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it interacts with the user until all the
required details have been provided and
then delivers a code. AI comes into play
via the use of natural language processing
to read, analyze, and understand product descriptions, Rotchin says. Currently,
machine learning is being used to reduce
the number of questions and/or choices
presented to users by, for example, eliminating theoretically possible but unlikely
options and offering only those that are
known to be relevant.
Some other GTM software providers
are developing their own AI and ML
tools for classification, while some incorporate 3CE’s solution into their products. Thomson Reuters, for example, does
the latter, giving its software the ability
to understand plain-language product
descriptions and identify the correct HTS
or ECN code (for imports or exports,
respectively), says Mary Breede, a customer insight leader for Thomson Reuters’
Onesource Global Trade Management
solution.
Another example of how AI and ML are
solving problems in global trade comes
from Pawan Joshi, executive vice president of product development at E2open,
the parent of GTM software provider
Amber Road. He notes that many data
sources include inaccuracies and inconsistencies. E2open uses machine learning
to correct such errors and improve the
data quality based on the source, he says.
“For example, if we keep getting addresses
with the city spelled incorrectly, we can
correct that across multiple languages.”
And because each of the tens of thousands
of entities on E2open’s network platform
has a distinct “signature,” the system can
identify the source of the incorrect information. “Machine learning has the ability
to self-correct it without human intervention,” he explains.
Joshi notes that an added advantage
of using ML to improve data quality in
global trade is that the number of errors
constantly declines as the machines learn
more, which may free up customers to
focus more on preventing problems.
KNOW THE LIMITS
While the benefits of applying AI and ML
to classification are clear, that doesn’t
mean they can—or should—com-
pletely replace human experts. One
reason, says Pride, is that some of
the historical data the technology is
learning from may be incomplete,
outdated, or simply incorrect.
What would happen if an AI- or
ML-based solution recommended
an incorrect classification? By law,
the importer is ultimately respon-
sible for customs compliance, and
McNeill notes that most software
vendors have clauses indemnifying
them in case of errors. However, the
technology could itself be a mitigat-
ing factor because it imposes a con-
