Why retail’s artificial intelligence bet is all wrong Posted on : Mar 05 - 2018

Everyone seems to be “using” artificial intelligence these days. So is retail. Big players like Amazon and Target are pouring huge amounts of resources into machine learning, and many companies sell “Artificial intelligence” tools for the retail industry.

There’s just one problem. Most of what the retail industry refers to as artificial intelligence isn’t AI. Furthermore, it’s bad for both customers and retailers. Using the “AI” that worked online in physical stores risks making physical stores look increasingly like websites amid a larger trend towards automation and reducing human presence in stores. This is altogether a very poor idea.

I teach at MIT and have worked with artificial intelligence tools to solve problems across several domains for years. AI is a field, but also an aspiration, we’re sold on (or fear) the aspiration, but it’s important for retailers to understand what AI –the field– can deliver today and how it can help (and hinder).

Despite all the hype, there is a place for artificial intelligence in retail for the future.

AI myths & AI hype

Many retailers have forgotten what really helps customers: In-store assistance from human workers that’s relevant to their shopping needs. Artificial intelligence can help human employees with that task, as well as with the less glamorous but quite important realm of supply chain and logistics management that has given Amazon its edge.

In order to understand how AI tools can help, it’s important to understand what AI is not.

In the retail world, products like Amazon Alexa and chatbots are commonly referred to as AIs, but they’re just sophisticated programs built on top of machine learning, natural language processing, and statistical algorithms.

There’s also a lot of hyperbole around machine learning and analytics that conceals a people problem. Used that way, machine learning needs vast amounts of data that needs to be formatted and cleaned for use. Computers aren’t good at automatically cleaning data; humans are.

The technologies we use today are hobbled by this. In order to receive clean data for machine learning, for instance, many retailers send customers questionnaires which are easier for computers to process. View More