International Trade
November 2nd, 2026 | Written by David Smason
Customs AI That Ignores the Operator is Built to Fail
Customs priorities will continue to evolve, from tariffs and “de minimis” to the latest advances in AI. Those issues get the headlines, but they can also make the conversations about technology and the issues operators face seem more complicated than they need to be.
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Building a company focused solely on bringing AI to customs (and still having the bruises to show for it) has taught me that the core challenges at the border don’t change: make sound decisions quickly, manage growing volumes and keep legitimate trade moving without compromising security. That’s the lens we need to bring to AI. The technology only matters if an experienced operator trusts it and can act on what it tells them, and getting that right takes a clear understanding of the field.
Three priorities can help agencies distinguish between AI that looks promising and AI that will actually deliver results.
Put Operators at the Center of Modernization
Customs AI that ignores the operator is destined to fail. Countless solutions are sitting on shelves because the experts they’re built for don’t trust them enough to use them. Customs operators are at the top of their fields, often with decades of experience behind them. They’re naturally skeptical of new technology, and they should be. If a system doesn’t perform the way it’s supposed to, they’ll lose confidence in it quickly.
Take, for example, a container coming through a port of entry that an algorithm flags as suspicious. The operator spends the time and resources to open the container, only to find there’s nothing there. If that happens even just once, the operator won’t use the technology again.
The same challenge arises when operators aren’t involved in the process from the beginning. If the people who’ll actually use the technology don’t have a role in shaping it, they’ll be resistant to its use before it’s even deployed. The most successful implementations involve operators at every step, from early surveys and proof-of-concepts through deployment. They’re built around existing workflows and use language operators understand. Just as importantly, they involve people with deep customs expertise who understand the realities of the job.
As a tech entrepreneur with a security background, one thing I’ve learned is that you can’t compromise on the time it takes to implement technology in the field. Building a system that works in a demo is very different from building one an operator will rely on at a busy port of entry. That extra work makes it much more likely that the technology will actually get used.
Prioritize Results Over Technology
The value of AI comes down to the results it delivers. Customs agencies need clear metrics for determining whether an AI investment is actually improving operations. Technical measures like F1 score, recall, precision and accuracy are important, but agencies also need to look at whether the technology is delivering measurable improvements in the field.
Take X-ray adjudication. Reviewing a complex cargo image can take minutes, even for an experienced operator. Advanced AI systems can significantly reduce that time. That’s a tangible outcome that can make a meaningful difference in operators’ day-to-day work.
One of the key challenges here is that customs is one part of a much larger process, so measuring impact isn’t always simple. Finding fraud, for example, doesn’t automatically mean the government can collect revenue on it. An abandoned container full of smuggled goods might never be claimed, meaning there’s no additional revenue to collect. But stopping those goods from reaching the market is still a successful outcome.
That’s why agencies need KPIs that reflect the broader mission. Real quantitative metrics on parameters such as time to release, revenue collection, narcotics interdiction and counterfeit goods seizures will all help show whether AI is producing meaningful results.
Invest in Technologies That Improve Decisions
Many customs agencies can now scan more cargo and collect more data than their teams can realistically review. What agencies need now is a better way to make sense of that data and turn it into action. That’s where AI can have the greatest impact. It helps customs officers analyze images and documentation together, identify anomalies and focus their attention where it really needs to be.
For operators, the output should be simple. A system might show red for high risk, yellow for caution, green when there’s no issue and blue when there’s a problem with the underlying data. The technology does the complex analysis in the background and gives the operator a clear indication of where to focus.
Some of the most valuable uses of AI are also the most straightforward. Document reading is a good example. Customs officers spend an enormous amount of time reviewing invoices, declarations and packing lists, often dealing with documents that are inconsistent or difficult to read. Automating that work can remove one of the most tedious parts of the job and give operators more time to focus on the decisions that require their expertise. It’s also a practical way to build trust by showing operators that AI can make their jobs easier.
Focus on What Doesn’t Change
Customs agencies that get these three things right tend to see results, and those that haven’t are still looking for the perfect solution ten years later. The difference comes down to focusing on what doesn’t change: the operational realities of customs work, the need for measurable results, and the expertise of the people doing the job every day.
That’s the lesson I’ve taken from building in this space: technology will always evolve, but the real value will always come down to whether operators trust it to help them make better decisions for the mission.
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