A recap of Fourth’s webinar with Deloitte, highlighting AI research insights on where restaurant operators are seeing real ROI today.
As restaurant margins tighten, AI is moving from a speculative investment to something operators expect to prove its value.
In June 2025, Deloitte published How AI Is Revolutionizing Restaurants, drawing on a global survey of 375 restaurant executives across 11 markets. Their research set out to answer a question many operators were already asking:
Where is AI actually delivering measurable return on investment in restaurants today?
That question was at the center of a webinar hosted by Fourth, featuring Evert Gruyaert, U.S. Restaurants & Food Service Leader, Principal at Deloitte Consulting LLP, alongside Clinton Anderson, CEO of Fourth. While the discussion looked at restaurant profitability in 2026, it was firmly grounded in Deloitte’s 2025 AI research and real-world operator experience, cutting through hype to focus on what’s working now.
Below are the key takeaways from that conversation, and what Deloitte’s research reveals about where AI is actually delivering ROI in restaurant operations today.
One of the clearest signals from Deloitte’s research is that AI is no longer viewed as experimental.
When asked about future investment plans, 73% of restaurant executives said they expect to increase AI investment somewhat in the next fiscal year, and another 9% said they expect to increase it significantly, while only 2% forecast a decrease.
That high level of commitment is notable in an environment where operators are still working to balance labor, supply chain pressure, and flat traffic. It shows that many leaders believe AI can move beyond buzz to tangible business impact.
What’s changed since earlier waves of AI experimentation is intent. Rather than deploying AI broadly, many organizations are becoming more selective, focusing on a small number of use cases where value can be clearly demonstrated.
As Gruyaert shared during the webinar, restaurants that pursue too many AI initiatives at once often struggle to generate meaningful results. The organizations seeing ROI are intentional, focused, and disciplined about how they measure success.
Deloitte’s research shows strong global alignment around where AI is expected to drive value. Across regions and concept types, three priorities consistently rise to the top:
These priorities aren’t accidental. They represent areas where restaurants already have rich data, clear ownership, and the ability to measure outcomes, making them natural starting points for AI investment.
Customer experience and marketing use cases, such as frictionless ordering, personalization, and loyalty, have been delivering value for several years. These applications tend to be more mature, with clearer adoption paths and more immediate top-line impact.
What stands out in Deloitte’s research, and throughout the webinar discussion, is the growing emphasis on operations. More restaurant leaders are now looking to AI not just to drive incremental revenue, but to help control costs, reduce waste, and improve execution at the store level.
This shift reflects a broader change in motivation. As pricing power tightens and margins come under pressure, operators are prioritizing AI use cases that improve forecasting accuracy, align labor and inventory to demand, and surface issues early enough to act, before costs are locked in.
In other words, AI is increasingly being evaluated not by how innovative it looks, but by how reliably it improves day-to-day operations.
Operational efficiency has always been one of the hardest challenges to scale consistently across restaurant locations, not because managers lack experience, but because the decisions they make depend heavily on timing and visibility.
Managers are responsible for forecasting demand, scheduling labor, managing inventory, and responding to real-time issues, often with incomplete information and little margin for error. When something goes wrong, the financial impact typically shows up after the fact, once the shift is over or payroll has already closed.
Deloitte’s AI research shows that this is where AI is beginning to deliver tangible economic value.
Operational AI use cases, particularly in forecasting and inventory management, are among the most widely adopted today because they address a core problem: decisions are often made too late to change the outcome.
AI-driven operational tools help shift that dynamic by allowing restaurants to:
In the survey, Deloitte notes that restaurants are already seeing economic value from AI in both customer experience and inventory management, two areas where AI operates close to the moment of execution and can influence outcomes in real time.
During the webinar, both Deloitte and Fourth emphasized that this move from reactive, post-shift analysis to proactive, in-shift decision-making is where many restaurants are beginning to see meaningful ROI from AI investments.
A consistent theme from Deloitte’s research is that AI success in restaurants is less about technological sophistication and more about execution discipline.
With no shortage of potential AI use cases available, many restaurant organizations face the temptation to pursue too many initiatives at once. Deloitte’s findings echoed throughout the webinar discussion, suggest that this approach often dilutes impact and slows adoption.
Restaurants seeing real returns from AI tend to take a more focused approach. They:
This discipline matters because restaurants operate in an environment where even small disruptions can have outsized consequences. Introducing new technology without clear goals or measurable outcomes increases complexity for operators and erodes confidence at the store level.
By contrast, focused AI initiatives that deliver visible improvements, whether in forecasting accuracy, inventory control, or day-to-day decision-making, build credibility quickly. They show operators that the technology is designed to support their work, not complicate it.
In an industry where execution happens one shift at a time, clarity and consistency are often more valuable than breadth. Deloitte’s research reinforces that focus is not a constraint on innovation, it’s what turns experimentation into sustained impact.
Deloitte’s research highlights a critical challenge for many restaurant organizations: while enthusiasm for AI is high at the corporate level, readiness to deploy and scale it effectively often lags behind.
In particular, many respondents report feeling underprepared across foundational areas such as strategy, operating models, and technology infrastructure, with the largest gaps appearing in risk management and governance.
For example:
In restaurants, this gap tends to surface most clearly at the store level. Corporate teams may see AI as a strategic lever, while operators experience it as another system layered onto already complex workflows. When tools are introduced without clear use cases, guardrails, or success metrics, skepticism grows quickly, even when the underlying technology is sound.
Deloitte’s research and the webinar discussion point to the same conclusion: collaboration is not a “nice to have”, it’s a requirement for AI ROI.
Organizations that bring operators into the process early, defining use cases together, aligning on what success looks like, and pressure-testing solutions in real operating conditions, are more likely to see adoption stick and value scale. Operators live with the consequences of technology decisions every day, and their input helps ensure AI initiatives support execution rather than disrupt it.
Closing the gap between corporate strategy and store-level reality is less about changing ambition and more about changing approach, grounding AI investments in the realities of daily restaurant operations.
The takeaway from Deloitte’s AI research is not that AI is a cure-all for restaurant challenges. It’s that AI has matured to the point where it can deliver measurable value, when it’s applied intentionally and tied to real operational decisions.
Restaurants seeing ROI today are not chasing novelty or experimenting for experimentation’s sake. Instead, they are:
What distinguishes these organizations is not access to better technology, but a clearer understanding of where AI fits into their operating model. They view AI as a tool for improving execution, not replacing judgment, and they invest accordingly.
As restaurants look toward 2026, Deloitte’s research offers a grounded view of where AI is already paying off today. It also reinforces a critical point: ROI doesn’t come from how advanced an AI tool is, but from how well it’s integrated into daily decision-making.
The path to AI ROI in restaurants is paved with focus, collaboration between corporate and operators, and the discipline to measure what actually moves the business forward.
To hear more context behind these insights, you can watch the full webinar featuring Deloitte and Fourth on demand.
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