AI agents offer restaurant businesses a practical way to streamline operations, raise quality, and cut costs, without needing a full-scale transformation project. These tools aren’t vague future tech. They’re focused, intelligent systems that take care of specific, repetitive tasks, often better and faster than a human could.
From scheduling smarter rotas to automating P&L reports, AI agents are already being used in restaurants to solve real problems and generate real returns.
In this article, we explore what AI agents are, and share 10 use cases that could be built and deployed in your restaurant business today.
What are AI agents?
AI agents are task-specific software tools that operate independently. Unlike generic tools like ChatGPT, these agents are goal-driven. They’re designed to take inputs, make decisions, and carry out actions on your behalf, without being told what to do every time.
You can think of them like digital team members, each built to take care of a particular task (often faster, more accurately, and more consistently than a human could).
10 Examples of AI Agents you could build for your restaurant chain
1. Prep volume forecaster
The problem: Kitchen staff often prep too much or too little food due to inaccurate forecasts.
What the agent could do: Ingest historical sales data, weather, events, and booking trends to recommend optimal prep volumes by hour, day, and menu category. This agent helps reduce food waste and ensure availability of high-demand items.
2. Smart rota optimiser
The problem: Managers spend hours creating schedules, often over- or under-staffing shifts.
What the agent could do: Analyse forecasted demand, employee availability, and labour budgets to auto-generate optimised rotas that maintain service quality while reducing excess staffing costs.
3. Fridge/freezer compliance watchdog
The problem: Manual temperature checks are missed or delayed, risking food safety compliance.
What the agent could do: Monitor IoT-connected fridges and freezers, track temperature thresholds, and trigger instant alerts or service requests when anomalies occur, ensuring compliance and reducing spoilage.
4. Weekly P&L summary compiler
The problem: Compiling cross-site performance summaries is time-consuming and error-prone.
What the agent could do: Pull sales, labour, and inventory data from POS and back-office systems, then generate clean, executive-ready performance summaries for each site and area manager, saving hours of manual collation each week.
5. Voice order assistant
The problem: Drive-thru and phone orders are error-prone, slow, and heavily reliant on staff training.
What the agent could do: Use AI to answer phone orders or drive-thru inputs, interpret customer intent accurately (even in noisy environments), and send the order directly to the KDS or POS, increasing accuracy and throughput.
6. Smart inventory reorder agent
The problem: Teams often over-order to “be safe,” increasing waste and tying up capital.
What the agent could do: Continuously track stock usage, sales patterns, and shelf life to recommend just-in-time reorders, reducing waste and freeing up cash flow without risking stockouts.
7. Quality control vision agent
The problem: Inconsistent plating or portion sizes impact customer experience and margins.
What the agent could do: Use cameras at prep stations to analyse final dishes against standard templates. Flag anomalies (e.g., underweight portions or poor presentation) in real time, helping maintain consistency and reduce customer complaints.
8. Pricing & menu optimisation engine
The problem: Menu pricing is often guesswork, and poor performers stay on the menu too long.
What the agent could do: Analyse item-level margin data, customer purchasing behaviour, and market trends to suggest price adjustments, identify menu cannibalisation, and recommend optimised product line-ups per location.
9. Staff onboarding & FAQ agent
The problem: High turnover creates constant training needs and inconsistent onboarding.
What the agent could do: Provide new team members with a chatbot that answers operational questions (e.g., “how do I log into the till?” or “what’s the spec for a BBQ burger?”), reducing burden on managers and speeding up onboarding.
10. Customer sentiment monitor
The problem: Customer reviews are often acted on too late, if at all.
What the agent could do: Automatically scan online reviews and survey data, detect sentiment trends, and highlight urgent issues (e.g., “slow service on Saturday afternoons”) to area managers with actionable recommendations.
Where should you start?
There are two great places to spot agent opportunities:
Is there a mental task that a human is doing now, especially if it is repetitive, slow, or error-prone?
If so, it’s a prime candidate for an AI agent.
If you had unlimited staff, what would you love someone to take care of?
AI agents can take on jobs that were previously ‘wished for’ but never practical at scale.
How to Get Started
In our experience, once teams begin exploring AI agents, the challenge isn’t finding use cases, it’s choosing where to begin.
That’s why Steer73 offers Free AI Agent Discovery & Roadmap Creation Projects.
You’ll work with an experienced team to:
- Identify high-impact opportunities for AI agents in your operation.
- Assess each based on ease of implementation, ROI potential, available tech, and strategic fit.
- Walk away with a prioritised roadmap of what to build first, and how.
This process helps ensure you focus on the highest-leverage use cases, avoid common pitfalls, and start generating value quickly.
If you are interested in taking advantage of a Free AI Agent Discovery & Roadmap Creation project, drop the words “Agent discovery” in an email to hello@steer73.com and we will share some further details.