

the qualification questions and recom-
mendation processes sound a lot like
something most outdoor specialty sales
staff hear on their first day of training.
It’s also no secret how retailers need
to reduce the cost of running physical
locations in order to counter a declining
percentage of overall sales. It’s pretty
safe to assume, after all, that dollars
will continue to shift to online, mobile
and social. It’s even possible that online
sales are just now hitting a critical
mass, recently reaching 10 percent of
total sales.
It’s not something providers of such
technologies are anxious to talk about –
most smart technology initially is being
marketed as “assisting employees” – but
AI-ML-NLP technologies will be deployed
to reduce the labor costs involved in
keeping physical stores open, if they’re
not already having some impact on hiring.
(Wal-Mart, for instance, recently said it
was six to nine months from beginning to
use drones to check warehouse invento-
ries in the United States.)
While it’s certainly true that in many
cases AI-ML-NLP technologies em-
power retail workers and enable stores
to deliver an omni-channel experience,
in other cases, they specifically handle
tasks traditionally executed by retail
sales staffs, cashiers and warehouse
workers. When the machines are smart
enough and the artificial intelligent
enough, those tasks are done much
more effectively.
That is no slight on the value of retail
employees or the customer service they
provide. Rather it is an acknowledge-
ment of the rapid advancements taking
place across the spectrum of AI-ML-
NLP technologies, driven most recently
by “deep-learning,” whereby large neu-
ral networks modelled after the human
brain are fed enough data to be trained
4D’s inventory robot rolls through the aisles
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