TL;DR
- Conversational AI for the drive-thru is three layers working together: automatic speech recognition (ASR), natural language understanding (NLU), and a large language model (LLM) for the conversational layer.
- The drive-thru is a harder environment than a call center or smart speaker: open windows, multiple speakers, accents, and guests changing their order mid-sentence.
- Independent data (the 2025 QSR Drive-Thru Report) found employees stepped in on roughly 21% of AI-taken orders, which is why a built-in real-time fallback matters more than a demo.
- Hi Auto’s own numbers across roughly 1,000 stores: 93%+ completion and 96% accuracy.
- Ask any vendor for completion and accuracy together, at real scale, and what happens when system confidence drops.
Conversational AI is software that understands spoken language, works out what someone actually wants, and responds in a natural back-and-forth exchange. It typically combines speech recognition, language understanding, and increasingly a large language model to hold the conversation. Hi Auto’s glossary covers the full definition of conversational AI if you need the formal version.
This guide takes a different angle. It is written for the IT leader evaluating conversational AI for the drive-thru specifically, where the same technology behaves very differently than it does in a call center or a smart speaker in someone’s kitchen. A system that performs well in one setting is not automatically good in the other. That gap is what this guide is about.
The stack, in plain English
Conversational AI for ordering is not one piece of technology. It is three layers working together, and each one can fail on its own.
Automatic speech recognition, or ASR, turns spoken audio into text. This is the first and most fragile step. If the ASR mishears the words, nothing downstream can fix it.
Natural language understanding, or NLU, takes that text and works out what the guest actually meant. “Gimme a two piece, dark, extra crispy, and make it a combo” has to map to specific menu items, a size, and a modifier, not just a string of words.
The large language model, or LLM, handles the conversational layer: asking a clarifying question, confirming the order back, staying natural if the guest changes their mind. It is the part that makes the exchange feel like a conversation instead of a phone tree.
Three layers, three separate points of failure. A vendor pitch that talks about only one of them, usually the LLM, is not describing the whole system.
Why the drive-thru breaks generic conversational AI
Much of the conversational AI on the market today was designed and tuned in windows-closed environments: call centers, contact center IVRs, in-car assistants with the doors shut. The drive-thru is a different, harder environment, and it exposes weaknesses that never show up in a demo.
The car window is open. Wind, traffic, the vehicle’s own engine noise, and the next car’s radio all compete with the guest’s voice at the exact moment the system needs to hear it clearly.
There is often more than one speaker, at different distances from the microphone. A parent in the driver’s seat and kids in the back both talking, at different volumes and different distances, is a routine order, not an edge case.
Accents and dialects vary enormously across a 300-plus location chain, and menu items, regional phrasing, and speech patterns shift by market. A system tuned on one region’s speech patterns can quietly underperform in another.
Guests change their mind mid-sentence. “Can I get a large fry, actually make that a medium, and add a drink” is normal drive-thru speech, and the system has to track the correction without losing the rest of the order.
And a typical drive-thru order takes roughly 5 to 7 conversational turns to complete: greeting, order, clarification, upsell response, confirmation, payment prompt, close. Each turn is another chance for the system to lose the thread.
Put those together and it is easy to see why a system marketed for “drive-thru” but built for a windows-closed environment struggles the moment it meets a real lane. This is the single biggest thing a technical buyer should press a vendor on, and it deserves more scrutiny than the sales deck usually gets.
Why raw LLMs are not enough
Generative language models are genuinely good at holding a natural conversation. That is not in dispute. The risk is what happens when a general-purpose LLM meets a high-noise, high-speed, safety-and-accuracy-critical setting like a drive-thru lane, where a misheard word becomes a wrong order and a slow response becomes a longer line.
That risk shows up in independent data. The 2025 QSR Drive-Thru Report, covered by Aneurin Canham-Clyne in Restaurant Dive on 2 October 2025 and by Danny Klein in QSR Magazine on 1 October 2025, found that at locations running voice AI an employee had to step in on roughly 21% of orders: the system could not answer a question, could not handle a customization, or an item was out of stock. That is close to one order in five, measured by a third party rather than claimed by a vendor, and it is the clearest available evidence that a drive-thru system needs a real-time fallback built into it rather than bolted on later. Hybrid, in other words, is a measurable engineering decision rather than a marketing word.
The systems that have actually scaled past a pilot tend to share a few things in common. They pair fine-tuned LLMs for order classification with in-house noise handling and ASR built specifically for the drive-thru, not adapted from a call-center or smart-speaker product. And they include a built-in real-time fallback: when the system’s confidence drops on a given moment in the order, a trained human supervisor steps in, live, rather than the guest being left to repeat themselves into a wall of silence. That is a hybrid AI architecture, and it is worth asking any vendor directly whether that is what they are actually selling.
For context, Hi Auto’s own numbers at scale across roughly 1,000 stores show what this kind of hybrid, purpose-built approach supports: completion rates of 93%+ paired with 96% order accuracy, and noise handling tuned for open-window conditions rather than a quiet room. Those two numbers, completion and accuracy, only mean something read together. A high completion rate with weak accuracy just means the system is confidently getting orders wrong faster.
Five questions to ask a vendor
Any conversational AI vendor pitching the drive-thru should be able to answer these without deflecting to a generic AI story.
- What is your completion rate and your accuracy rate, together, and at what scale? A number from a pilot of ten stores tells you very little about what happens at three hundred.
- Was your ASR and noise handling built for the drive-thru specifically, or adapted from a call-center or voice-assistant product? Open-window, multi-speaker, traffic-noise conditions are a different engineering problem than a quiet room.
- What happens when confidence drops mid-order? Ask specifically whether there is a built-in real-time fallback with a trained person in the loop, not a claim that the system runs on its own with no backup.
- What languages does the system handle, and does it detect and switch automatically? English and Spanish, auto-detected and switched mid-conversation, is table stakes for many QSR markets, not a premium feature.
- What does deployment actually look like, from pilot to fleet-wide rollout, and how long does each stage take? Vague answers here are usually a sign the vendor has not scaled past a handful of stores.
None of these questions require naming a specific competitor. They simply force a vendor to show their work instead of their pitch deck.
The real question is not whether it can talk
Holding a conversation is something most of this technology can now do. That part has matured fast. The harder, more useful question for an IT leader is whether the same system holds up, consistently, across hundreds of lanes, accents, weather conditions, and shift changes, without adding a new escalation path for franchise partners and support teams to absorb.
That question is as much organizational as it is technical. Richard Del Valle, CIO and Chief of Staff at Bojangles, put it plainly:
“I think it really has been a wonderfully collaborative effort here at Bojangles because we’ve never treated this as an IT project. This has been a Bojangles project from day one all-inclusive including our franchise partners and our franchise business consultants and the team that helps franchises be successful.”
Richard Del Valle, CIO and Chief of Staff, Bojangles. QSR webinar, “AI Order Taker That Works: The Bojangles Model”, July 2025. Watch the clip
That is the shift in thinking this guide has been building toward. The technical evaluation matters, and it should be rigorous. But the vendor that scales is usually the one whose architecture and rollout approach were built with the operators who actually run the lane, rather than around them. The quality of the demo predicts far less than that does.
Evaluating conversational AI vendors for the drive-thru takes more than a demo. Download the Buyer’s Guide: AI for the Drive-Thru for a fuller evaluation framework, and use the Hi Auto glossary as your technical reference while you compare vendors term by term.