The New KDS and AI Roadmap for Restaurants

Artificial intelligence (AI) is increasingly penetrating the restaurant industry, presenting both opportunities and challenges for operators. Patrick Bobrukiewicz, vice president of AI strategy and innovation at Thrive Restaurant Group, highlights the fragmented state of data management in many restaurants, which hampers the effective implementation of AI. He notes that vital training materials, operating procedures, and brand standards are often scattered across various formats, such as shared drives and emails, creating inefficiencies in leveraging AI technology.

Bobrukiewicz explains that while structured data—such as sales and inventory—exists within restaurant operations, the need has arisen to consolidate it in a way that promotes deeper inquiry. He argues that instead of merely generating reports post-facto, the focus should transition toward predictive analytics. Managers could benefit from being able to query systems directly about labor costs, menu changes, or product shortages, rather than piecing together data retrospectively. This shift could significantly enhance operational efficiency and decision-making.

He illustrates this future vision with a critique of current practices: “A P&L is like reporting on the news the week after the weather is already done,” he states, emphasizing the need for forecasting rather than mere reporting. At Thrive, efforts include utilizing Google’s Gemini Enterprise to bridge the gap between structured and unstructured data, enabling operators to understand not just what has happened, but why certain outcomes occur.

The strategy follows Bobrukiewicz's framework of the “three threes” which includes ensuring data integrity, defining business language for the AI systems, and then developing a discovery layer for analysis. He cautions against rushing into dashboard creation without first establishing clean data and a coherent understanding of the terminology used within the organization. Prematurely jumping to data visualization risks operational misalignment and ineffective insights.

As the industry navigates these advancements, the focus will likely remain on how effectively restaurants can integrate AI and predictive analytics into their daily operations. Operators will benefit from understanding these developments and adapting their data practices accordingly. The ongoing evolution may depend on the industry’s ability to unify data sources and refine AI applications for practical use in restaurant management.