How generative AI is changing the quick service restaurant industry
Every industry, including the quick service restaurant (QSR) market, plans to transform its business with artificial intelligence (AI), especially generative AI. Several years ago, Wendy’s began its AI journey, leveraging cloud computing services and generative AI to improve employee and customer experiences. The drive-through experience presents numerous challenges for QSR restaurants due to the complexity of menu options, limited time offers, special requests and ambient noise.
Wendy’s chose to handle the drive-thru experience with AI because 75 to 80 percent of Wendy’s customers choose the drive-thru as their preferred ordering channel. The company saw a tremendous opportunity to improve the customer experience by creating a seamless ordering experience using in-car AI automation.
Matt Spessard, CIO of Wendy’s
In an interview with Maribel Lopez of Lopez Research, Wendy’s CIO Matt Spessard shared how his AI program has advanced over the past year and shared advice for other leaders looking to tackle AI within their business. In 2024, Wendy’s announced an expansion of its partnership with Google Cloud, using Google’s AI technology and resources to enhance its generative AI platform called Wendy’s FreshAI. Wendy’s Fresh AI aims to address challenges that traditional AI couldn’t solve, such as understanding casual conversations and handling Wendy’s extensive menu customizations. For example, the back-and-forth nature of conversations is a very complex technical challenge for AI. It also added Spanish as a language option in 2024.
Traditional AI rule-based chatbots were not the answer because they cannot easily support the diverse and dynamic nature of natural conversations. For example, Wendy’s realized that there were over 200 billion combinations of words and options to order a Dave’s Double. Additionally, it can take years of development and tremendous work to maintain, modify, and extend the capabilities within these more rigid rules-based solutions. Today, Wendy’s uses generative AI to interpret conversations, create responses and adapt in real time rather than following a narrow set of rules.
While many discussions of AI focus on job loss issues, Wendy’s shared that its AI efforts also benefit its employees. Wendy’s FreshAI works alongside restaurant teams, eliminating ordering issues while empowering crew members to focus on preparing and filling orders efficiently.
How does Wendy’s measure AI success and return?
Wendy’s takes a pragmatic view of what AI success means. Its efforts are focused on providing speed, accuracy and consistency to customers. It measures these results with metrics such as the number of orders delivered without human intervention and the consistency of the customer experience on the go. What have been the results so far? The percentage of orders successfully handled by Wendy’s FreshAI without intervention from restaurant team members averaged 86%, and she expects that average to rise.
One test location showed service times 22 seconds faster than the Columbus, Ohio market average. Wendy’s noted that other QSR companies define “accuracy” as any order initiated by the AI assistant and delivered to the point-of-sale system, including orders where a crew member joins the conversation to correct an inaccuracy. Wendy’s shared that if it uses that broader definition of accuracy, its FreshAI success rate reaches nearly 99%.
What’s next for AI at Wendy’s
Wendy’s FreshAI has moved from pilot to production. It is now available in nearly 100 restaurants in 17 Spanish-speaking countries. There are still tremendous upsides to expanding technology deployment as Wendy’s operates over 7,000 restaurants worldwide. Going forward, Wendy’s sees the Fresh AI assistant as a platform that will expand across various ordering channels, such as mobile apps, kiosks and smart devices.
Tips for handling AI innovation?
Wendy’s CIO Spessard said the company learned many lessons along the way. He provided the following top tips for organizations looking to adopt AI:
- Identify the right use case and technology. Wendy’s points out that organizations should think pragmatically about the use cases where AI can provide the most value and then choose the right AI technology (eg, generative AI, traditional machine learning) to tailored to that specific need. Artificial intelligence is a broad spectrum of technologies, so a “right tool for the right job” approach is essential.
- Start with small-scale experiments. Spessard recommends that companies start with small-scale experiments within the organization, exposing employees to AI capabilities. This exposure helps organically grow the use of AI as more use cases are identified. Spessard said Wendy’s had to resist the urge to deploy too widely and too quickly. Iterating in the early stages of its AI deployment helped Wendy’s achieve the desired accuracy and consistency.
- Emphasize continuous improvement. Wendy’s emphasized the importance of a feedback loop, constant iteration, and a mindset of even 1% improvement every day. It regularly analyzes customer and employee feedback, allowing Wendy’s to refine aspects such as tone, pace and expression in Wendy’s FreshAI. These improvements were critical to improve the performance of the AI assistant.
- Engage stakeholders early and often. Wendy’s found it very valuable to regularly conduct demonstrations of the AI assistant’s capabilities and progress to key stakeholders as franchisees. These demonstrations helped build trust and support for the initiative as stakeholders could see improvements over time. Additionally, Wendy’s noted that the rapid democratization of AI technologies is making it easier to manage change, as employees are already familiar with using AI in their personal lives, setting the expectation for it in the workplace as well. work.
A key takeaway from the discussion was basing AI innovation on iterative improvement, continuously engaging employees and partners in the process, and cultivating a willingness to experiment and learn rather than trying to perfect the technology before deployment.
