McDonald's: a global restaurant AI partnership with Google Cloud
In December 2023 McDonald's and Google announced a multi-year deal to bring Google Cloud edge computing and AI to restaurants. No results data was disclosed.
On December 6, 2023, McDonald’s and Google announced a multi-year global partnership to connect Google Cloud hardware, data and AI technology to thousands of McDonald’s restaurants: edge computing to power platforms such as the app and self-order kiosks, Google Distributed Cloud deployed in restaurants, and generative AI applied to priority areas of the business.
At a glance
| Item | Detail |
|---|---|
| Company | McDonald’s |
| Industry | Retail |
| Use cases | Predictive maintenance, quality inspection, recommendations, marketing |
| Functions involved | Supply chain, operations, marketing |
| Reported results | No quantified benefits disclosed. The announcement describes a rollout plan: software and hardware upgrades to thousands of restaurants from 2024, covering the loyalty app with 150 million members and thousands of self-order kiosks |
| Moments rating | S (quick win) |
| AI scenario score | 96 |
Case details
Background
In the press release, McDonald’s describes itself as having more than 40,000 restaurants in over 100 countries, about 95% owned and operated by independent local franchisees. Its mobile app is the entry point to a loyalty program with 150 million members and growing, and its restaurants run thousands of self-order kiosks. On December 6, 2023, McDonald’s and Google announced the multi-year global partnership simultaneously from Chicago and Sunnyvale.
What they did
- Edge computing in restaurants. Google Cloud’s edge computing will power new restaurant and customer platforms, putting data storage and high-performance computing inside individual restaurants.
- Google Distributed Cloud. This combined hardware and software product is planned for thousands of restaurants, letting them use cloud applications and run McDonald’s own software and AI locally.
- Equipment and operations. Edge capabilities give visibility into how equipment is running, to reduce business disruptions and make work less complex for restaurant crews.
- Generative AI. Google Cloud set up a dedicated team in Chicago, close to McDonald’s global innovation center Speedee Labs, to apply generative AI across several priority areas.
Brian Rice, McDonald’s executive vice president and global chief information officer, said connecting restaurants to millions of data points across the digital ecosystem would make tools more useful, models smarter and restaurants easier to operate.
Results and data
The press release announces a partnership; it reports no post-launch results such as scheduling efficiency, equipment downtime or order accuracy. What it confirms is the plan: McDonald’s will be the largest restaurant retailer worldwide to use new Google Distributed Cloud capabilities, and thousands of restaurants are slated for software and hardware upgrades starting in 2024. Earlier claims of “85% less scheduling time”, “7,000+ restaurants in China”, “300 million+ members in China” and an “in-car AI ordering agent” do not appear in the source.
What to take from it
- Unify the foundation before the applications. McDonald’s standardized restaurant computing and data infrastructure before talking about generative AI. Chains with many locations and uneven connectivity can start by deciding which workloads run locally and which in the cloud.
- Start with equipment data. Monitoring equipment and reducing downtime is easy to quantify and lower risk than launching a customer-facing conversational app.
- Co-locate joint teams. Putting the vendor team in the same city as the company’s innovation center speeds up communication.
Limits
- This is a partnership announcement, not a results review, and cannot support an ROI case.
- The approach depends on an overseas cloud vendor’s hardware and software; companies under strict data rules may need a local cloud or on-premises deployment.
- Standardized edge hardware pays off at thousands of locations. Smaller chains need to model costs and returns separately.
Source
Original source: McDonald’s case material (McDonald’s corporate)
This entry is industry research: a publicly documented, third-party AI case. It is not a Moments client case. For work Moments delivered itself, see client cases.
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