AI in restaurants isn’t a sci-fi someday—it’s arriving shift by shift. In the next year or two, you’ll see voice bots in more drive-thrus, computer-aided kitchens, smarter marketing that actually feels personal, and back-office “copilots” that predict labor, prep, and demand with eerie accuracy. The winners won’t be the flashiest pilots—they’ll be the operators who pair AI with tight SOPs, clean data, and thoughtful change management.
Below is a practical look at where AI is truly headed next for restaurants, with concrete examples you can track right now.
1) Voice AI moves from pilot to playbook—carefully
Over the last 18 months, drive-thru and phone voice bots have gone from experiments to scaled deployments. Wendy’s FreshAI, built with Google Cloud, began in two states in 2024 and has since expanded widely, with corporate updates describing rollout to hundreds of sites. Internal writeups and trade coverage cite measurable time savings (e.g., ~22 seconds faster than local averages in early tests). Restaurant DiveWendy’s
Not every test sticks the landing the first time. McDonald’s ended its IBM automated order-taking pilot in 2024 after two years—then doubled down on a broader Google Cloud partnership to apply AI and edge computing across equipment, operations, and future ordering experiences. That’s the pattern to watch: less one-off bots, more platform-level AI (kitchen telemetry, predictive maintenance, order verification, decision support) woven into everyday ops. Restaurant DiveMcDonald’s Corporation+1
What to expect next:
- Multi-brand drive-thru voice stacks (vendors + chains) standardize on shared platforms and MLOps pipelines.
- Phone-answering assistants finally catch on for independents and mid-market brands, as accuracy improves and costs drop.
- Operators tighten guardrails: fallback to a human in <2 seconds, hot-swap for promotions and menu 86s, and clear escalation paths when ambient noise or accents stump the model.
Operator note: Before you pilot, script your “golden paths,” set handoff rules, and decide what counts as success: order time, upsell attach, or fewer abandoned calls. This is change management as much as it is model performance.
2) Kitchen AI: from robots to real-world throughput
Robots get headlines; throughput wins the day. Chipotle’s “Autocado” and Augmented Makeline show how targeted automation can shave minutes and steady quality—especially for bowls/salads that dominate digital mix. After testing, Chipotle highlighted how automating specific steps improves digital order accuracy and frees staff for hospitality. Expect continued iteration as vendors consolidate (Serve Robotics acquired Autocado maker Vebu in late 2024). MediaRoomRestaurant Dive
On the fry side, Miso Robotics continues to evolve “Flippy,” with 2025 updates and planned rollouts—though actual installed base remains modest versus early ambitions, a reminder to separate press releases from production reality. Restaurant Divemedia.hubtas.com
What to expect next:
- Computer vision checks for doneness/hold times and flags items at risk before the expo bottleneck.
- AI-informed KDS logic routes tickets based on real station load, not just menu category. Square, for instance, already uses AI to auto-assign items to stations in its KDS. Square
- Small wins compound: automating prep forecasting and batch schedules often outperforms a new robot arm on ROI.
Operator note: Pilot the boring stuff first—prep yields, cook-to-hold timers, station balancing. Better data and rules alone can cut peak ticket times 2–4 minutes without a single robot purchase.
3) Personalization without creepiness: loyalty + CRM grow up
The pendulum is swinging from spray-and-pray emails to predictive, one-to-one marketing. Starbucks’ “Deep Brew” popularized the idea of algorithmic personalization at scale; recent coverage still ties the program to strong ROI by aligning offers with behavior, daypart, and even weather. Expect more brands—big and small—to borrow this playbook as POS/CRM vendors expose AI features natively. BankInfoSecurity
SevenRooms reports show operators leaning on AI to merge reservation, spend, and preference data so hosts and servers deliver “superhuman hospitality” (remembered preferences, pacing, and offers that actually fit the guest). The near future is less “guess a promo” and more “know the guest.” PR NewswireSevenRooms
On the vendor side, Toast has rolled out AI marketing assistants to generate targeted campaigns across email/SMS based on menu and sales signals—an example of AI embedded where operators already work. Toast+1
Operator note: Start by unifying IDs (loyalty, reservations, online ordering) and codifying consent. If your data is fragmented, AI will only personalize the mess. Build a “three-segment” starter plan (new, lapsed, VIP) and let the model subdivide from there.
