Originally designed for more trivial tasks in the second half of the 20th century, AI has now become more widely available and is exponentially increasing in accuracy and reliability. Many software development companies now specialize in machine learning, which is a subset of AI, in order to ease the process of more complex tasks such as data analysis.

In a nutshell, if you send a computer 1,000 photos of t-shirts on different people and tell the computer they are photos of t-shirts, after using a specific machine learning method (e.g., decision trees), the computer will be able to identify what is and what isn’t a t-shirt. This is an oversimplified example, but essentially, a computer learns on its own after being provided an original set of data. While some developers are working to improve intuition-only AI (no initial data needed), the example above is more widely available.

Aside from analyzing large sets of data, AI and machine learning can seriously reduce costs for retail companies. AI can help retail companies in the following ways:

• Manufacture and design products
• Detect counterfeit items (image and material recognition)
• Predict future consumer trends with less error than human intuition
• Customize the online and physical shopping experience for individual users.
• Provide retail analytics using custom dashboards
• Provide real-time video content analytics for security cameras

You’d be surprised how accurately today’s AI can detect even the slightest imperfection in clothing. AI has been helping lately, specifically retail companies, to look at fabrics to determine whether or not they are counterfeit.

More importantly, however, combining AI and business analytics has allowed for incredibly accurate predictions in consumer trends. Being able to predict which clothes are going to be the most popular at the beginning of the month, versus the end of the month is a HUGE deal.

When it comes to managing company data, more importantly, making sense of it, specialized software can be used to easily make predictions on what next months’ consumer trends are going to be. But what people often forget are the day to day tasks that AI can accomplish.

In our article, How Retail and Video Analytics are Helping Brick-and-Mortar Stores, we discussed the importance of video content analytics (VCA). In video analytics, facial recognition is a game-changer, as being able to determine specific moods may be the difference between a repeat customer and someone who ditches the store.

Being able to detect specific moods automatically and what paths people are taking throughout your store, and more importantly, what you can do to improve these trends further is something that recent AI software has been able to achieve.

Retailers are now moving away from 20th century styled methods of analysis (e.g., monitoring security footage with a single person), and now turning towards a modern approach incorporating a few specialists and a lot more AI to monitor the store’s performance.