--- title: Evaluating AI technology in QSR description: Discover how QSRs can effectively evaluate and implement AI technology, focusing on real use cases, partnerships, and avoiding common pitfalls. image: https://blog.telaid.com/hubfs/Blog%20Post%20Featured%20Image%20(1).png --- [![Telaid%](https://blog.telaid.com/hs-fs/hubfs/Telaid%20-Aug2017/Images/logo.png?width=168&height=35&name=logo.png "logo.png")](https://www.telaid.com/) - [PODCAST](https://www.telaid.com/podcast/) - [RESOURCES](https://www.telaid.com/company/resources/) - [BLOG](https://blog.telaid.com/blog) - [Why Telaid](https://www.telaid.com/why-telaid/) - - [Why Telaid](https://www.telaid.com/why-telaid/) - [Technology Partners](https://www.telaid.com/why-telaid/technology-partners/) - [People, Process and Tools](https://www.telaid.com/why-telaid/people-process-and-tools/) - [Technology Solutions Center](https://www.telaid.com/why-telaid/technology-solution-center/) - [BICSI Training](https://www.telaid.com/why-telaid/bicsi-training/) - [Solutions](https://www.telaid.com/solutions/) - - [Solutions](https://www.telaid.com/solutions/) - [DeviceWatch](https://www.telaid.com/solutions/devicewatch/) - [Retail Automation](https://www.telaid.com/solutions/retail-automation/) - [Technology Lifecycle Solutions](https://www.telaid.com/solutions/technology-lifecycle-solutions/) - [Technology Deployments](https://www.telaid.com/solutions/technology-deployments/) - [Physical Security](https://www.telaid.com/solutions/physical-security/) - [Wireless](https://www.telaid.com/solutions/wireless/) - [Interactive Solutions](https://www.telaid.com/solutions/interactive-solutions/) - [Point of Sale (POS)](https://www.telaid.com/solutions/point-of-sale/) - [Workforce Device Management](https://www.telaid.com/solutions/workforce-device-management/) - [Case Studies](https://www.telaid.com/solutions/case-studies/) - [Resources](https://www.telaid.com/company/resources/) - [Services](https://www.telaid.com/services/) - - [Services](https://www.telaid.com/services/) - [Managed Services](https://www.telaid.com/services/managed-services/) - [Project Management Organization](https://www.telaid.com/services/project-management-organization/) - [Integration and Deployment](https://www.telaid.com/services/integration-and-deployment/) - [Day 2 Services](https://www.telaid.com/services/day-two-services/) - [Industries](https://www.telaid.com/industries/) - - [Industries](https://www.telaid.com/industries/) - [Retail](https://www.telaid.com/industries/retail/) - [Logistics](https://www.telaid.com/industries/logistics/) - [Finance](https://www.telaid.com/industries/finance/) - [Healthcare](https://www.telaid.com/industries/healthcare/) - [QSR](https://www.telaid.com/industries/qsr/) - [Grocery](https://www.telaid.com/industries/grocery/) - [Convenience](https://www.telaid.com/industries/convenience/) - [Company](https://www.telaid.com/company/) - - [Company](https://www.telaid.com/company/) - [Team](https://www.telaid.com/company#Team) - [Labor Partners](https://www.telaid.com/company/partners/) - [News & Events](https://www.telaid.com/company/news-events/) - [Community Engagement](https://www.telaid.com/company/community-engagement/) - [Careers](https://www.telaid.com/company/careers/) - [Blog](https://blog.telaid.com/blog) - [Contact Us](https://www.telaid.com/company/contact-us/) [![Contact Us](https://no-cache.hubspot.com/cta/default/3454475/92d5f75f-e3df-4380-9163-746c0c54d2ee.png)](https://cta-redirect.hubspot.com/cta/redirect/3454475/92d5f75f-e3df-4380-9163-746c0c54d2ee) #### Call Us We are happy to discuss your business challenges over the phone and answer any questions you have about us. 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It is here, and the pressure to adopt it is real. But according to Brooks Thompson, Director of Emerging Technology and Partnerships at Telaid, the harder question is not whether to adopt AI. It is knowing which AI is actually real and which is just noise.  Where AI Is Gaining Real Traction QSR has always adopted new technology, Thompson notes, but it has rarely led the way. That is starting to shift as AI computer vision and generative learning, already proven in other industries, find their footing in restaurants. The technologies gaining the most traction are the ones touching the customer directly: chatbots, drive-thru communication tools, and personalized suggestions. Large language models, by contrast, are still mostly working in digital channels rather than in-store. On the operations side, Thompson points to production planning as an area with real upside: understanding what is in the prep area, predicting drive-thru volume, and balancing speed of service against waste. He also highlights simpler technology making a