How can artificial intelligence improve your restaurant's operations?

By leveraging AI, managers can efficiently identify exceptions in inventory, as the system can pinpoint discrepancies caused by inaccurate counts, improper portioning, or missing invoices. This eliminates the need for manual data analysis to locate inventory discrepancies. The AI can also identify specific employees responsible for over or under portioning, enabling brands to focus on addressing the root causes and implementing corrective actions, rather than spending excessive time uncovering the issues. Additionally, the use of computer vision technology eliminates the need for manual inventory counting, as the AI can accurately assess on-hand inventory, pending orders, and generate suggested orders to maintain optimal stock levels.

Effective inventory management is a core challenge for food service operators. Artificial intelligence can empower restaurants to tackle this obstacle. By analyzing sales records, menu items, and purchasing trends, AI determines the quantity of each ingredient needed over various periods. Systems then advise ideal stock levels and automatically trigger reorders as projected demand dictates. This data-driven process curtails overstocking expenses while avoiding stockouts that hamper business. By bringing predictive analytics and automation to stock planning, operators gain powerful tools to strengthen operations and the client experience.

You can use AI to track what you have, and project forecasts for what you need to buy in, order ahead, when you'll run out, and so much more based on previous data.

Optimizing kitchen operations often involves prioritizing orders, a task typically handled by an expediter. This role is crucial for directing kitchen staff on which orders to prioritize. An AI engine can enhance this process by monitoring each station and dynamically reordering tickets based on real-time conditions, ensuring maximum productivity and optimal food quality, with hot dishes remaining hot and cold dishes staying cold. Additionally, AI can track capacity and provide other systems with current capacity information and estimated order delivery times. Implementing such technology would significantly improve current processes, leading to increased throughput and sales.

Traditionally, analytics has primarily focused on retrospective analysis and scorecarding, lagging in real-time insights. However, predictive analytics has evolved, enabling the anticipation of outcomes before they occur. The most significant opportunity now lies in the integration of AI with business intelligence, which can not only predict outcomes but also recommend actions with predictable results. Consider marketing, where the adage goes that 50% of efforts are effective, but it's unclear which half. What if, with AI, we could ensure that 100% of marketing efforts are successful?