4) Pricing, promotions & the “dynamic” debate
2024’s “surge pricing” news cycle made one thing clear: changes to price must be transparent and consumer-friendly. When reports suggested Wendy’s might test dynamic pricing, the company clarified it wasn’t planning “surge” increases—rather, time-based discounting and menu flexibility. The near future looks like: scheduled value windows, combo optimization, and automatic markdowns on perishables—not ride-share-style spikes. FortuneRestaurant Dive
Operator note: If you experiment, set explicit rules (e.g., prices only go down at certain dayparts; communicate clearly in app and on menu boards). Focus AI on promo eligibility and merchandising—getting the right offer to the right guests—rather than on headline price swings that erode trust.
5) Forecasting, scheduling & supply: copilots for managers
The least glamorous AI may save you the most money. Expect mainstream POS/back-office suites to ship demand forecasting that feeds scheduling, prep plans, and purchase orders—using history, events, weather, and seasonality. This isn’t hypothetical: platform providers across the stack are shipping forecasting features, and delivery marketplaces are exposing AI-derived insights operators can use (DoorDash’s reports and analytics are increasingly AI-augmented). Restaurant Dive
Pair this with AI contact centers and support workflows. DoorDash’s case study on AWS highlights a generative AI solution handling hundreds of thousands of calls per day with sub-3-second response times. As costs fall, similar call-deflection and knowledgebase copilots will become feasible for multi-unit operators, too. Amazon Web Services, Inc.
Operator note: Treat forecasts like sous-chefs, not oracles. Post a “plan vs. actual” board, close the loop after each shift, and let the model learn your realities (events, staffing limits, neighborhood quirks).
6) Delivery & marketplaces: AI at the edges
AI is increasingly embedded in the services that surround your restaurant. DoorDash has added AI-powered brand safety tools (e.g., SafeChat+), upgraded ad products and targeting, and experimented with voice ordering. Even when these features live off-premise, they shape which orders you see and at what cost. DoorDashDoorDashVerdict Food Service
Operator note: Ask partners for incrementality reporting. If you fund promos, require lift analyses that separate “would’ve ordered anyway” from true new demand. AI can make your money smarter—or waste it faster.
7) The culture clash: pilots vs. production
AI is powerful and imperfect—both things are true. You’ve seen the videos: bots mishearing orders or quirky edge cases at the lane. You’ve also seen the data: time savings and attach improvements when systems are tuned, monitored, and supported by solid SOPs. The near future is not “flip a switch and replace roles.” It’s a sociotechnical change: new tools plus new workflows plus new training.
Recent history explains why the winners will be boringly methodical: McDonald’s paused one drive-thru AI test but accelerated broader AI/edge adoption with Google Cloud; Wendy’s continued scaling its voice assistant; Yum! Brands formed an industry partnership with NVIDIA to supercharge Byte (its internal digital/AI platform) across KFC, Taco Bell, and Pizza Hut. That arc—iterate, standardize, then scale—is the future. Restaurant DiveWendy’sYum
8) What about independents and small groups?
The good news: you don’t need a lab to use AI. Accessible wins include:
- Menu/KDS intelligence that auto-routes items and highlights choke points (live in several POS suites today). Square
- AI marketing assistants that draft effective email/SMS in-platform (Toast, Square) using your actual item and sales data. ToastSquare
- Phone-answering voice AI to capture orders/reservations when staff are slammed (watch for vendor offers that price per call/minute instead of big retainers).
