This is a research prototype. The data and analyses are preliminary and not yet validated — we'd welcome your .
Between 2021 and 2024, McDonald's piloted an IBM-developed AI voice ordering system at over 100 U.S. drive-thrus. The system suffered from significant accuracy issues, including misinterpreting orders, merging orders from adjacent lanes, and adding incorrect items, which were widely documented in viral social media videos. Due to these performance failures and accuracy rates below the company's 95% target, McDonald's terminated its partnership with IBM for this technology in June 2024.
Between 2021 and mid-2024, McDonald's reportedly piloted an AI-enabled voice ordering system that was developed with IBM and based on its 2019 acquisition of Apprente. It was reportedly deployed at at over 100 U.S. drive-thrus. Social media posts alleged frequent misorders, such as adding unwanted items, mixing adjacent lane orders, and ignoring corrections. In June 2024, McDonald's confirmed it ended the IBM pilot, citing plans to explore alternative voice-AI vendors.
Domain classification, causal taxonomy, severity scores, and national security assessments were LLM-classified and may contain errors.
AI systems that fail to perform reliably or effectively under varying conditions, exposing them to errors and failures that can have significant consequences, especially in critical applications or areas that require moral reasoning.
The AI system failed to perform reliably under varying conditions, such as handling crosstalk from adjacent lanes or understanding standard customer requests, resulting in incorrect orders.
AI system
Due to a decision or action made by an AI system
Unintentional
Due to an unexpected outcome from pursuing a goal
Post-deployment
Occurring after the AI model has been trained and deployed
The incident was caused by the deployed IBM AI voice ordering system failing to accurately process customer orders in real-world drive-thru environments.
A pilot program for an AI voice-ordering system at McDonald's drive-thrus was terminated due to high error rates and customer frustration. The incident is purely commercial and technical in nature, carrying negligible national security implications across all assessed categories.
The AI system operated independently to receive voice inputs from customers, process them in real-time, and autonomously update the digital order menu without direct human intervention during the interaction.
Limited Risk: The system is an AI-powered voice chatbot used for automated order taking, which falls under the category of chatbots requiring transparency so users know they are interacting with an AI.
The system's primary function was to act as an AI voice assistant to take customer orders at drive-thrus using voice recognition technology.
The reports provide consistent, detailed accounts of the pilot program, the specific errors made by the AI system, and the subsequent decision by McDonald's to terminate the partnership with IBM. Multiple sources corroborate the accuracy rates and the nature of the customer complaints.
None
Technology
Brittle Tree-Based NLU Model
IBM model used rigid logic trees instead of flexible LLMs to parse orders.
Poor Voice Recognition Accuracy
Accuracy was in the low 80s, failing the required 95 percent threshold.
Inadequate Noise Filtering
System could not filter out crosstalk from adjacent drive-thru lanes.
Data Inputs
Crosstalk Interference
Audio from neighboring lanes was processed as the primary customer's input.
Out of Domain Requests
Customer requests for non-menu items like Mountain Dew caused system errors.
Human Factors
Mid-Order Customer Changes
Customers changing their minds broke the rigid decision-tree logic of the AI.
Customer Frustration
Screaming or laughing customers further degraded voice recognition performance.
Process and Methods
Rigid Error Correction Flow
Error correction attempts triggered compounding mistakes like adding extra items.
Delayed Human Intervention
System failed to hand off to human staff quickly during cascading errors.
Management
Premature System Deployment
Trialing AI in live drive-thrus before achieving target 95 percent accuracy.
High Upfront and Support Costs
High maintenance and integration costs reduced economic viability of the AI.
AI system involved
Identified by automated extraction; can differ from the alleged developers above.
