The responsibility follows the submission; it does not shift to the software that helped prepare it.

Automation can prepare the work, not own the decision

That principle has direct consequences for brokers and traders. A broker acting as an indirect representative is jointly liable for the debt. With direct representation, the trader remains responsible for the data it supplied. A software licence does not change those arrangements.

A useful customs system should therefore make its suggestions reviewable. It can read invoices and packing lists, validate commodity codes, check supporting documents and flag gaps in the data. When product wording is unclear, it should identify the ambiguity and show the evidence behind its suggestion. A qualified agent can then decide how to handle the case.

The approval trail matters too. A system should record who reviewed and approved each declaration before submission. That gives the broker a clear account of how information was checked and who made the judgment calls. Automation can reduce routine checking time, but people still need enough detail to assess exceptions and stand behind the result.

For teams handling repeatable work, that arrangement can improve consistency. Software can apply the same checks across a large number of documents without the attention lapses that can affect manual processing. Its value depends on agents being able to see what it found, understand where it is uncertain and resolve issues before a declaration goes out.

A brokerage uses AI to catch document errors earlier

EORI (UK), a customs brokerage that works with the AI platform Customaite, provides an example of this division in practice. The brokerage handles thousands of declarations each week. Its earlier workflow was manual: agents checked commodity codes line by line, validated documents and corrected data themselves. The process was reliable, but it took time and depended on sustained human attention.

Trade Solutions Manager Robert Keegan described the pressure as “rising volume combined with inconsistent trader documentation.” Incomplete invoices and vague product descriptions consumed agents’ time, while service-level agreement pressure continued to grow.

Customaite now handles recurring checks, including commodity code validation, document review and data quality flags. Agents retain the judgment calls and handle exceptions. Keegan likened the change to replacing a hand saw with a power tool: the person doing the work remains responsible, but can complete it faster and with greater precision.

A packing list error shows how that handoff works. The document had swapped gross and net weights. Customaite identified the mismatch early in the process, and an agent clarified the figures with the exporter before submission. The system detected the inconsistency; the person resolved it.

The brokerage also found data quality problems from some traders that had persisted unnoticed during manual processing. That illustrates a practical effect of applying the same checks repeatedly: recurring gaps can become visible, giving agents a chance to address them instead of correcting only the declaration in front of them.

Questions to ask when assessing a customs tool

For brokers evaluating an AI system, three checks are central. Can agents see the reason for each suggestion? Does the tool surface uncertainty instead of presenting a guess as settled information? Does a qualified person approve the declaration before submission?

Clear answers to those questions help teams judge whether a tool fits their workflow. The software should make the evidence easy to inspect and the exceptions easy to find. Agents, in turn, need a defined review step and enough time and information to resolve issues that automated checks cannot settle.

At EORI UK, the stated model is to automate repetitive work while keeping judgment with its agents. That approach leaves the declaration’s accountability with the people and businesses responsible for it, while using software to check more consistently and flag potential problems earlier. For brokers, the useful measure of trust is whether the system makes human review better informed and easier to document.

Jana De Borger is a trade compliance and customs software specialist based in Belgium. She holds a Master’s degree in Interpretation from KU Leuven and works at Customaite, focusing on global trade compliance management. She previously worked as a Senior Sales Executive at AEB, specialising in customs and trade compliance software and services, and held senior sales roles in other trade compliance organisations.

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