Every AI ambition eventually runs into the same reality: a model is only as good as the data it's built on. For many retailers, that data is scattered across systems, inconsistently owned, and far less reliable than anyone would like to admit. These panelists will discuss how to audit data quality, access, and lineage, prioritize the datasets that unlock the highest-value use cases, and put the governance, pipelines, and feedback loops in place so AI has a foundation it can truly depend on. Attendees can expect to hear about:  

• Auditing data quality, access, and lineage to see what AI can reliably act on 
• Prioritizing the datasets and connections that unlock the highest‑value use cases 
• Establishing data governance, ownership, and standards that AI can depend on 
• Building pipelines and feedback loops so models keep learning from fresh signals