difference, like systems that verify a bag's weight against the order to catch missing items before they reach the customer. [![Blog Post Featured Image (1)](https://blog.telaid.com/hs-fs/hubfs/Blog%20Post%20Featured%20Image%20(1).png?width=1200&height=628&name=Blog%20Post%20Featured%20Image%20(1).png)](https://share.hsforms.com/1LZ0jGqNQRjW6JjVhizZqHA221hn) The Gap Between the Lab and the Store A consistent challenge is the gap between what works in a lab with abundant resources and what works in an actual restaurant with limited bandwidth and infrastructure. A solution built for a 100,000 square foot facility does not automatically translate to a QSR location. That gap is part of why cloud-based architecture, decoupling data and shifting compute elsewhere, is becoming a bigger part of the conversation. Voice: The Use Case Everyone Is Watching Thompson sees voice technology as the one poised for a breakout moment. Early chatbot and AI ordering experiences generated excitement, but adoption has been cautious since brands do not want to risk the customer experience, and most people have had a frustrating call center encounter that makes the risk obvious. Once voice is figured out at scale, Thompson expects it to spread quickly, unlocking upselling and more personalized interactions. The technology and the appetite are there. The execution is not quite ready yet. Choosing the Right Partners Thompson's first piece of advice for any QSR company considering AI is to focus on partnerships before anything else. Who is helping deliver the system, and does the architecture actually hold up? It is easy to get excited about use cases without understanding the infrastructure required, only to discover later that cost or model limitations make the plan unworkable. An experienced partner brings outside perspective that helps validate pain points before they become expensive surprises. A few red flags are worth watching for. Any partner who claims they can do everything without acknowledging real challenges is an immediate warning sign. So is a partner who never mentions training or customization, since every AI model needs to be trained for its specific environment, and that takes real time. Start With the Problem, Not the Technology Thompson's most counterintuitive advice is to flip the usual approach. Rather than starting with the technology and searching for a place to apply it, start with the actual pain points in the business and determine whether they are worth solving. Validating how real and how costly a problem actually is tends to surface the right AI use case naturally. Integration Is the Dream, Rarely the Starting Point Full integration with existing IT systems is the ideal outcome, but Thompson sees two common paths there. Some organizations try to integrate everything from day one, which is often slow due to technical debt. Others prove the technology independently first, then face a long road toward broader adoption, which tends to generate more frustration once leadership is invested and asking why it is not yet delivering results. The better outcome is a blend of the two: proving the concept while staying engaged with the teams who will eventually support it. To bridge that gap, Telaid runs workshops that bring in the project owner alongside every supporting team, operations, marketing, and IT, so everyone understands the plan early and can build it into their own roadmaps. Real AI vs. Marketing Buzzwords With "AI" attached to nearly every product on the market, Thompson offers a simple test. If a tool just hands you data points and leaves the decisions to you, that is analytics, not AI. True AI gathers that data and produces recommendations or makes decisions on its own, whether at the edge or in the cloud, depending on the speed required. Where Deployments Actually Break Down Most AI projects do not fail because the technology stops working at scale. They fail in the decisions and execution that happen before deployment. A solution might work perfectly in its own controlled environment, but once it starts interacting with other teams and systems, internal politics and miscommunication often stall it out. Thompson is clear that most failures are not really about the technology. Projects typically have proper metrics and real-world testing behind them. What breaks down is communication between teams and a failure to connect the technology to genuine value for the people using it. His closing advice: getting stakeholders to sign off is not the same as getting real buy-in. Without genuine change management at the store level, even a well-designed solution will struggle to deliver the adoption and ROI it was built for. Ready to take the next step? 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