The impact of AI in QSR operations

Artificial Intelligence (AI) has changed the essence of the way business is conducted in the Quick Service Restaurant (QSR) industry, and it isn’t done at this point. With its capacity to give real-time business intelligence quickly and efficiently, this innovation has opened better approaches to exploit the massive data sets produced across businesses. In our earlier blog “How AI is redefining the guest experiences in Quick Service Restaurants?”,  we looked at the impact of AI on guest experiences.

AI is spreading its magic when it comes to operations in the quick service restaurant industry, which have yearly sales adding up to $799 billion every year. A few years back, fast food and other QSR operations were not known as centers of technological innovation. Today, fast food companies have been making significant investments in emerging technologies such as AI to increase revenue and efficiency. Presently QSR operations are getting intelligent, with companies deciphering and orchestrating business activities and performance on a daily basis. This doesn’t stop here – companies can now navigate from a broad perspective all the way down to a single data point analysis for yielding valuable conclusions and actionable insights in areas such as fraud detection, safety and compliance, loss prevention, employee scoring, and location performance scoring to name a few.

With the prowess of AI technology, quick service restaurant industry can achieve new financial success from the insights that offer concrete competitive advantages.

If you’re as intrigued to know some of the top use cases, we have put together some of the latest innovations and trends in QSR and distilled them down here in this blog.


  1. Loss prevention by fraud detection

Internal and external frauds carried by the restaurant’s employees or third parties can cause a visible dent in the business’s bottom line. The National Restaurant Association estimates that employee theft and fraud accounts for around 75 percent of restaurant losses. Managing fraud is a continual process, and you need the tools to record events and the data to keep a track of unresolved issues. For example, delivery errors, refund issues, discount misuse, wrong transactions or multiple transactions, and gift card fraud – all account to the losses. External frauds by customers in the form of false complaints and chargebacks are common too.

AI models and well-designed algorithms can detect most anomalies in daily transactions and everyday operations such as thefts, frauds and misrepresentations by staff. These algorithms can be trained to look for minute details deep into the system spanning gaps, erases, and discounts, and then drill it down by time and day, department, employee, the product sold. The algorithm can drill down the activities by specific area and specific employee/department. These anomalies can then be verified through video supervision or any third-party intervention.


  1. Employee scoring to improve performance 

As the AI models intake input from numerous POS terminals across a restaurant network, they can synthesize the data to examine employees’ performance specific to each piece of data. This allows the algorithm to analyze errors, track transactions, monitor customer service and cross-selling, ratings given by customers or peers, and measure the overall employee performance.

AI plays a significant role in comparing different employees and scoring them on performance. However, such employee comparisons can be complex as they can vary by location, time, week, and even POS terminal used. This is where AI clustering can help combine various employee transactions, comparing employees on level ground. The outcome of this helps discern those underperforming employees and highlights those who are performing above standard.


  1. Deciphering the location performance

Location performance can uncover the locations in a restaurant network and other probable distinct features such as finest and worst location, queue time, and compliance. This predictive modeling primarily uses data-driven scoring to analyze items sold, quantity, and exact location.

Location performance of a restaurant chain can also present reports that analyze store traffic and queue as per different business metrics, allowing for targeted promotions. With these models, you can quickly formulate different KPIs according to business needs and on the fly.


  1. Effective order management 

AI-driven recommendation systems utilize online orders from the application and data from past purchases to predict what customers may order in the future. McDonald’s, for example, has set up self-ordering kiosks, giving customers complete control over their order, including any alternates or specifications they need. Self-ordering kiosks can aid the restaurant in reducing the number of attendants taking orders and let employees free to provide efficient table service.

Likewise, having an AI-powered conversational chatbot reduces the need to monitor telephone orders and order-related queries, regardless of whether it operates as an individual restaurant or a more centralized call center. Your AI application helps take orders and effectively manage them, removing your dependence on calls and additional resources. Ultimately, it offers better customer service and leaves customers with a great experience which can help the restaurant get repeat customers and more sales.

The QSR industry can utilize predictive analytics techniques to forecast sales and plan for what’s ahead. With a customer data platform, coupled with AI and machine learning can help decipher data patterns to construct more targeted marketing campaigns, deliver desired customer experience, and improve menu planning and pricing. AI can further analyze and categorize items through NLP algorithms to learn patterns and issues past the individual item level. These are great tools for making personalized customer interactions and enhancing customer satisfaction.

Tech4TH, a global digital solutions and services company is well-positioned to add customer value using its expertise in crafting digital solutions powered by AI. Tech4TH combines its expertise in the latest technologies, domain knowledge and consulting to deliver intelligent engagement to enterprises in hospitality.

Whether small or big, QSR and the food industry strive to make their operations smarter and improve their brand reputation built over the years. A comprehensive data platform, artificial intelligence and other technologies like automation will transform the way the food industry delivers superior customer experience and simplifies operations.