How Retail Finance teams are using Agentic AI to protect omni-channel margins

Summary

Omni-channel retail has spread margin, cash, and markdown decisions across more channels, fulfillment paths, and return routes, while agentic systems increase the speed and complexity of change. The post presents ontology as a way to preserve the meaning and business context behind finance figures, keeping definitions, channels, and cost drivers current. Databricks Genie is described as a data-smart AI coworker that answers finance questions in plain language, grounding responses in an evolving ontology, source traces, permissions, and governed AI costs. It focuses on margin after fulfillment and returns, inventory cash tied up in the wrong place, and full-price revenue at risk from markdowns and returns, then prepares actions for a person to approve. Unilever deployed Genie to more than 1,200 finance and business users; analysis that took days now takes minutes, with expected multi-million-euro annual cost avoidance.

Takeaways

1. 1

   Genie’s finance use cases cover margin by channel and product after fulfillment and returns, cash tied up in overstocked or misplaced inventory, and full-price revenue at risk from markdowns and returns.

2. 2

   Genie does not make pricing, promotion, or markdown decisions; it gives finance a governed view of emerging risks so the relevant business owners can act.

3. 3

   Unilever brought Genie to more than 1,200 finance and business users, with analysis time falling from days to minutes and expected multi-million-euro annual cost avoidance.