AI Takes Retail by a Storm

Generative AI in Retail

This article is a continuing series on fields gaining on automation and artificial intelligence. Today’s topic is the Retail Sector.  download article


AI in Retail is not a quick fix

Organizations first need to prepare for digital transformation. Converting processes, consumer trends, previous decisions, databases, transactions, videos, documents, etc. into usable digital data comes first. Not only that, AI applications need to be trained once the data is available.

Great care is taken so that the advantages of AI applications can be realized. AI is like a baby that needs nurturing to mature. AI is not a one-size-fits-all.

Organizations need to be on board with the technology.  Personnel go through training, acceptance, and understanding of this new-age tech.


Automation has proved itself

Automation has transformed the retail landscape for ages. For example, Walmart has been at the forefront of automation for a long time. Their business may not be as chic as the fashion industry but you can’t argue with the results. Walmart is the world’s largest company by revenue, with about US$570 billion in annual revenue, according to the Fortune Global 500 list in October 2022. Their success comes from the hub and spoke distribution system along with automation to trigger replenishment orders to its suppliers.

Currently, AI is taking retail to the next level.


About AI tech in Retail

Generative AI uses computers enhanced by graphics processing technology. This tech crunches big data in a blink of an eye and then regurgitates solutions regarding commercial business.

AI Enhanced Marketing

Illustration of a shopper

Most of us have searched for products or movies online. Likely a good percentage of us have had product recommendations later on where we thought “Hey, that’s not a bad suggestion, maybe I’d like to get that”, right? So what’s going on?

Generative AI applications create personalized shopping experiences and generate product recommendations. Here’s what AI is doing under the hood:

Numerical weights are being placed on genres and categories we have selected in the past. Next, products are recommended on whatever fits this profile. Finally, it records the predictions that were correct and gives the selected items high scores (weights)- AI learns our shopping behavior.

That is the central mechanism of AI; grading the options, predicting options based on past selections, and recording which predictions were correct.

What is newsworthy is that generative AI can review YouTube videos and other social media to feel the pulse of consumers. Marketing analysts no longer need to wade through mountains of public feedback and make sense of it. AI can do that for them.

Customers prefer non-invasive communications tailored to their liking. AI can be tuned for sensitivity to timing, method of communication, and quantity of content that fits the customer’s personality. Revenue increases by 40% when marketing is personalized[1].

For new products, AI apps can take a sketch and turn them into finished product designs. Or it can create new product designs based on a mix of trending likes.     

In summary, generative AI helps brands, marketers, and retailers to:

  1. improve customer engagement
  2. increase sales
  3. enhance the overall shopping experience[1][3][5].


Supply Chain Operation

Illustration of Supply Chain Managment Functions

A supply chain refers to the life cycle of products, from where they originated to their final destination and everything in between- womb to tomb.

Generative AI applications optimize

  • Logistics – moving stuff from point A to point B
  • Store operations – minding the store
  • Organization and support functions – administrative support[2].

Generative AI is involved in improving Retail’s agility, visibility, and sustainability. These functions reduce costs, and enhance forecasting and capacity planning[1][2][3][4][5]. That’s a big ask.

AI-based supply chain management solutions help organizations tackle the challenges of all business functions, from procurement to sales [2].

Managing the Supply Chain is complex. There are a lot of moving parts that need to work together. AI uses powerful data processing to sort the data and connect the dots. 

Supply Chain AI-enhanced areas:

  • warehouse center efficiencies[1]

Illustration of Warehouse parking lot

by optimizing warehouse centers for efficient power, waste, and fuel management. This results in cost-effective deployments[4].

  • supply and demand modeling

Illustration of a supply and demand chart

AI evaluates consumer trends, economy, and business headwinds to make statistical predictions of supply and demand

  • capacity planning[3]

Illustration of a capacity planning chart

Capacity is about quantities needed, like personnel, equipment, suppliers, etc. Generative AI can use statistics gathered from the data to predict the capacity needed to optimize the business.

  • overview across the supply chain spectrum[4]

Illustration of supply chain management overview

An overview is normally a dashboard app indicating performance to the established metrics across the supply chain.

  • personnel workload balancing[5]

Illustration of workload balancing

AI must learn from the SCM experts before any benefit can be realized. AI is allowed to make decisions about the many tradeoffs that occur between the supply chain’s moving parts.  Afterward, SCM personnel roles adjust to support the AI system instead of chasing problems.

  • satellite image route overlays

Illustration of satellite image of a highway

AI can see logistical problems like breakdowns and traffic jams and help deliveries stay on time.


Store Operations

AI can

  • streamline inventory, sales, and processes
  • improve customer connections to brands
  • empower associates to stay focused on customers[1][2][3][4][5]
  • manage day-to-day operations
  • execute buy online/pick-up in-store
  • reduce in-store equipment costs[2][3]
  • replenish shelves using predictive analysis [3]
  • Scan for safety issues[4]

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