AI forecasting is the most proven use case in restaurant operations today. Learn why it drives measurable ROI in labor, inventory, and execution.
Forecasting has always been one of the most important, and most fragile, parts of restaurant operations.
Every schedule, prep plan, and inventory order depends on it. When forecasting is off, the consequences show up quickly: overstaffed shifts, understaffed rushes, unnecessary waste, or missed sales because the right product wasn’t ready at the right time.
That’s why, as AI adoption has accelerated across the industry, forecasting has consistently emerged as one of the most proven and reliable use cases in restaurant operations.
According to Deloitte’s How AI Is Revolutionizing Restaurants research, published in June 2025 and based on a global survey of 375 restaurant executives across 11 markets, forecasting and inventory management are among the operational AI applications already delivering measurable value today.
This theme featured prominently in a webinar hosted by Fourth, where Evert Gruyaert, U.S. Restaurants & Food Service Leader and Principal at Deloitte Consulting LLP, joined me to discuss where AI is delivering real operational impact, and why forecasting continues to lead the way.
Some AI use cases promise long-term transformation. Forecasting delivers value much sooner.
Restaurants already generate enormous amounts of data through POS systems, historical sales, menu mix, and traffic patterns. The challenge has never been a lack of data, it’s been turning that data into decisions quickly enough to matter.
Traditional forecasting approaches struggle in this environment. Averages smooth out real variability. Weekly plans assume conditions won’t change. And manual adjustments often happen only after service is already under strain.
AI changes that equation by processing more inputs, more frequently, and with far less manual effort. As a result, forecasting became one of the first operational areas where restaurants could see tangible improvements without fundamentally changing how stores run.
Most forecasting failures in restaurants don’t come from poor effort or bad management. They come from timing.
In many organizations, forecasts are built well in advance and reviewed after the fact. If demand runs higher than expected, teams scramble. If it runs lower, labor and food costs are already committed.
We discussed during the webinar that this “plan first, react later” model limits a restaurant’s ability to change outcomes. By the time performance is analyzed, the opportunity to intervene has passed.
Deloitte’s research reinforces this point. Forecasting creates the most value when it informs decisions before and during the operating day, not just after results are tallied.
AI doesn’t just improve forecast accuracy. It changes how forecasts are used.
Instead of relying on a static number built days in advance, AI-driven forecasting allows restaurants to:
In practice, this means fewer extreme corrections and fewer late surprises. Managers can see trends forming earlier and make smaller, more controlled adjustments, whether that’s reallocating labor, adjusting break timing, or slowing production.
Deloitte’s research shows that forecasting and inventory management stand out because they operate close to the moment of execution, where decisions still have financial impact.
Another reason forecasting continues to lead AI ROI is that it underpins nearly every operational decision in a restaurant.
Accurate forecasts influence:
When forecasts are unreliable, downstream decisions suffer. Schedules drift, overtime creeps in, waste increases, and managers spend more time reacting than leading.
The operators seeing the most value from AI are treating forecasting as a foundation, not a side project. They are using demand signals to align labor and inventory decisions across the business, creating more consistency shift to shift and location to location.
One of the most important shifts we discussed in the webinar is how AI is changing when decisions get made.
Instead of forecasting once and reviewing performance later, AI allows restaurants to forecast continuously and adjust inside the shift. That fundamentally changes the role of the manager.
With better forecasts and real-time updates, managers can:
This move from post-shift analysis to in-shift action is where forecasting delivers some of its strongest operational returns.
Forecasting remains one of the most proven AI use cases in restaurant operations because it solves a problem operators face every day.
It has:
Deloitte’s research and our conversations with operators point to the same conclusion: restaurants that forecast more accurately, and act on those forecasts earlier, are better positioned to control costs and execute consistently at scale.
As the industry looks forward in 2026, forecasting is likely to remain the foundation of successful AI adoption in restaurant operations.
Not because it’s the most advanced application, but because it’s the most practical.
AI-powered forecasting helps restaurants move away from averages, reduce last-minute scrambling, and make decisions with greater confidence. When paired with clear objectives and disciplined execution, it becomes one of the fastest paths to meaningful operational ROI.
To hear more context behind these insights, including how forecasting fits into a broader AI strategy for restaurant operations, you can watch the full webinar featuring Deloitte and Fourth on-demand.
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