December 5, 2019
Top retail cash trend for 2020: Artificial intelligence and machine learning lead to innovative operations
Balance Innovations introduces Cash Insights, a series covering retail cash trends, industry developments and innovations. This is the first in the series, written by Darren Knipp (bio below).
Retailers who are experimenting with artificial intelligence are optimistic about a future with streamlined operations and significant cash savings, all managed scientifically.
Although still in the early exploratory stage, recent developments in AI and machine learning point toward multiple opportunities for improved retail operations. Newly developed AI software can influence decision making for cash management, inventory control, back office record keeping, fresh produce and deli production, store safety and robotic floor cleaning, to name only a few. Machine learning, a branch of artificial intelligence based on the concept that systems can learn from data, can identify patterns and make recommendations or decisions with minimal intervention from people.
Increasing interest in cash management
In the past year, as we’ve talked with our retail customers, we’ve seen a lot of interest and experimentation going on with AI and machine learning. An area where AI can have an impact in the retail setting is the use of it to better understand and utilize cash assets across an enterprise. Retailers keep certain amounts of cash at each store because that’s what they’ve always done. Take self-checkouts for example. We’re seeing increased use of self-checkouts, which can hold a large amount of cash – upward of $5,000 each, depending on the size and type of checkouts. The question now becomes, is that necessary? AI enables retailers to make better use of cash in these machines.
How much is too much?
Of course, retailers don’t want to run out of cash at the checkouts and delay shoppers trying to get out the door quickly and efficiently, which can impact the customer experience. On the plus side, stuffing these machines full of cash frees up maintenance time and ensures plenty of cash to meet the demands of a busy day at the registers. But, on the flip side, it ties up cash – often a lot more cash than a store actually needs – that could be used for higher value projects that can improve sales or customer experience.
Better cash distribution
To guide retailers toward more efficient operations, we’ve developed AI and machine learning algorithms that define the amount of cash that should be in each store overall; and then specifically, across self-checkouts and tills. It’s clear that the cash needs of a large retail chain vary widely across the enterprise based on location and shopper demographics. Machine learning collects and analyzes all of that data so there’s no guesswork about demand. AI algorithms aided in our discovery that most retailers can reduce the amount of cash they keep across all of their stores by at least 40 percent to 50 percent. So, rather than their cash being held, unused, in a self-checkout or a store safe, their cash can be put to good use.
AI and machine learning can create new savings across a large chain that can be used for other purposes such as expansion, store remodels, acquisitions or whatever else they need to stay competitive in a crowded marketplace. Machine learning is a pragmatic approach – slow and controlled at first, relying on algorithms to get accurate predictions. This data collected over a period of time shows retailers ways to be more efficient and aggressive in how they manage cash. AI and machine learning will expand across many retail sectors for the coming decade and beyond. To learn more about how AI is transforming retail, check out IHL Group’s recent research paper on the promises and potential of AI. Click HERE to get your copy.
Next: Retail trend for 2020 – Creating a better in-store experience
As SVP of Brink’s Enterprise Solutions and President of Balance Innovations, Darren brings his passion for creating customer focused solutions towards helping retailers revolutionize their approach to managing cash.