At its global convention in Las Vegas in June, McDonald’s unveiled its strategy for the coming years. Among the announcements was one about the company’s AI platform ArchIQ and its order-taking voice assistant Archy, in limited testing, at a handful of drive-thrus in the US.
In the summer of 2024, efforts of a similar system were shut down and the company subsequently told franchisees it was terminating its global order automation agreement, and no further phase was communicated.
This wasn’t just a trial run. It was deployed for three years since its launch in 2021, with an automated voice handling the drive-thru window at about a hundred US locations. What happened during that time is known primarily through videos of customers experiencing a parody of errors. Sources familiar with the tech later explained that the system had several difficulties, including interpreting different accents and dialects.
Behind closed doors, however, the problem was documented long before it reached social media. Analysts following the project noted franchisees’ discontent not seeing progress, and updates were infrequent.
So the numbers didn’t add up. The system had to be cost-effective compared to having a person at the counter, and it failed to offset operating costs. When it was shut down, the company declined to comment.
Two years later, though, the technology has advanced but underlying challenges of dialects and ambient noise at the order windows remain. No one repeats a move, though, that costs three years, a public retreat, and a collection of critical videos, so the question isn’t so much what’s new about Archy, but why McDonald’s decided to revisit this AI initiative.
Always in the picture
The day the system shut down, the company stated that the project gave them confidence that a self-service voice ordering solution would still be part of their future, and the case for a comeback became urgent. In August this year, after reporting lower than expected results in the US, CEO Chris Kempczinski asked analysts to sympathize with restaurant managers overwhelmed by several product launches in just a few weeks, each with its own training. Teams were spread thin, service times lengthened, and customer satisfaction plummeted. And in a model where drive-thru service generates around 70% of business in the US, the pressure builds up on the front line.
But returning to where something has failed, there has to be a clear understanding of what went wrong, and that’s no easy task since the most plausible explanation isn’t necessarily the correct one. During that time, industry analysts spoke about AI accuracy as the main challenge, and the debate centered on how much it should be increased. But the real problem was something else.
A definitive clue came from the QSR Drive-Thru Report, which audits the self-service systems of major American chains. In its 2025 edition, the study visited locations of three brands that already used automated voice systems, and found that when theAI took an order, it was 81% accurate. When it got stuck and transferred the conversation to an employee, the accuracy rose to 95%.
So the difference wasn’t how well the system listened, but what it did when it didn’t understand the customer. That was the scenario on which the pilot program was decided, and the difference from the first case in 2024 where it simply misunderstood and kept going. In fact, for three years there were employees near those loudspeakers who could’ve resolved issues right away. But they didn’t intervene because nobody, or nothing, called them.
None of this is unique to a fast-food chain. Any organization that puts an automated system in front of its customers reaches the same point when a machine is left alone with a person with no one watching. That moment doesn’t appear in any performance metric, and it’s the only one that matters when something goes wrong. The good news is that almost everything that’s going to happen there can be known beforehand.
When AI fails
When an automated system makes a mistake and continues operating, the error can go undetected, so the customer is left with a problem without anyone taking responsibility. And that gap won’t be filled by putting someone in charge of everything since no one can supervise every order at a service window, or every conversation on a customer service channel. What’s needed is for the system to pause during an interaction and transfer it to another user. And that needs rigorous testing.
For example, it can easily redirect the customer when the product is out of stock or the order isn’t on the menu. But it might not stop when it misunderstands the customer.
The other part lies with the employee who receives the handover and how they resolve the issue. What needs to be considered is how many incidents are closed in real time without the customer having to restart the process. This requires that the employee taking over can see what’s happened to that point and has the autonomy to resolve it.
Directing responsibility, not counting mistakes
The errors seen before aren’t random. In self-service kiosks with automated voice commands, most incorrect orders come from customizations, or requests that deviated from the ordinary. So there’s a small group of customers who account for the majority of errors. That’s the catch: the system is correct nearly all the time, while these customers often receive something different from what they ordered. It’s precisely these cases, the ones the average leaves out, that sink a pilot program.
Therefore, the question that reveals the problem isn’t how accurate the system is, which any technical report can answer. It’s who the customers are who bear the brunt of the errors, how much they’re worth, and what happens if they leave.
Before deployment, someone has to put in writing what specific errors a specific client will tolerate, and ask if the organization accepts it. That’s not a percentage, it’s a decision, and it can’t be signed off by just the tech team because it’s not a technical statement. This is where the bigger business comes in.
This case shouldn’t push an organization to retreat into internal practices to avoid what happened to McDonald’s. If AI is only introduced where the error is invisible, the value will also be invisible. That’s why McDonald’s has returned despite the reputational cost it’s already paid.
Reaching the customer doesn’t depend on the system never making mistakes. Teams depend on what’s done when the system does make mistakes, and knows when to stop.
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Source: News