Debunking Four of the Biggest AI Myths


It’s a buzzword that continues to dominate retail conversations yet there are still misconceptions around its practical uses. GreyOrange’s Samay Kohli wants to help retailers realise the benefits of AI for their supply chains.

There’s a lot of confusion around Artificial Intelligence (AI). Indeed, we have abused the concept; from The Terminator movies through to misperceptions of the cutting-edge technology today. Because AI is so linked to sci-fi and futuristic notions, some people in the retail industry think that it doesn’t have a practical purpose for their business right now.

GreyOrange’s Chain Reaction report with Retail Week surveyed 50 retail supply chain and tech leaders and found that, while only 18% are investing in AI and machine learning today, more than 40% believe it to be transformative to the future growth of their businesses.

There’s clearly a disparity and my mission is to bridge that gap by demystifying AI.

Myth 1: Strong AI isn’t here yet

AI at the simplest level is machine learning that is helping retailers make more informed decisions with their inventory and avoid grappling with data. While it’s mainly the social media giants that we see in the news maximising on AI and robotics, there are many retailers also making the opportunity real. It’s not an if, it’s a when. Retailers need to invest in this area or they will get left behind. 

Walmart, for instance, is using AI to make ecommerce more affordable. As a retail giant, it relies on optimising large warehouse facilities to meet consumer demand and so it is using GreyOrange’s AI Greymatter as its operating software to support fulfilment. Walmart is also benefitting from improved people retention (which I’ll explain in myth three).

Myth 2: AI isn’t practical for retailers

Brands are promising customers increased service levels. Ten to 15 years ago, it used to be that retail’s aim was to keep the shelves stocked. Now retailers are offering everything from drone delivery and one-hour delivery, to next-day and green deliveries, and more… but I have yet to come across one retail supply chain person who thinks these service levels are sustainable.

This is where AI comes in, by enabling retailers to manage their inventory and understand what items sell well with others and where those items should be placed. It’s a win-win as retailers can keep less inventory and meet these increased service levels, so there’s less waste, which brings down the environmental cost of supply chain and it helps reduce attrition rates. 

GreyOrange’s most prominent case study for this is H&M, which is running multichannel, multi-brand in a single warehouse facility. The site enables H&M to optimise all of its deliveries in one and this is ultimately helping it get closer to the consumer.

Myth 3: AI-driven automation will lead to job losses

Humans and robots are not in competition with each other and they won’t ever be; adopting AI is not about replacing people. Every single warehouse where GreyOrange has helped embed AI-driven automation, attrition has dropped.

The average warehouse worker only stays in their role for seven weeks. This means it’s not a job, it’s a gig. Think about it this way – on average, warehouse workers walk 13 miles a day, which is too much of a strain; by having the right mix of robots and humans coexisting at sites, retailers are making these jobs more doable, turning it from a gig to a career. 

With automation in warehouses and distribution centres, a retailer’s ability to fill jobs increases, too, as workers value not being treated like machines and are more likely to apply for roles knowing that their skills will be put to good use.

Myth 4: AI should only be considered on a trial basis

Piloting AI and robotics won’t help – retailers have to invest and embrace it fully to really make it work. AI is here to stay so the emphasis for retailers is not about deciding if you need it, but instead exploring how best to use it.

My advice to retailers when adopting AI is to look at how it can be used to solve an issue that affects 5% to 10% of your fulfilment. By starting this way, return on investment is then relatively easy to achieve. 

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