CFVG Embracing AI to Elevate Foodservice

Regional differences in AI adoption were also men - tioned. Frampton, based in the Bay Area, observed that his region is “far advanced from the rest of the country” with “everybody here is using AI throughout the day for different reasons.” He expressed a strong conviction that AI “is going to change this industry more than anything ever has” and committed to going “all in” both personally and professionally. Klinger corroborated high adoption in the Boston area, influenced by institutions like MIT and Harvard. He described how Boston University has “dove in both feet first” with AI task forces and initiatives, building a data science facility to apply research. Poye recalled an MIT course advising to treat AI (like ChatGPT) like an intern: providing solid advice, spe- cific details, context, and objectives to help it “think.” He also humorously mentioned the advice My take on it is it’s going to change this industry more than anything ever has. We’ve had a lot of different things come and go, but we feel that it’s going to change every aspect, not just food, but how we train, how we track things and everything.

to “treat the thing well.” Klinger noted that he finds it interesting that younger generations, with different communication styles, might struggle with proper prompting compared to older generations used to clear sentences. Poye expanded on this, suggesting that regional differences in language and questioning could impact AI interaction in multi-state operations. Finally, Weber summarized the overarching future vision for AI in retail: “The differentiator will come in when you have a single data lake , and then you have AI connected across all the actual workflows.” He emphasized that while individual AI initiatives are manageable, the real challenge for small to medium businesses is to achieve a “one data language” across all disparate systems, enabling them to communi- cate and unlock AI’s full transformative potential.

Final Reflection AI in foodservice and retail is less a distant innovation and more a practical tool that can transform daily operations. Concerns about data quality or resource limitations can be addressed through approaches like “global training,” which generates accurate forecasts even from incomplete data. The human element is equally crucial. Long-tenured employees may resist change, requiring patience, communication, and training, particularly in AI prompting to empower staff and align insights with business expertise. Looking ahead, the potential of AI lies in unifying disparate data streams into a single, connected framework. When systems can communicate across workflows, AI becomes not just a technology, but a strategic enabler, reshaping efficiency, decision-making, and competitiveness in foodservice and retail.

A data lake is a centralized repository that stores vast amounts of raw data in its native format. Unlike a data warehouse, which typically stores structured and formatted data, a data lake can handle structured, semi-structured and unstructured data. This allows organizations to store data as it is, without needing to structure it first, making it flexible for various analytical and machine learning purposes.

Brandon Frampton , Director of Fresh Food, Loop & Poppy (Loop Neighborhood Market)