AI in Food Service: Real-Time Energy & Resource Optimization
- James McGreggor
- May 28
- 3 min read
Updated: 2 days ago
Overview
This use case focuses on how AI can turn QSR locations into responsive, energy-efficient environments—reducing utility costs, extending equipment life, and advancing sustainability without affecting guest experience or staff performance.
The Challenge
Most QSRs still operate their energy systems on fixed schedules, regardless of customer volume or environmental conditions. This leads to inefficient energy use—HVAC and kitchen equipment running during slow periods, lights blazing during daylight hours, and systems straining through high-cost grid hours. Over time, this not only inflates utility bills but also shortens equipment lifespan and undermines sustainability goals.
The Opportunity
AI is capable of enabling responsive energy optimization by adapting in real time:
Adjust HVAC zones and lighting based on occupancy and weather forecasts.
Automate transitions between active, standby, and idle modes for kitchen equipment.
Align energy use with utility grid signals for demand response participation.
Detect anomalies in energy consumption that signal equipment issues early.
Notably, Wingstop has introduced a “Smart Kitchen” system that uses AI and machine learning to forecast restaurant needs in 15-minute intervals. By analyzing hyperlocal data—like weather and events—it proactively adjusts operations to improve efficiency and reduce wait times. This kind of granular prediction model can be extended to energy and resource optimization, allowing QSRs to react to demand with precision rather than guesswork.
By layering predictive analytics on top of smart sensors and control systems, AI optimizes resource use hour by hour, store by store—without interrupting operations.
The Approach
Start by identifying locations with unusually high utility usage or maintenance issues. Build a charter with sustainability, facilities, and operations leaders to set measurable goals—such as reducing energy costs, improving uptime, or extending equipment life.
You’ll need:
Inputs: Equipment telemetry, occupancy sensors, weather forecasts, utility pricing data
Expertise in: AI modeling, IoT integration, control systems, predictive maintenance
Supported by: Operations, facilities, IT, energy consultants, sustainability leads
Begin with a small pilot across stores with varied climates or equipment setups. Monitor energy consumption before and after AI optimization, and feed alerts into your maintenance systems to detect early signs of failure—before they disrupt service.
What to Watch Out For
Sensor calibration issues: Garbage in, garbage out—faulty readings lead to costly miscalculations.
Resistance to automation: Give staff simple override options to maintain comfort and control.
Underappreciated long-tail ROI: Many gains are marginal per store but material across a multi-location portfolio.
Operational complexity: The system should be intuitive—avoid bloated, brittle automation setups.
The Impact
QSRs that adopt AI for real-time energy and resource management have the opportunity to reduce utility costs, increase equipment lifespan, and boost ESG performance. Over time, reduction in utility costs unlocks cumulative savings and supports greener operations—without compromising speed or service. However, as Dan Crosby, Founder and CEO of Legend Energy Advisors: “It doesn't matter whether you're trying to save money or save the environment, you're going to do both of those things through efficiency.”
Partner With Us
At Blue Forge Digital, we help QSRs turn operational data into intelligence. Let's work together, align with your sustainability goals, and build a smarter footprint.
References
Wingstop – Smart Kitchen SystemBurt, Kayla. "Wingstop Launches AI-Powered Smart Kitchen to Reduce Wait Times." The U.S. Sun, 16 Nov. 2023, https://www.the-sun.com/money/14297446/wingstop-smart-kitchen-service-time-fast-food/. Accessed 27 May 2025.
Dan Crosby – Legend Energy Advisors QuoteCrosby, Dan. “Traversing the Data Center Power Market with Legend Energy Advisors.” Data Center Frontier Podcast, hosted by Rich Miller, 17 Aug. 2023, https://www.datacenterfrontier.com/podcast/article/33014455/dcf-show-traversing-the-data-center-power-market-with-legend-energy-advisors. Accessed 27 May 2025.