Artificial intelligence has crossed the line from “interesting pilot” to core infrastructure in foodservice. Operators now use voice agents to answer and route calls at scale, conversational AI to take orders in drive-thru lanes, computer vision to monitor compliance and yield, and cobotics to standardize high-volume prep. The result is a new operating model in which speed, accuracy, and personalization are not just differentiators; they are the battleground for margin and market share.

In essence; The restaurant industry is on the brink of a dramatic transformation… defined by robotics, artificial intelligence, and smart infrastructure.

Across the industry, investment signals are clear. Popmenu’s 2025 research indicates that roughly a third of operators (34%) already use AI, with another 48% planning adoption this year, suggesting AI will be near-ubiquitous by year-end for at least some processes. These investments are not purely experimental; they are becoming formal programs aimed at labor stabilization, waste reduction, and better guest conversion. Deloitte’s 2025 survey of 375 restaurant executives underscores this shift from trials to scalable capabilities and highlights the central challenge leaders now face: balancing a surge of front-of-house innovation with deeper back-of-house transformation and the governance to make both sustainable.

Restaurants have long managed thin margins and volatile inputs, but the past two years have hardened a consensus: unit economics improve when repetitive tasks are automated, demand is forecastable at the station level, and data is unified across channels. AI’s value emerges precisely at these seams. Accurate forecasts improve ordering and prep lists; schedule optimization reduces overtime spikes and unproductive slack; and personalization increases check while making offers feel genuinely helpful. The bigger strategic lesson is technical architecture. The winning brands assemble a “data spine” early, clean POS data, consistent guest identity, and open APIs, so that AI use cases can be tested and scaled without rewiring the tech stack each time. Organizational readiness and governance are now the limiting factors, not the availability of tools.

Forecasting, inventory, and waste control are the most pervasive starting points. Demand models that incorporate dayparts, weather, event calendars, and promotion history can generate purchase quantities and dynamic prep lists with far greater confidence than manual heuristics. The cash-flow effect is immediate: less emergency buying and fewer write-offs. Computer-vision audits add a second layer by monitoring portioning and food safety compliance in real time, creating an evidence trail that reduces risk while raising consistency.

Cobotics then standardize the highest-volume, most repeatable tasks. Chipotle’s ongoing tests with two systems, Vebu’s Autocado for avocado prep and Hyphen’s automated makeline for bowls and salads, illustrate a pragmatic approach: automate the SKUs and assembly flows with the highest consistency and let staff focus on complex orders and hospitality. The company has publicly stated that bowls and salads account for about two-thirds of its digital orders, which explains why an “augmented makeline” could have an outsized impact on throughput and labor per transaction. Trials in Southern California continue to refine speed, accuracy, and crew acceptance, with the cobots positioned as helpers rather than replacements.

By reducing human labor by 25 to 30%, restaurants will see a shift in financial strategy: higher upfront investment in technology and maintenance, balanced by significantly lower ongoing labor costs.

My forecast is also a design brief. Kitchens and service corridors should anticipate equipment bays, robot docking, and service clearances; utilities should be instrumented for predictive maintenance; and the overall footprint should support modular swaps as generation-two hardware arrives. The restaurant of the future is not just a place to eat. It’s a living, breathing ecosystem powered by robotics, optimized by data.

Figure 1 , AI Adoption in Restaurants (2025)

Figure 2 , Chipotle Digital Order Composition (Bowls & Salads share)

Voice AI has matured fastest where customer intent is well-structured and the cost of a missed interaction is high. Red Lobster’s systemwide rollout of a SoundHound phone agent is a straightforward illustration: every call gets answered, menu-trained automation handles routine requests and takeout orders, and crew are freed during peak periods. In aggregate, “answer every call” is a simple but material revenue recapture lever for large casual brands.

