Supporting Your Retail Workforce With AI & ML
We hear these words all the time: ‘Artificial Intelligence (AI)’ and ‘Machine Learning (ML).' But what do they mean, and how do they help your retail operations?
Let’s start with artificial intelligence
AI is when a machine is given data and programmed to think like a human. It takes the human error away, and the machine can adapt and sequence its “thoughts” in a way that is applicable to your store. AI-driven optimization automatically generates employee schedules, considering factors like business volume, labor demand, skills, preferences, and schedule and pay compliance. Consider how much that is for one person to keep track of and make decisions around, while also running all the other tasks it takes to keep the store and team operating smoothly from day-to-day. Might seem like a little too much for one person to handle in a day. In fact, scheduling often takes a huge amount of time for managers. One customer, Kum & Go, noted that it would take at least an hour for their managers to figure out the scheduling puzzle. That is why it’s easier to give information to the computer to process and learn over time. Allowing AI to take the first pass at scheduling, frees up your manager to get back to tasks where their presence is absolutely necessary, like coaching their team. Additionally, the data being used will help the computer learn, adapt, and create more effective strategies, while also benefiting your company. Gartner recently stated, “In 2021, AI augmentation will generate $2.9 trillion in business value and recover 6.2 billion hours of worker productivity.”¹
But isn’t machine learning the same thing?
The two terms tend to go hand-in-hand, but ‘Machine Learning’ is the actual process of AI’s work. Machine learning allows for retailers to monitor traffic patterns and adjust their forecasting to learn the ebbs and flows. Forecasting can be a major obstacle for retail operations, but using AI to detect patterns allows managers to make more accurate decisions. More accurate forecasts achieved through machine learning will allow retailers to optimize labor and get the right person, with the right qualifications, to the right position, at the right time. We continuously see retailers manually creating their schedules and having to find associates to cover shifts at the last minute. Not only does this mean doing a job twice, but it also takes valuable time away from the store manager. We have seen that forecasting using machine learning methods can improve the accuracy of forecasts by 20 percent, an increase worth tens of millions of dollars as schedules better reflect the demand in a retail store.
So why use AI and ML in retail?
When you partner AI and ML together, you are going to have happier outcomes for both your corporate offices and the store employees. With the help of UKG’s machine learning and AI, you will see less HR issues and proper staffing for your locations. The system will learn what works best for your employees in terms of scheduling and forecast shifts that will fit everyone’s needs. No more headaches and no more constant shift swapping. Your managers will be able to focus on what matters for driving your business forward every day and employee satisfaction will improve.
The past year has been hard on store associates. Your essential workers have been working relentlessly, and their schedules were more often than not a puzzle. With schedules optimized by AI in place, your operation can help promote a happier, safer, and more productive work environment for your employees. Remember what Gartner quoted earlier? “6.2 billion hours of worker productivity recovered.” Now imagine having that in your store. When AI and ML are partnered together effectively, you are going to have better retention rates and more productive employees.
If you want to learn more, check out our Research & Insights page for information that will show the value of AI and ML and how important your data is to the future of your company.
¹Gartner Research, Predicts 2018: AI and the Future of Work