Mcdonald's AI Case
Ryan Owen holds an MBA from the University of South Carolina, and has rich experience in financial services, having worked with Liberty Mutual, Sun Life, and other financial firms. Ryan writes and edits AI industry trends and use-cases for Emerj's editorial and client content.
Dick and Mac McDonald opened the first McDonald’s restaurant in San Bernardino, California in 1940. By the end of the decade, the restaurant added its now-famous French fries. Ray Kroc joined the growing organization in 1954, purchased it in 1961, and served as its CEO into the early 1970s. Over the next decades, the restaurant chain grew, adding its drive-thru concept, Hamburger University, and iconic menu items like its Filet-O-Fish, Big Mac, and Quarter Pounder sandwiches.
Today, McDonald’s is the world’s second-largest restaurant company by revenue and its largest fast-food restaurant chain with more than 39,000 locations in 119 countries. As of January 2022, McDonald’s trades on the NYSE with a market cap of approximately $200 billion. For the fiscal year ended December 31, 2021, McDonald’s reported total revenues of over $22 billion.
McDonald’s created McD Tech Labs after its 2019 acquisition of Apprente, a voice technology firm, which in turn followed its earlier acquisition of Dynamic Yield, a Tel Aviv-based firm that specialized in personalization and decision logic technology.
In 2020, McDonald’s announced its Accelerating the Arches growth strategy, which includes a “double down on the 3 D’s: Digital, Delivery and Drive-Thru,” and the integration of these newly acquired AI technologies.
In this article, we’ll look at how McDonald’s has explored AI applications for its business and industry through two unique use-cases:
- Automated Drive-Thrus — McDonald’s acquired natural language processing and machine learning expertise through its 2019 acquisition of Apprente, which the chain has applied to its automated order-taking initiatives.
- Order Prediction — McDonald’s uses machine learning-based decision technology to predict what menu offerings are most likely to drive sales in its drive-thru business.
We will begin by examining how McDonald’s uses natural language processing and machine learning technology to speed its order-taking process as its drive-thru business has become more popular during the pandemic.
Automated Drive-Thrus
Since the introduction of its first drive-thrus in the 1970s, McDonald’s has grown to rely on the model to attract and serve its guests. As of December 2021, McDonald’s offers drive-thru service in some 25,000 locations across the world, including at 95% of all US operations. Valued by consumers for its convenience and flexibility, the drive-thru model has taken on even more importance during the COVID-19 pandemic, representing some 70% of sales in top markets since March 2020.
“Our drive-thru sales across our top six markets continue to stay elevated versus pre-pandemic levels even as dining rooms reopened,” said Chris Kempczinski, the chain’s president and CEO during its Q3 2021 earnings call.
In addition to implementing drive-thru innovations like express pick-up, express drive-thru, and smaller “on-the-go” locations, McDonald’s is also looking to improve on its ordering and payment functions.
The company made its biggest jump into innovating the order and payment components of the drive-thru process by beginning to automate drive-thru order-taking in 2018, and with its 2019 acquisition of Apprente. Later renamed McD Tech Labs, the acquisition enabled McDonald’s to implement and test automated order-taking at several of its US-based restaurants, Kempczinski said in October 2021.
Apprente technology, per a Q3 2019 McDonald’s press release is a “voice-based [platform] for complex, multilingual, multi-accent, and multi-item conversational ordering.” McDonald’s hopes that using the solution’s natural language processing and machine learning capabilities will create a faster, simpler, and more accurate order-taking process at the company’s drive-thrus.
Here Mason Smoot, McDonald’s Chief Restaurant Officer for the US, explains McDonald’s plans for its Automated Order-Taking technology at the Company’s Worldwide Connection 2.0 (WWC) Meeting:
Since 2018, McDonald’s reports that the chain has cut the time it takes to serve drive-thru customers by thirty seconds and claims that this has resulted in increased customer satisfaction. McDonald’s has announced plans to expand its drive-thru “innovations” to more than 10,000 locations worldwide.
Automated order-taking boasts an 85% accuracy rating, Kempczinksi said at the Alliance Bernstein’s Strategic Decisions conference, adding that about 20% of orders have required human intervention.
In its most recent earnings call, the chain reports substantial benefits for both customers and its employees, and has announced plans to continue to scale the technology to more than 14,000 locations across the country.
To help drive that growth, McDonald’s entered into a “strategic relationship” with IBM in Q4 2021 to tap into their expertise in “building AI powered customer care solutions and voice recognition,” Kempczinski continued during the Q3 2021 earnings call. As part of the deal, automated order taking “will continue to be integrated into McDonald’s highly secure technology ecosystem.”