- Personalization light: use SevenRooms-style segments (VIP, lapsed, new) and let AI refine; train staff on “next-best action” prompts during service. SevenRooms
Start small, measure, and scale. If a feature claims it’ll “transform your business,” ask for before/after data from a comparable concept.
9) Guardrails: data, privacy, and fairness
AI loves data; guests love trust. Put simple but strong guardrails in place:
- Consent & transparency: Be clear about data use in loyalty and reservations.
- Data minimization: Collect the least you need; keep it securely.
- Bias checks: Test voice bots with real neighborhood accents and background noise; tune prompts and escalation accordingly.
- Human in the loop: Define when staff must review suggestions (e.g., comp thresholds, allergy overrides).
Regulators and platforms are moving fast—staying slightly ahead with clear policies is an advantage, not a burden.
10) A 90-Day AI playbook (practical, not precious)
Weeks 1–2: Inventory your reality
- Tech map: POS, KDS, online ordering, loyalty/CRM, labor, inventory.
- Data audit: Can you match a guest across systems? If not, fix that first.
- Baselines: Peak ticket time, attach %, email revenue per send, phone abandonment.
Weeks 3–6: Pilot two high-leverage use cases
- Ops: KDS routing + prep/hold timers or demand forecasting that feeds schedules.
- Growth: In-platform AI marketing assistant to run 2–3 targeted campaigns (new, lapsed, VIP).
Set explicit success metrics (e.g., −90 seconds ticket time at peak; +15% email revenue per send).
Weeks 7–10: Add one frontline AI
- Phone or drive-thru voice at a single busy store with fallback and clear SOPs.
- Train staff on handoff, upsell scripts, and guest recovery.
Weeks 11–13: Measure & decide
- Scale what worked; retire what didn’t. Document your “AI service standards.”
The restaurant AI horizon: three shifts to bet on
- Ambient AI in the building. Less “a bot over here,” more sensors + models inside equipment and KDS that keep pace with the kitchen and prevent “Friday night surprises.” McDonald’s/Google Cloud is the canonical model—edge compute, data unification, and AI as fabric. McDonald’s Corporation
- First-party personalization that feels like hospitality. Tools from SevenRooms and POS suites turn data into genuinely helpful touches (not spam). Servers and hosts get suggestions that match the moment; guests feel known, not watched. PR Newswire
- Voice as a real channel. Expect voice to handle a meaningful share of drive-thru and phone volume, not to replace people, but to reallocate them to hospitality and accuracy where humans shine. Front-of-house stress drops; upsell consistency rises when the model is tuned. Wendy’s and peers will keep proving the case; others will follow—more cautiously after public missteps. Wendy’sRestaurant Dive
Real-world examples to watch
- Wendy’s FreshAI expanding from pilots to hundreds of drive-thrus. Wendy’s
- McDonald’s x Google Cloud: AI/edge across equipment, operations, and future ordering. McDonald’s Corporation
- Chipotle testing targeted automation (Autocado, Augmented Makeline) to stabilize digital make times. MediaRoom
- Yum! Brands x NVIDIA: scaling internal AI platform (Byte) across KFC/Taco Bell/Pizza Hut. Yum
- Toast & Square embedding AI assistants for everyday tasks (marketing, KDS logic). ToastSquare
- DoorDash leveraging AI in safety, ads, and contact centers that field massive call volumes. DoorDashDoorDashAmazon Web Services, Inc.
Bottom line
AI’s “near future” in restaurants won’t look like robots replacing people—it’ll look like less chaos at the pass, fewer abandoned calls, smarter promos, and more consistent hospitality. The chains are laying the rails at scale; the tools are landing in SMB-friendly platforms; and the operators who win will be the ones who:
- Clean their data and connect the dots,
- Pilot narrowly with clear success metrics, and
- Train people as thoughtfully as they tune models.
Technology changes fast; good service habits compound faster. Use AI to free your team to do the one thing algorithms can’t: make guests feel great, dish after dish.
