Importers have long turned to global trade management
(GTM) software and stand-alone tariff-classification solutions to help them classify their products correctly. But
the complexity of the task makes it hard to fully automate.
Now, software providers are betting that incorporating
artificial intelligence (AI) and machine learning (ML) into
their products to make the software “think” and act more
like a human expert will take classification technology to
the next level. Proponents believe these technologies could
significantly improve accuracy and prevent costly errors—a
welcome development at a time when so much is riding on
getting things right.
DESCRIBE GLOBAL, CLASSIFY LOCAL
Like most countries, the U.S. bases its classifications on the
global Harmonized Tariff Schedule (HTS), a numerical
product-identification system administered by the World
Customs Organization. The U.S. version, known as the
Harmonized Tariff Schedule of the United States (HTSUS),
takes the global version’s descriptions and classifications
and adds more fine-grained detail.
It’s complicated, to say the least. The HTSUS assigns each
imported item—which may be a finished product, part,
component, or raw material—a unique 10-digit number.
To find that number, importers drill down through chapters, headings, subheadings, and individual item numbers,
progressing from the vague and general (vegetable products) to the excruciatingly specific (cut flowers and flower
buds of a kind suitable for bouquets or for commercial purposes, fresh, dried, dyed, bleached, impregnated, or otherwise prepared: roses). The HTSUS currently includes more
than 17,000 classification-code numbers. (U.S. exports
require a similar product classification and an export control number, or ECN, to be filed with the U.S. Department
of Commerce.)
Identifying the correct number can be a tedious,
time-consuming process. It’s also complex and rife with
ambiguity, even for customs-compliance professionals. A
product might fit several HTSUS descriptions, or it might
fit none of them exactly.
Furthermore, the descriptions, which were developed for
duty-assessment purposes, may bear little resemblance to
those used by manufacturers or end-users. Just one example: electric toothbrushes, which are classified not with
other toothbrushes but as electronics. Sometimes creativity
is called for, such as when classifying sets and kits, which
requires establishing the product’s “essential character.”
To top it off, the General Rules of Interpretation (GRI)
that govern the classification process are difficult to learn,
says Beth Pride, president of BPE Global, a provider of customs-compliance consulting services.
It’s a challenge for anyone—including the government
officials who judge whether a classification is correct—to
always get it right. Now, the hope is that bringing today’s
digital tools—namely, AI and ML—into the process will
help cut through the complexity.
WHY AI AND ML?
Artificial intelligence is an umbrella term for any type
of technology that mimics human thought patterns or
behavior, explains William McNeill, a Gartner analyst who
writes an annual market report on GTM software. The
algorithm-based technology conducts analyses, makes decisions, and responds or takes action much as humans do.
For example, AI applies “natural language processing” to
recognize, understand, and respond to or act on written or
verbal communication. Machine learning is a subset of AI.
This technology uses iterative processes to access and cor-