Order Prediction
Digitization is coming to the drive-thru. As we drive around—and go inside—McDonald’s restaurants, we are increasingly met with digital displays, and those displays are growing smarter.
In the United States alone, McDonald’s serves more than 25 million customers daily. In the chain’s top markets, some 70% of sales come through the drive-thru. As part of its Accelerating the Arches effort to make the drive-thru as efficient and convenient as possible for its customers, McDonald’s purchased Dynamic Yield in 2019, planning to leverage its decision technology to add personalization to its drive-thrus and modernize the customer experience.
When McDonald’s added Dynamic Yield’s machine-learning-driven solution to its drive-thrus at select locations in 2018, the tech considered purchases made by other customers when updating the offerings presented on the ordering displays. The technology also updated the digital drive-thru menu displays based on:
- The time of day
- Current order selections
- Current restaurant traffic
- Popularity of menu items
- Weather conditions
Here, at McDonald’s November 2020 Virtual Investor Update, Mason Smoot, McDonald’s Chief Restaurant Officer for the US, explains the company’s vision for its Drive-Thru strategy:
Through the machine-learning technology acquired in the Dynamic Yield transaction, McDonald’s digitized its point-of-sale process at its brick-and-mortar locations with decision technology. After testing the digital displays in several domestic markets in 2018, McDonald’s announced plans to continue its rollout to more US and international locations in 2019.
With Dynamic Yield’s machine-learning-driven decision technology, McDonald’s now offers digital displays that suggest items popular with other customers, or that are frequently purchased in certain weather or during a specific time of day. These digital displays can also suggest faster-to-prepare items to alleviate drive-thru slowdowns.
Rumors first emerged in early 2021 that McDonald’s was considering selling its Dynamic Yield business. Possible reasons included disagreements about $70 million in technology fees that McDonald’s said were not paid by its franchisees.
Later in 2021, in December, McDonald’s announced plans to sell Dynamic Yield to Mastercard. Although Dynamic Yield, with its portfolio of more than 400 global brands, will become part of Mastercard’s digital service strategy, Mastercard, through its President of Data & Services, stated that it plans to “continue working with McDonald’s.”
While McDonald’s does not provide separate financial performance information for its Dynamic Yield business, the company does plan to continue using the technology in its bid to further personalize its customer experience and, at the time of the Dynamic Yield sale, reiterated its plans to “further scale and integrate Dynamic Yield’s capabilities globally and across ordering channels.”
ML and AI Case Study : McDonald’s Goes All-In on Machine Learning
What is Machine learning ?
Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.
Deep Learning and Modern Developments in Neural Networks
Deep learning involves the study and design of machine algorithms for learning good representation of data at multiple levels of abstraction (ways of arranging computer systems). Recent publicity of deep learning through DeepMind, Facebook, and other institutions has highlighted it as the “next frontier” of machine learning.
Types of ML
What is Artificial Intelligence ?
Artificial intelligence (AI) is wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. AI is an interdisciplinary science with multiple approaches, but advancements in machine learning and deep learning are creating a paradigm shift in virtually every sector of the tech industry.
🍟 How McDonald’s is using AI & ML :
Over the last twelve months, the fast-food chain has spent hundreds of millions of dollars to acquire technology companies that specialize in artificial intelligence and machine learning. McDonald’s has even established a new tech hub in the heart of Silicon Valley — the McD Tech Labs — where a team of engineers and data scientists is working on voice-recognition software.
Central to McDonald’s and really any fast-food business is the need to keep costs low and efficiencies high — something big data, artificial intelligence and robotics can support.
The acquisition is the latest data and analytics venture from McDonald’s, which has been investing heavily in digital transformation since 2015. The company paid $300 million for Dynamic Yield — which may sound big, but it’s nothing compared to the fast food chain’s market capital of $143.5 billion.
Data Volume and Augmentation
The sheer volume of McDonald’s data is amazing. Every day, 68 million customers visit one of McDonalds’ 38,000 retail locations — and the majority of them do not get out of the car. So, the question becomes: How does one service these drive-in customers with more AI-driven personalization?
Consider the data McDonald’s can use to improve personalization:
- Historical sales data at each of their franchises
- External augment data such as weather, traffic, nearby events or activities, and Census data
- Day of the week/time of day stats
- Customers’ past purchases
- Trending items
- Location information
What can McDonald’s do with all this information?
