The Economics of AI in Foodservice

For decades, labor costs in foodservice businesses have been 30-35% of revenue. As minimum wage increases take effect across the country, restaurants grappling with changes to their P&L’s face a new set of challenges. At the same time, the cost of silicon and AI-based solutions has continued to fall. We stand today at a crossroads where AI systems are being introduced broadly because the economics are more compelling than ever.

But how do these AI systems benefit your business? Restaurants generally could be better businesses if they could serve the food fresher, or if they could be run with greater labor efficiency. For this reason, from robotic fry cooks to voice-activated drive thru order takers, the most revolutionary advance has been AI-driven inventory tracking and production planning.

Straight from the Fryer: Serve it Fresh and Crispy

Fried food is best served immediately after frying. It comes out with a delicious crispy layer, with the oil still bubbling on the edges, and a warm interior with moisture and body. However, QSRs will often overproduce to make sure there’s food available. The longer the extra food sits in warm holding, the more time the moisture has to seep out and make the crispy exterior soggy. Although warm holding has made operations far easier, overholding is perhaps the most important problem affecting customer satisfaction in QSRs.

Warm holding can be overused, leading to stale food and damaged customer satisfaction.

It doesn’t need to be this way. Even with static forecasts, GMs can predict how much food they’ll need to serve at any time. With machine learning, the predictions can be updated in real time alongside events that transpire in the restaurant, and guidance can be passed on to crew members. 

Sensors act as eyes for the system. The system sees and considers all sorts of events such as customers entering the lobby, a school bus pulling into the parking lot, or a double batch accidentally getting cooked and thrown into the warm holding cabinet. Advanced sensors can gather information where eyes couldn’t, such as depth sensing or infrared cameras.

The sensors feed that information to the QSR Brain, which makes instantaneous calculations about whether more food will be needed in the next few minutes. It’s crunching far more data than a person ever could, and delivering a far more precise prediction in real time. The eyes and advanced sensors can be monitoring demand and inventory at the same time, and at the same time as it’s checking 10 other things in the restaurant. This flexibility and centralization bring big value to the restaurant.

30,000 Years’ Experience in the Industry

Extending beyond the four walls of the individual restaurant, the QSR Brain has the additional benefit of being in many restaurants at the same time and viewing essentially the same processes in each one. With a “federated learning” approach, the system may learn to do a task or make a calculation in one restaurant, and apply what it learned to the next 1000 restaurants. More locations drives accuracy towards 100%, with crew members benefiting from a wealth of knowledge learned in restaurants where they’ve never worked and never will. For a big chain with 10,000 locations and standardized ingredients, the system can effectively gain 27 years’ experience every day it’s on the job.

All of this data informs not only the AI algorithms, but management as well. The AI distills what it sees into KPIs (Key Performance Indicators), presented in an intuitive dashboard relevant to each management level: a GM can see the KPIs like throughput, waste, and freshness of their restaurant in real time, whereas a regional or corporate manager can see aggregated data for their region. The dashboard is easily configurable for any form of decision making, with both live and historical data.


Al Indig is an engineer and project manager at PreciTaste with personal experience in AI project implementation for F500 clients. Read his other articles here: