Artificial intelligence (AI) has quickly moved from “shiny new tech” status to a daily reality in the restaurant industry. At this year’s QSR Evolution Conference in Atlanta, GA, nearly 80–90% of the audience reported they’re already using AI in some form—whether that’s in voice ordering analytics, marketing, or customer experience.
The panel, moderated by Jen Kern from Qu, brought together voices from across the industry — from restaurant operators to technology innovators — including Taco John’s, Nékter Juice Bar, Auntie Anne’s, Zignyl, SignalFlare.ai, Invisible Technologies, and Flybuy. Together, they explored how AI is reshaping restaurants.
But enthusiasm alone won’t carry a project across the finish line. As panelists emphasized, AI success in restaurants depends less on the algorithms themselves and far more on the data foundations that power them.
Data First: The Unsexy Secret to AI Success
Panelists were clear: before restaurants can unlock the promise of AI, they have to do the gritty, behind-the-scenes work of developing a solid data-first tech foundation with clean data at the core. AI runs on data so without reputable, trusted data, AI will not work.
According to a 2024 MIT report, a staggering 95% of AI projects fail, most often due to change management, poor integrations, and especially bad data. In an industry where the average restaurant group runs 15–25 tech systems, silos are the rule, not the exception. Breaking them down is where the real work begins.
One operator spent nearly 1.5 years preparing and standardizing data before an AI model ever touched a single order. That diligence meant when their models went live, the systems could scale cleanly across 330+ locations. Skipping that step? That’s how AI pilots turn into expensive shelfware.
Pilot with Purpose
Too often, brands see initial results in a handful of locations and assume the technology is “ready” — only to hit roadblocks at scale.
For Taco John’s, the patience of keeping their voice AI assistant, Elena, in just two restaurants for nearly a year was what built the foundation for later success. When the rollout expanded, the system hit a 93% non-intervention rate across 25+ locations, proving the long runway was worth it.
Those “small nuances” — regional menu variations, staff training, guest behavior — are what turn pilots into stumbling blocks if overlooked. Extended pilots give operators time to work through those issues, train teams, and establish trust.
The takeaway? A pilot isn’t proof of concept. It’s just the first step in a long-term strategy. True success requires executive buy-in, patience, disciplined process, and the right people. Without those, scaling AI in restaurants is nearly impossible.
From Kiosks to Analytics: AI as a Guest-First Tool
Panelists emphasized that AI’s greatest value shows up where it touches the guest directly:
- Kiosks: Adding kiosks increased guest satisfaction and led to more repeat visits. Built-in suggestive selling features helped drive higher check averages.
- Analytics: Platforms processing 30+ million unique orders per month improved pickup ETA accuracy by nearly 30% and cut wrong-location pickups in half.
And the guest-first story extends well beyond the four walls. Jon Asher, CTO of Nékter Juice Bar, pointed out how AI ensures the brand shows up where guests are searching:
Asher also highlighted how AI helps maintain brand voice and responsiveness across hundreds of locations:
“Customer service responses are automatically going out with zero intervention… and it escalates things that need to be escalated, like allergy mentions or one-star ratings, straight to the franchise owner or GM.”
Together, these examples show that AI isn’t just about automation or cost savings. It’s about meeting guests where they are — at a kiosk, in the drive-thru, on Google, or in a review thread — with consistency, precision, and a touch of hospitality.
The Long Game: Building an AI Roadmap
The message from the panel—and echoed by many industry leaders—is that AI in restaurants isn’t about a quick win. It’s about a long-term strategy:
- Clean your data: Standardize, de-duplicate, and integrate.
- Pilot with purpose: Test in controlled environments, refine, and extend slowly.
- Invest in change management: Train teams, communicate value, and bring staff along for the ride.
Think beyond cost-cutting: Use AI to improve consistency, accuracy, and guest delight.
Restaurants that treat AI as a foundation, not a feature, are the ones building staying power. The work isn’t flashy — cleaning data, piloting carefully, winning over teams — but it’s exactly what separates projects that scale from those that stall.
At Qu, we’re excited to see the shift from AI hype to real AI execution. By fusing edge computing with embedded intelligence, we’re redefining modern restaurant infrastructure — from the drive-thru to the kitchen, online to on-premises.
Panels like this one remind us that the long game is the only game when it comes to scaling technology in restaurants.
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