Personalised and improved customer experience
Not only can customers order and pay through the McDonald’s mobile app and get access to exclusive deals, but when customers use the app, McDonald’s gets vital customer intelligence about where and when they go to the restaurant, how often, if they use the drive thru or go into the restaurant, and what they purchase. The company can recommend complementary products and promote deals to help increase sales when customers use the app.
There might be a way to identify the customer who is driving in in order to provide a custom personalized menu for that patron. Maybe it’s based on geo-fencing in their app, or on identifying the license plate number using image-based deep learning algorithms like Convolutional Neural Networks (or CNNs).
If the line is moving fast, maybe change the menu accordingly. If the checkout line is long, maybe change the menu to only items that are faster to prepare.
Kiosks and interactive terminals
As one solution to the increasing costs of labour, McDonald’s is replacing cashiers in some locations with kiosks where customers can place their order on a digital screen. Not only are labour costs reduced, but the error rates go down.
Packaging the End-to-End Solution Is the Key
The hard part isn’t building the above predictive models. It’s taking all the individual pieces — from raw data to cleanup, to building out the model, predicting, and distributing the predictions to user interfaces, and letting them take action. How the company packages the end-to-end workflow to make use of data and create intelligent predictions that drive actions — that is the value they can create.
Once McDonald’s starts rolling out this personalized behavior, and assuming it works well, it’s going to make customer ordering so easy. Customers will understand how to use AI-enabled systems and see the value of machine learning — and soon, they’ll start demanding similar systems from other brands. With more and more retail chains beginning to look for such solutions, this move is an early indication of how AI will revolutionize the retail space.
References:
https://emerj.com/ai-sector-overviews/artificial-intelligence-at-mcdonalds/
Ethical and Legal Use of Data
McDonald’s announced earlier this month that it was deploying an AI chatbot to handle its drive-thru orders, but it turns out it might break privacy law.
The chatbot is the product of a voice recognition company McDonald’s snapped up in 2019 called Apprente which is now known as McD Tech Labs.
McDonald’s deployed the chatbots to ten of its restaurants in Chicago, Illinois. And there lies the issue.
The state of Illinois has some of the strictest data privacy laws in the country. For example, the state’s Biometric Information Privacy Act (BIPA) states: “No private entity may collect, capture, purchase, receive through trade, or otherwise obtain a person’s or a customer’s biometric identifier or biometric information.”
One resident, Shannon Carpenter, has sued McDonald’s on behalf of himself and other Illinois residents—claiming the fast food biz has broken BIPA by not receiving explicit written consent from its customers to process their voice data.
“Plaintiff, like the other class members, to this day does not know the whereabouts of his voiceprint biometrics which defendant obtained,” the lawsuit states.
The software is said to not only transcribe speech into text but also process it to predict personal information about the customers such as their “age, gender, accent, nationality, and national origin.”
Furthermore, the lawsuit alleges that McDonald’s has been testing AI software at its drive-thrus since last year.
Anyone found to have had their rights under BIPA violated are eligible for up to $5000 each per case. Given the huge number of McDonald’s customers, it’s estimated that damage payouts could exceed $5 million.
Once again, this case shows the need to be certain that any AI deployments are 100 percent compliant with increasingly strict data laws in every state and country they operate.
An Appetite for AI at McDonald’s
Craig Brabec, the fast-food giant’s chief data analytics officer, discusses how the organization is using AI to boost customer experience and optimize internal operations, among other successes.
As one of the most recognizable brands in the world, McDonald’s is always seeking ways to provide faster, better service to its loyal customers—and data and AI may offer an opportunity like none other.
“The scale of opportunity is tremendous because we serve approximately 65 million customers every day during every part of the day,” says McDonald's Vice President and Chief Data Analytics Officer Craig Brabec, who has more than 25 years of experience in corporate strategy and data analytics and was previously director of global data insights and analytics at Ford Motor Company.
“For instance, we’ve found that customer demand patterns since the COVID-19 pandemic have shifted considerably, including changes in McDelivery and Drive Thru, so we can quickly recognize and make the most of that opportunity through data analytics and AI technologies,” says Brabec, who in his role helps define and infuse data across the global enterprise and establish best-in-class strategy and governance.
In this “AI From the Front Lines” interview, Brabec recently spoke with Beena Ammanath, executive director of the Deloitte AI Institute, about the iconic fast-food brand’s data-driven AI journey and the challenges and myths organizations must overcome on their path to AI success.
