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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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