Drive-thru voice ordering shows both the promise and the pitfalls. Bojangles has moved from pilots to a multi-hundred-location rollout of Hi Auto’s assistant (“Bo-Linda”), with claims of human-level order accuracy, while McDonald’s ended its high-profile pilot with IBM in 2024 after two and a half years of testing. The lesson is not that voice AI “works” or “doesn’t,” but that context matters, noise management, menu complexity, handoff to crew, and fallback escalation determine guest experience as much as the model itself. Importantly,

McDonald’s simultaneously deepened a broader AI and data partnership with Google Cloud for in-restaurant compute, analytics, and future agentic applications.

Figure 3 , Drive-Thru Satisfaction and Voice-AI Perception (2024)

The customer-experience layer is shifting as well. Dine Brands (Applebee’s and IHOP) has disclosed a personalization engine designed to tailor recommendations and offers to guest behavior, aligning with a wider movement to use first-party data for relevance rather than blanket discounts. As budgets tighten, this “next-best action” capability is increasingly treated as a loyalty and margin lever rather than a marketing experiment.

AI and connected sensors are proving useful behind the scenes. Forecasting stabilizes ordering; dynamic routing reduces delivery cost and variance; and computer vision paired with smart scales identifies waste patterns by station and SKU. These capabilities are increasingly relevant to ESG reporting as well, where credible data on waste, energy, and sourcing is required by lenders, landlords, and, in some markets, regulators. Tech leaders who choose systems with clean exports and consistent identity resolution will find that sustainability reporting becomes a by-product of good operations rather than an additional burden.

The following represent some summary case studies that are worth considering:

Bojangles: drive-thru voice AI at scale. Following successful pilots, Bojangles signed for a rollout of Hi Auto’s assistant across hundreds of stores, with coverage noting deployment in roughly 200 locations at the time and performance claims that approach human accuracy.

The brand’s path shows the practical sequencing that works: constrain scope, tune prompts and menu handling, design escalation to crew, and then scale with clear SLAs.

Red Lobster: every phone call answered. The brand’s partnership with SoundHound deploys a menu-trained AI agent across all locations for takeout ordering and routine questions. The strategic rationale is simple: phones ring most when the dining room is busiest. Offloading that load to automation protects service flow and recaptures demand that would otherwise abandon.

Chipotle: targeted cobotics for high-volume SKUs. Bowls and salads account for the majority of digital orders at Chipotle, making them ideal for Hyphen’s automated makeline; meanwhile, Vebu’s Autocado is designed to reduce the most repetitive prep tasks without changing the recipe or final mashing. The combination aims to speed the line, shrink portion variance, and give staff back minutes for hand-crafted items and guest interaction. The company is iterating in-market before wider expansion.

McDonald’s: knowing when to stop (and where to invest). After two-plus years, the company ended its IBM Automated Order Taker pilot in 2024. Rather than a retreat from AI, leadership reframed its focus to broader digital infrastructure, edge compute, analytics, and agentic use cases via Google Cloud, that can support multiple operational applications, not just voice. The takeaway is disciplined governance: define success and “kill criteria” up front and reallocate capital where ROI is more durable.

The practical path begins by linking AI initiatives to hard outcomes: speed of service, order accuracy, waste, and labor per transaction. A balanced portfolio typically pairs one front-of-house use case (AI phone agent or drive-thru) with one back-of-house use case (demand forecasting or prep lists) so that teams see benefits both to the guest and to the kitchen. Data rights and vendor selection deserve early attention. Contracts should guarantee exportable data and open APIs, and should avoid lock-ins that strand investments. Many brands adopt a “lightweight CDP” approach, centralizing guest and order data and pushing segments back into POS and messaging systems, to support personalization without rebuilding the entire stack.

Change management is the underrated success variable. Staff adoption rises when automation is framed as a way to remove drudgery and give back time for hospitality, when escalation to humans is fast and respectful, and when savings are partially reinvested in wages, training, and better tools.

