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relate data, recognize patterns, and
use what it has “learned” or experienced to improve its predictive or
decision-making model.
Most GTM software vendors have
“put AI and related technologies
on their product road map,” Pride
says, “but AI is still developing, and
vendors are trying to find the best
places to use it.” She and others con-
sulted for this article believe there’s
a strong use case for AI and ML in
global trade, particularly for classi-
fication accuracy (the main focus of
this article), denied-party screening,
calculating estimated time of arrival,
and risk prediction and avoidance.
AI and ML tools can process enormous
amounts of information from multiple
sources, including the tariff classification
schedules, a user’s own product data, cus-
toms rulings, and historical classification
data. What’s particularly valuable about
machine learning is that it considers the
same data sources that are available to
humans but “looks for correlations that
you can’t get to with the human mind,”
McNeill says. For example, when human
experts classify an imported product, they
use their own country’s version of the
Harmonized Tariff Schedule and maybe
review some examples of what other com-
panies have done. But machine learning
could go further, he says. A hypothetical
example: analyzing historical submissions,
cross-referencing them to dispute rulings
and other relevant data, and detecting
a recurring problem with imports asso-
ciated with the classification number in
question. “I would argue that it’s not pos-
sible for classifications and denied-party
screening to be accurate enough without
automation,” McNeill says. “You couldn’t
make the correlations that enable you to
put a new lens on the data you have and
find new value from it.”
Automating routine tasks and freeing
up experts to focus on problem-solving
makes sense, especially since it’s hard to
find experts in this field. Furthermore,
machine learning could force proper
application of the GRI. And when con-
fronted with new products that aren’t
explicitly provided for in the tariff sched-
ules, Pride says, ML could “learn” from
similar cases and recommend new classifi-
cations—not just to importers but also to
governments. In fact, some governments
are already using AI to identify classifica-
tion errors and related violations.
One example of how this works can
be found in the classification solution
offered by 3CE Technologies. President
and CEO Randy Rotchin describes the
software as “an expert system designed
to emulate how an expert would tackle
this problem.” The software “reads” the
commercial goods description, and if all
the details needed for correct classification are included in that description,
it suggests a classification code. If not,
