Transitioning a massive retail enterprise away from legacy reporting required a comprehensive focus on change management. Instead of performing a standard one-to-one migration of its ~2,000 manual Excel files into static dashboards, the internal data team restructured the entire reporting ecosystem. The team conducted immersive internal workshops to map out the exact operational flow of category managers and business heads during critical review periods. This exercise revealed that users do not simply look at isolated charts; they follow a specific analytical narrative—beginning with overall category health, drilling down into brand performance, and ending on specific problematic SKUs.

The enterprise coded these real-world business flows directly into its Looker semantic layer and used them to train conversational AI agents built with Gemini. This approach unlocked an intelligent ‘Talk to Data’ capability, allowing non-technical category managers to query complex retail performance metrics using simple, natural language conversations.

CRF’s modern architecture on Google Cloud delivered substantial business value across both internal workflows and consumer-facing channels. In customer service operations, the company deployed an intelligent customer service chatbot that now automatically manages 50% to 60% of incoming customer queries. The bot allows users to track order statuses, verify product availability based on store proximity, and seamlessly allow human in loop through agent callback option, significantly reducing customer wait times and lowering support costs.

In marketing, TOPS under Central Retail Food used Google Cloud to transition away from flat, threshold-based promotional campaigns that frequently caused gross profit margin erosion. By enriching product metadata and historical transaction logs within BigQuery, the team launched a hyper-personalized marketing model. This system segments customers into distinct groups based on their purchasing habits and lifestyle preferences, such as being ‘conscious parents,’ ‘vegan lovers,’ ‘health and wellness,’ and others, allowing the business to deliver highly targeted offers that maximize campaign conversion rates while directly protecting gross profit percentages.

The enterprise is utilizing Gemini Enterprise Agent Platform pipelines to productionize high-granularity, item-level demand forecasting models capable of absorbing immense data volumes.

Moving forward, CRF plans to scale these retail forecasting models directly into its wholesale operations to maximize existing infrastructure utility. The team is also working toward reducing offline data latency to a 30-minute window to better support AI agent requirements. CRF’s three-year strategy focuses on scaling agentic AI to completely automate Standard Operating Procedures (SOPs) for store associates and back-office staff at the Store Support Center, giving employees valuable time back to focus on customer engagement and unlocking future opportunities to safely monetize data insights for suppliers.