{{item.videoDuration}}
{{item.title}}
{{item.text}}
{{item.videoDuration}}
{{item.text}}
Key takeaways:
Top-performing restaurants run on connected decisions. Choices about food, demand, labor, inventory, equipment, and service need to be coordinated by smart technology and skilled people—or efficiency and revenue suffer, and guests don't come back.
Automating tasks isn’t enough. Today’s managers juggle data from multiple disconnected systems. Agentic AI can pull those signals together and turn them into real-time action, catching problems before they affect the guest experience.
The real payoff may be a new kind of restaurant manager. With agentic AI taking on more operational complexity, managers can focus on leading their teams and creating the personal customer interactions that build loyalty.
Ask five restaurant general managers about their greatest operational challenge, and you’ll probably get five different answers. But the underlying challenge is usually the same: The information they need in order to run the restaurant is scattered across systems (e.g., forecasting, hiring, scheduling, point-of-sale, digital, inventory, training, and maintenance), leaving the manager to connect the dots and coordinate the response. To help move beyond these challenges and deliver better experiences, restaurants should run as intelligent enterprises.
Consider a quick-service restaurant (QSR) launching a limited-time offer (LTO), a new, differentiated product built on non-core SKUs that store teams rarely handle, to boost digital and drive-thru demand. Because LTOs are designed to hit the market with speed, they typically receive more rapid operational testing and training than standard product rollouts, and the team believes it has everything covered. The forecast, labor schedule, inventory position, and training plans all look reasonable. But those plans were built in a controlled environment; a new, more popular than forecasted LTO launch is not a controlled environment.
Once the promotion goes live, the LTO’s assembly steps—manageable in controlled testing but newer to most of the crew—overwhelm the sandwich station under peak digital and drive-thru demand, creating backlogs and longer wait times. The blind spot wasn’t in any individual plan, but in the way demand, product novelty, limited staff familiarity, and real-world demand would interact when the LTO exceeded expected peak volume. This is precisely the kind of pattern that machine learning can detect in the earliest hours of the LTO window, flagging slower assembly times or emerging bottlenecks as they first appear, so real-time decision intelligence can intervene and recommend clear actions. Addressing issues before they escalate allows the manager to free up capacity to serve customers better, while equipping the crew with the right talk track to push promotions and honor loyalty.
Or imagine another restaurant whose primary coffee brewer fails during the morning rush. An unfamiliar error code halts production just as digital and drive-thru orders are peaking. Rather than scrambling through equipment manuals, maintenance portals, and help desks, the manager turns to an AI copilot. Within seconds, the copilot interprets the error code, draws on equipment history and troubleshooting procedures, and guides the manager through corrective actions while coordinating maintenance escalation and operational work-arounds. What could have become a prolonged service disruption is instead managed through an immediate, coordinated response, allowing the manager to maintain service continuity while minimizing lost sales and guest frustration.
Production and service breakdowns show the ways that disconnected systems and isolated automation can hurt a restaurant’s revenue and reputation. The solution in both cases is an AI-powered store orchestration layer that connects systems and coordinates action.
How can agentic AI orchestrate restaurant operations?
Today, general managers act as the manual integration layer in restaurants, typically piecing together fragmented data from more than 20 separate software systems to manage sales, employees, guests, and waste. Service, staffing, and equipment issues often emerge only after revenue and the customer experience have already been affected, forcing managers into reactive problem-solving. Alternatively, district leaders detect margin leakage when they conduct their regular performance audits, after the opportunity for real-time intervention has passed.
An AI orchestration layer can turn fragmented data into a better methodology to improve timely decision-making and help drive profitability. More good news: This AI orchestration layer doesn’t need to be a costly reinvention of existing systems. It can be a flexible, modular, and scalable solution on any platform, allowing restaurants to begin implementation immediately where needed most while giving crew members what they need to run their day more effectively.
In the QSR example above, a store intelligence orchestrator could analyze prior promotion launches, external events, channel demand, employee skills, historical service times, and readiness gaps before recommending a coordinated plan for deployment, training, prep, and replenishment. Instead of another dashboard, the manager would receive a clear set of actions designed to protect speed of service and throughput.
In the second example, a voice-enabled AI copilot could help an employee quickly identify the asset and receive troubleshooting steps grounded in the manufacturer’s manual and relevant service guidance. If the issue remained unresolved, the orchestrator would automatically create and submit an urgent work order containing the necessary details and service history, then route it for approval.
Ready to get started? Yes, chef!
Here are three strategic shifts restaurant leaders can make to build AI-powered smart operations:
Shift 1: Think orchestration, not automation. Restaurant managers make countless micro-decisions daily about inventory, staffing, deployment, food safety, and more. According to PwC and industry estimates, these can consume as much as 40% of restaurant managers’ time. An AI orchestration layer can bring together operational data, external signals like weather and local events, and IoT-enabled equipment to anticipate disruptions and act on them. Once connected across locations, it can also feed insights to the enterprise—for example, equipping procurement teams with immediate insights about demand, waste, and inventory so they can negotiate better terms with suppliers.
Shift 2: Bring executive-level focus to AI initiatives. The companies pulling ahead are pinpointing where agentic AI can make the biggest difference to revenue, growth, and profitability. The COO should own operational AI initiatives in partnership with the CHRO, CTO, and CIO, among others. Restaurant managers also need a seat at the table, since they know best where handoffs break down. Without their input, even well-resourced AI initiatives risk solving the wrong problems or designing workflows that don’t reflect how work actually gets done at the restaurant level. Leaders should invest in upskilling managers to direct both people and AI agents. This requires deliberate training, coaching, and support structures so managers can grow into an expanded scope.
Shift 3: Redesign the talent architecture. At the enterprise level, cross-functional teams design intelligent workflows and set guardrails. In the restaurant, managers stay accountable for execution while AI helps them make better decisions and handle routine work. Consider equipment maintenance: AI can guide employees through initial troubleshooting and service requests, managers make calls about availability and service continuity, and technicians build the skills to maintain increasingly sophisticated equipment. The goal isn’t to replace human judgment but to free people for coaching, hospitality, and guest recovery.
Where should restaurants start with AI-powered store orchestration?
Restaurant brands need a better way to turn the information they already have into coordinated action at the restaurant. Your starting question is not, “Where can we add AI?” but rather, “Which store-level decisions and handoffs create the most friction and lose the most value?”
Begin by mapping the workflows that consume the most managerial time or most directly affect speed of service, labor productivity, equipment uptime, waste, and guest experience. Then establish the baseline, redesign the workflow, define what the orchestrator may recommend or execute within predefined guardrails, and test it in a representative set of restaurants.
A focused store orchestration diagnostic can quantify the opportunity, prioritize the first use cases, and define a scalable road map across the technologies, operating model, and workforce. The goal is to build a reusable decision-and-action layer that learns continuously, delivering greater value as more workflows and restaurants are connected.
Discover the intelligent enterprise
Are you designing for what’s next?
{{filterContent.facetedTitle}}
{{contentList.dataService.numberHits}} {{contentList.dataService.numberHits == 1 ? 'result' : 'results'}}
{{contentList.loadingText}}
{{contentList.loadingText}}
Follow us