Ammanath: Craig, we both use AI with our families in our personal lives. My younger one has entire conversations with voice-activated devices and he’s so polite! But as a business executive, you’ve lived and breathed the AI journey for nearly two decades of your professional life. These days, nearly all organizations use data-driven AI technologies to improve efficiency, while mature adopters are also harnessing it toward boosting differentiation, according to Deloitte’s most recent State of AI in the Enterprise report. What do you see as the biggest business opportunities for AI and data analytics today?
Craig Brabec
Brabec: I’ll start by agreeing that AI has been transformational in my family, especially for my daughter with special needs. AI has allowed her to access her music independently in a completely frictionless way.
In addition to AI’s personal effects on my family, data-driven AI has the potential to allow McDonald’s to offer a faster and more personalized customer experience. To be honest, speed and convenience have always been in our McDonald’s DNA. We’ve put a lot of work into our delivery and Drive Thru areas of the business and saw some of that pay off during the COVID-19 pandemic.
But the business opportunities for AI and data analytics go beyond customer experience at McDonald’s. We also take a full enterprise view of internal AI opportunities related to supply streamlining, restaurant operations, inventory management, demand planning, and equipment repair before failure, among many others.
Now, as AI use expands, even skilled adopters are recognizing the existence of a “preparedness gap” that can span strategic, operational, and ethical challenges, per the State of AI report. With this in mind, what AI challenges should business leaders consider?
A common challenge is recognizing that we are still in the early stages of AI. It’s not a silver bullet: It requires a high volume of quality data, solid analytics capabilities, and strong business applications. When you adopt AI, you often have to create the data, train models, and do labeling. Sometimes the solutions are still fairly linear and can be brittle and difficult to flex when the models evolve.
It’s also important to note that narrow AI still dominates today’s solutions, which for the most part address very specific problems under a defined set of operating conditions. With new technologies and developments the data science community will be able to go after wider opportunities, but for now, it’s still quite narrowly focused.
At McDonald’s, we’re addressing this preparedness gap with our talent—from how we hire to how we ensure our employees have the tools they need to build up AI acumen within their functions or markets. We are not naïve about the limitations of AI, so it’s important that we think beyond current constraints and continue to innovate to meet our customers’ needs.
Beena Ammanath
That’s very true. According to Deloitte’s 2021 Tech Trends report, many organizations experimenting with AI report feeling hamstrung by clunky, brittle development and deployment processes that can stifle experimentation and hinder collaboration between product teams, operational staff, and data scientists.
Another challenge I see all the time is establishing trust in AI. The increased use of deep learning techniques has caused people to demand explanations of AI models and focus on ethics. People don’t want AI to be a black box. Instead, they really want explainability—to understand what is driving recommendations. That’s a good problem to have, though, because it shows that people see the value in making business decisions driven by data analytics. So the challenge then becomes helping them to understand what is driving those recommendations.
But do you think there can be a playbook when it comes to ethics in AI? After all, explainability may mean different things for different people and different industries. Deloitte’s Trustworthy AI framework, for example, includes six different dimensions to build trust in AI, including assessing whether AI systems are fair and impartial and putting policies in place that clarify who is responsible for the output of AI systems’ decisions.
I think the demand for explainability is a good thing because it ensures we are having a discussion about AI and using it appropriately. But in many ways, trust comes from demystifying things: We’re continuing to educate our professionals and business leaders about what the models are actually doing and how they work. Our team at McDonald’s operates in a hub-and-spoke model; the spokes include our global markets, our various global and market functional departments, and initiatives such as digital, Drive Thru, and delivery.
“I see AI gaining traction in every industry, creating value for individuals and society.”
I’ve seen companies organize their AI teams very differently, depending on their maturity in this area. I wanted to highlight how different companies are succeeding by organizing in a variety of ways, especially if they are just getting started. How are your teams set up at McDonald’s?
I absolutely agree that one size doesn’t fit all. On the one hand, I think understanding how to get AI capabilities to the front lines is essential. On the other hand, there are areas that you want to be strategically aligned and you need a central core. At McDonald’s, our operations are fairly decentralized to help support our franchise model, so we need to be thinking about AI in that way. At the same time, we are starting to expose the broader McDonald’s organization to AI and are working to share this thinking throughout the organization.
Of course, AI challenges can also stem from misaligned expectations, particularly around myths that have been perpetuated about what these technologies can do. What is the biggest myth you have heard about AI?
