The experimentation phase of AI in commerce is ending. More than a third of agentic AI users say their primary focus has shifted from pilots and experimentation to scaling AI across functions and teams (Salesforce Commerce Blog). The question for commerce leaders has shifted from "Does this work?" to "How do we do this at scale?" Current implementations focus on customer-facing use cases like autonomous customer service, AI shopping concierges, and exception handling, but organizations are increasingly applying AI agents to complex operations like supply chain management and merchandising optimization.
Scaling AI exposes foundational data problems that small pilots masked. More than 6 in 10 organizations cite poor data integration (63%), lack of a defined AI strategy (63%), and poor data quality (62%) as major or moderate barriers to AI success (Salesforce Commerce Blog). Only 27% of organizations report having fully unified customer data across sales, service, marketing, and commerce teams, yet those with unified data report 40% better AI and automation outcomes and 40% stronger customer retention (Salesforce Commerce Blog). Commerce leaders are prioritizing data unification as step one: consolidating systems of record, fixing pricing and inventory sync issues (which affect 98% of multi-channel sellers), auditing integration points between core systems, defining data ownership, and building metrics before scaling further (Salesforce Commerce Blog).