Little Caesars knows what you're going to order before you do. With AI tools, soon more restaurants will.

• AI tools are enabling some restaurants to get a jump on what customers want — and when.

• Little Caesars, Darden Restaurants, and Jack in the Box are each tapping into troves of sales data.
• The tech helps companies deliver faster, more-accurate orders with better service.
• This story is part of "Revolutionizing Customer Experience With AI," a series exploring the game-changing technologies driving customer interactions.

Normally it takes about 10 minutes to cook a pizza, but Little Caesars customers can now grab a fresh one in less time than they may have spent waiting for someone to answer the phone to take their order.

Using the brand's mobile app, a hungry dad can order a large pepperoni pizza (and a side of Crazy Bread, of course), pick it up from a nearby restaurant's heated Pizza Portal, and be on his way home for dinner.

"They don't have to talk to a cashier," Anita Klopfenstein, the chain's chief information officer, told Insider. "They key in their code, the doors open magically, they grab their food, and they're out the door in 30 seconds or less."

The world's third-largest pizza chain knows how many pizzas to keep on hand at any given moment, thanks to what Klopfenstein calls "pizza forecasting."

Analyzing five prior years of restaurant-level data with a machine-learning algorithm allows the company's CaesarVision platform to predict the exact amount of prepped ingredients and finished pizzas that each US location should have ready to go at any hour of every day.

These artificial-intelligence insights help the chain cut down on waste and improve the speed and accuracy of service, and other brands are keen to do the same.

The CaesarVision system is functionally similar to one from the foodtech startup PreciTaste that uses cameras and 3D sensors to tell restaurants such as Chipotle how much steak (and other burrito components) workers need to start prepping for the lunch rush.

"Technologies like PreciTaste that work alongside team members to help them operate even more efficiently are increasingly paramount to success," Danny Meyer, a restaurateur and investor, said last year.

Darden Restaurants, which owns full-service chains including Olive Garden and LongHorn Steakhouse, has a veritable trove of data it's starting to feed into AI and machine-learning algorithms to help its restaurants forecast guest counts and other patterns, CEO Ricardo Cardenas said during the company's earnings call in June.

He added that the company already performed an annual analytics review, which has helped it improve its business, and that he expected the benefits of better forecasting to flow through the entire company.

"If you forecast your traffic better, you order better, you receive better, you schedule better," Cardenas said.

Service is also better, Klopfenstein said, since the AI tools enable Little Caesars to hire more easily and train new employees more quickly — a big lift in a tight labor market. 

Where it previously took three to four days to orient a new hire to the point-of-sale system, it now takes about 15 minutes, she said. 

"We videotaped my husband putting in an order to show that you can take someone off the street and put them in front of our POS system and they can start taking orders right away," she added.

The burger chain Jack in the Box is investing big bucks to boost its point-of-sale system with AI models to help predict demand and recommend menu items for customers, CEO Darin Harris said earlier this year. Harris called the system "the central heartbeat of our organization."

"We think the restaurant-level technology will help us drive more efficient operations and drive cost out," Harris said.

At Little Caesars, restaurant-level data is processed within the store and fed up to the company's Microsoft Azure-hosted cloud platform to mesh with insights from the entire US operation, Klopfenstein told Insider.

She said this approach helped her team dynamically adjust to changing demands for computing power, which helps keep cloud expenses within budget.

Distilling the complexity of these systems into something so seemingly simple for employees and customers is critical to the brand's growth, Klopfenstein said: "Your entire goal is to make it seamless to the colleagues in the store — and, most importantly, your customer — because if it's difficult to use, they're not coming back."