I think the biggest myth is the idea that AI will follow the traditional hypercycle of technologies and that its development will eventually slow down. Those who might say the next “AI winter” is coming are not typically on the front lines and understanding the transformations taking place.
I see AI gaining traction in every industry, creating value for individuals and society as a whole. There are huge volumes of quality data available to enable AI models, and we at McDonald’s are increasingly smarter about improving business acumen across the organization.
Right. I hear a lot about the “AI winter” in Silicon Valley, in the context of ethics and explainability and transparency—that is, should they even be doing this without having all the guardrails in place? I wonder how it will all shake out. That said, I completely agree that, in the non-Big Tech world, there is tremendous opportunity and transformation. In that regard, what do you wish you had known before you started your AI journey?
I have recognized repeatedly the importance of starting with a clear definition of the central question and the business opportunity. The insights and learnings on the AI journey may cause directional adjustments to that central question as you uncover the truth from the data, but keeping it as your beacon is critical. Avoid having the pursuit of business value turn into simply a lab experiment with new technology—be ready to adjust course and stay committed. The promise of AI is real, but as you explore the data, the findings may surprise you.
I love it. I know you do a lot of work externally as well to help democratize AI and take others along on the data literacy journey.
Yes. That doesn’t mean making everybody a data scientist, but it calls for establishing data literacy throughout McDonald’s. In addition, we want to expose the community to what we’re doing and how we’re doing it. We work with numerous nonprofit organizations to get younger generations interested in this space—not just as a technology or science thing but as something that is pervasive in their lives. As an AI community, let’s pay it forward.
Editor’s note: This is the first in the series “AI From the Front Lines,” which will go beyond the hype to reveal the opportunities and challenges enterprises can realize through AI and data analytics, featuring the real-world experiences of enterprise executives in conversation with Beena Ammanath, executive director of Deloitte’s AI Institute.
This publication contains general information only and Deloitte is not, by means of this publication, rendering accounting, business, financial, investment, legal, tax, or other professional advice or services. This publication is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified professional advisor. Deloitte shall not be responsible for any loss sustained by any person who relies on this publication.
About Deloitte: Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee ("DTTL"), its network of member firms, and their related entities. DTTL and each of its member firms are legally separate and independent entities. DTTL (also referred to as "Deloitte Global") does not provide services to clients. In the United States, Deloitte refers to one or more of the US member firms of DTTL, their related entities that operate using the "Deloitte" name in the United States and their respective affiliates. Certain services may not be available to attest clients under the rules and regulations of public accounting. Please see www.deloitte.com/us/about to learn more about our global network of member firms. Copyright © 2021 Deloitte Development LLC. All rights reserved.
https://deloitte.wsj.com/articles/an-appetite-for-ai-at-mcdonalds-01612382529
Are McDonald’s AI-Powered Drive-Thrus The Future?
Ah yes, the direction we’ve all known the world was headed in seems to accelerate upon us quicker each month. In the last quarter of 2021, McDonald’s decided to partner with IBM, by giving them access to their McD Tech Labs, in the hopes of creating a better AI-powered, automated drive-thru experience.
After some initial success over the summer, the goal in question is whether or not this is something that makes sense to expand. The original trial run was tested at just 10 locations in the Chicago area. McDonald’s currently operates 13,000+ locations in the US alone, so obviously this sample represents but a tiny fraction of the McD’s empire.
My initial thought when I first heard about this was, “As a customer, would I personally like this?” This AI drive-thru concept has my brain bouncing around a few ideas on whether or not this is a smart idea from both a business standpoint and also a customer experience perspective.
On the business front.
For McDonald’s, it seems this implementation of AI in the drive-thru would make sense. At a time when the hiring market remains quite turbulent, companies that utilize technological advantages can continue their productivity progress as well as create an edge over their competition. In this case, tech can help supplement the lack of employees where it may exist. One can imagine the cost savings implemented here.
Particularly in this pandemic, as employees both leave for other work opportunities, and unfortunately even become sick and need to take leave, one can envision AI as a great solution for both improving customer sales in relation to operations speed as well as improving employee safety by having less staff interaction between each other as well as customers. Win-win here.