Conclusion: the hospitality platform

The restaurant is becoming part kitchen, part data hub, and part experience engine. The brands that integrate AI deeply, but thoughtfully, will compress service time, reduce variance, and meet guests with context and care. The trick is to remember what not to automate. AI should carry the repetitive load so your people can deliver the parts of hospitality that no machine can reproduce. As Ancill puts it, “Automation will push restaurants to become more efficient, resilient, and scalable,” and design and operations should evolve in tandem to capture that advantage.

Robert Ancill is a globally recognized restaurant consultant, design innovator, and trend forecaster. Based in Los Angeles and originally from Glasgow, Scotland, he launched his consulting career in 2002 with the founding of The Next Idea, a hospitality concept and design agency that has since evolved into The Next Idea Group. Under his leadership, the firm has overseen more than 800 restaurant and café openings, remodels, and brand launches across more than 24 countries. As a leading authority on food‑service concepts, franchising, architectural design, and emerging consumer behaviors, he also serves as Chairman of Heritage Restaurant Consultants and as a board advisor to the cutting‑edge AI‑powered experience platform Atmosfy.

A respected futurologist in hospitality, Robert produces annual trend forecasts that span robotics, AI, vegan and non-alcoholic beverages, and the shifting demise of traditional casual-dining brands, even predicting TGI Fridays’s struggles. In an exciting new publishing venture, he has launched a visionary trilogy of books debuting in Fall/Winter 2025. The first volume, Restaurant Marketing: The Ultimate Guide to Modern Restaurant Marketing, delivers a 250-plus-page playbook, combining AI, SEO, design psychology, loyalty programs, vendor directories, and real-world case studies, to help operators thrive in a tech-driven marketplace. Subsequent volumes will tackle restaurant design and the traveling restaurant consultant, offering both tactical guidance and behind‑the‑scenes stories drawn from his global career.

https://www.thenextideagroup.com

https://www.globaldesignconsultant.com

https://www.robertancill.com

https://www.Heritagerestaurantconsultants.com

https://www.linkedin.com/in/robertancill/

• Robert Ancill , “Robots and AI in the Restaurant Industry,” June 5, 2025. https://www.globalrestaurantconsultant.com/restaurant-blog/robots-and-ai-in-the-restaurant-industry/

• Popmenu / Restaurant Technology News , “One-Third of Restaurants Have Already Adopted AI; 48% Plan to in 2025,” Feb 2025. https://restauranttechnologynews.com/2025/02/market-research-one-third-of-restaurants-have-already-adopted-ai-technology-48-plan-to-do-so-this-year/

• Deloitte , “How AI is Revolutionizing Restaurants,” 2025 (survey of 375 executives). https://www.deloitte.com/us/en/about/press-room/deloitte-how-ai-is-revolutionizing-restaurants.html

• SoundHound / Red Lobster , Press release on AI phone agent rollout, Sept 23, 2025. https://investors.soundhound.com/news-releases/news-release-details/red-lobster-partners-soundhound-ai-power-phone-ordering-across

• Bojangles & Hi Auto , Coverage (Restaurant Business). https://www.restaurantbusinessonline.com/technology/bojangles-speeds-ahead-drive-thru-ai

• Bojangles & Hi Auto , Coverage (Restaurant Dive). https://www.restaurantdive.com/news/bojangles-to-deploy-hi-auto-drive-thru-voice-ai-at-scale/722008/

• McDonald’s ends IBM AOT pilot , AP News, June 2024.https://apnews.com/article/bebc898363f2d550e1a0cd3c682fa234

• Chipotle Press Page , Autocado and Augmented Makeline; bowls/salads ~65% of digital orders. https://newsroom.chipotle.com/2024-09-16-CHIPOTLE-DEBUTS-AUTOCADO-AND-THE-AUGMENTED-MAKELINE-BY-HYPHEN-IN-RESTAURANTS

• Restaurant Dive , Chipotle + Hyphen testing background. https://www.restaurantdive.com/news/chipotle-starts-automated-makeline-hyphen-food-safety/695443/

• Intouch Insight , 2024 Drive-Thru Study press release. https://www.intouchinsight.com/press-releases/friendliness-impacts-2024-drive-thru-performance