AI in Retail: How Does Automation Increase Productivity?

Is Artificial Intelligence becoming more intelligent? Perhaps it is the Amazon effect, or maybe it’s accelerated technological advances, but businesses across all industries are now harnessing the power of AI to deliver trusted forecasting and merchandising decisions.


But, is there a shift away from manual forecasting and human reliance, and if so, why? Does AI allow retailers to predict the future, or does it just make automated connections from information that humans brains fail to process?

How Does the ‘Amazon Effect’ impact Artificial Intelligence

The ongoing evolution and disruption of the retail market, both online and in-store, is often cited as the ‘Amazon Effect’. This is because, to compete with the global behemoths like Amazon, retailers are constantly seeking advanced methods to streamline their merchandising processes, and offer highly personalised experiences.

According to Research and Markets, the global AI in retail market size is expected to grow from USD 993.6 million in 2017 to USD 5,034 million by 2022, at a Compound Annual Growth Rate (CAGR) of 38.3%.

According to the same report, the key drivers for this growth in retail are improved Return on Investment (RoI), boosted inventory accuracy, and supply chain optimization. Perhaps the question should no longer be, ‘why should retailers use AI?’, but instead, ‘how?’

The flagship product of Amazon’s AI breakthrough is its smart speaker, the Echo, and the Alexa voice platform. Certain components of their voice recognition intelligent system, such as re-ordering commands, can turn traditionally inactive shoppers into potential loyalists with supreme convenience and ease of experience.

The most notable point, however, is that grocery retailers’ profit margins are incredibly thin. Amazon, meanwhile, is well positioned to cut costs and offer streamlined logistics services than its grocery competitors whose expertise outside of grocery are limited.

Does AI make predictions or just optimize processes?

There is potential that AI may soon predict the future; Google’s Deepmind division the branch that conducts AI research, is now giving its AI algorithms an ‘imagination’. When coupled with machine learning, AI could soon predict how certain situations will evolve with a high level of certainty and make decisions.

Amazon, meanwhile, has been driving an AI-focused strategy to predict consumer behavior for some time. Its recommendation system runs on a totally machine-learning infrastructure, so its suggestions on what to buy, watch or read next are incredibly smart. This leads to more conversions and upselling across the business, as well as giving Amazon insight on how to price its products for its customers, and how much stock to hold.

Does AI Represent a Silver bullet syndrome?

Behind the scenes, technology is being used by a number of retailers to automate decisions based on data sets larger than human brains can process. At the same time, some are guilty of investing in AI for ‘technology’s sake’, or taking a haphazard, scattershot approach without a specific goal in mind. In these instances, unfortunately, merchants feel let down by their new technology; AI fails because retailers expected a lot more from their investment.

Do merchants need AI to accurately predict trends and buying patterns? Or is it oversold as a magic solution? The answer depends on how it is used.

Using Artificial Intelligence to solve a specific challenge

AI shouldn't be approached in isolation. For the technology to give retailers true forecasting insights, they must already value the power of data science and analysis.

For Morrisons, for example, it identified that its traditional replenishment systems were failing. Based on laborious manual ordering from an in-store workforce, it was leading to significant overstocking and understocking issues, costing the business money. The UK retailer recognised that data could provide an answer. In the end, AI decision making reduced stock holding in store by 2 to 3 days; a huge impact on the sale of perishable produce.

AI in retail doesn’t predict the future, at least not yet. It takes reams and reams of intricate behavioral and circumstantial data analyses to form patterns and trends. These trends allow retailers to make science-based decisions that result in more accurate stock levels, and pricing that better suits product life cycles.

This is especially valuable in the grocery retail industry, in which stock is highly perishable with short shelf lives.

Prof. Dr. Michael Feindt Prof. Dr. Michael Feindt

is the mind behind Blue Yonder. In the course of his many years of scientific research activity at CERN, he developed the NeuroBayes algorithm. Michael Feindt is a professor at the Karlsruhe Institute of Technology (KIT), Germany, and a lecturer at the Data Science Academy.