“Business owners across the industry say they're unable to find staff and in some cases even cite a lack of desire to work, while workers say they can demand better pay and benefits in the tight labor market.” (Business Insider)
A question companies must address in the future will be how to create new opportunities, as well as incentives, for workers as AI undoubtedly takes over various areas of operations. Perhaps pay increases will become more commonly injected into the equation. McDonald’s is headed in the right direction here. “The company says…the average wage for employees at company-owned restaurants will be $15 per hour by 2024.” (CNBC). Other companies like Starbucks are well known for providing excellent health benefits and education opportunities for its workers. Making positive strides is also Raising Cane’s. “The biggest pull that Raising Cane’s has is their opportunity for advancement. Workers can quickly move through the ranks to become a trainer or shift lead which means more money.” (foodfornet.com). If employees feel more encouraged and able to acquire additional skill sets, they can fill new roles within an organization, providing value to the company.
The initial McDonald’s reports came back that the AI voice recognition drive-thru got customers’ orders correct somewhere around the 85% mark (CNBC). So the thought now is, what happens to the 15% of orders that don’t suffice? How much does this cost McDonald’s in terms of lost profit? Does it offset the savings of having fewer workers? Do customers get angry and go elsewhere? After all, in the age of fast food, there are most likely other burger places nearby.
Another potential negative McDonald’s has to consider is the price it may cost to expand this technology. It could add up to quite a pretty penny in order to roll out the technology to its thousands upon thousands of locations. And of course, let us remember that tech is constantly evolving and can break along the way, so what would this cost in terms of maintenance and upkeep?
"Now there's a big leap from going to 10 restaurants in Chicago to 14,000 restaurants across the U.S., with an infinite number of promo permutations, menu permutations, dialect permutations, weather — and on and on and on.” (McDonald’s CEO Chris Kempczinski via Business Insider)
From the customer perspective.
I can think of both positives and negatives of this service from this angle as well. On the positive front, I think about how long drive-thru lines have been over the past 2 years. Driving around during dinner time, you’ll easily see the wildly long lines of vehicles at every single fast food restaurant you pass, some stretching all the way into the highway, into the path of oncoming traffic!
I myself have waited in line at a Starbucks drive-thru, realized I didn’t have a great deal of time to sit there, then drove off, ultimately making myself a coffee at home. Due to AI speed enhancements at a drive-thru, I can imagine a world where several lanes exist instead of the traditional 1 or 2. Think of a system like your local bank where there are sometimes 4 or even 5 lanes one can drive up to for service. Think how effective this could be at a drive-thru restaurant if the majority of the experience was automated and, if for the most part, accurately executed. One could get in and out quickly and on with his / her day? How grand! After all, isn’t that what fast food is all about?
Perhaps if customers were given a choice of what kind of ordering experience they could receive upon driving up, that would be another great perk. I think about my local CVS which has 2 in-person checkout counters and 4 self-service checkout machines. 99% of the time, I personally opt for the self-service option because I usually have the items I need and just want to scan them and exit the store. One could envision a similar experience at a drive-thru establishment.
Throughout the pandemic, many companies have created alternative options to satisfy their customers. Several big-box stores like Whole Foods and Target have done exceptionally well at mastering the curbside pickup option. Many customers enjoy this new way to shop. The more customers you can make happy, the better.
A few other potential negatives here from the customer angle:
What if I am the kind of person who desires to hear an actual human’s voice on the other side of that speaker instead of a robot? Can I have that interaction if I want? (Chris Matyszczyk at ZDNet isn’t sold just yet)
What if I am a less tech-savvy individual and have trouble using the automated system? The fact that McDonald’s serves millions of customers a year means that not everyone pulling up to the drive-thru will be monthly subscribers to the latest tech magazine. In short, McDonald’s customer base is about as broad as any out there.
“Kempczinski said the restaurants using the voice-ordering technology are seeing about 85% order accuracy. Only about a fifth of orders need to be a [sic] taken by a human at those locations, he said, speaking at Alliance Bernstein’s Strategic Decisions conference.” (CNBC)
What if I am one of those customers in the 15% range, whose order isn’t done correctly and must be elevated to a human and explained? Now it actually takes even more time to complete my order, fueling my frustration.
The future will lead the way.
Taking all these factors into account, McDonald’s and other businesses will have to decide if the pros outweigh the cons, both from a revenue standpoint, as well as the customer experience perspective. With this technological drive-thru idea being, shall we say, relatively newish, only time will tell if it makes sense to incorporate this AI service on a larger scale. Like so many things in customer service, it’s going to come down to - does the customer like this, do they want it, do they feel it improves their overall experience? The answer to this question will most likely drive if, and how, McDonald’s and other drive-thru businesses proceed into the future.
https://www.customercontactweekdigital.com/tools-technologies/articles/are-mcdonalds-ai-powered-drive-thrus-the-future
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