Retailers face challenges optimizing AI agents for shopping recommendations

Retailers are increasingly using AI-powered search and shopping assistants, with 44% of users preferring AI search over traditional methods, according to McKinsey, as reported by Retail Dive.

However, simply integrating AI agents is not enough; retailers must optimize product data, pricing, and inventory in real time to meet AI agents' standards and influence shopper decisions effectively.

Gartner predicts that over 40% of AI projects in commerce will be canceled by 2027 due to rising costs, unclear business value, and insufficient risk controls, all linked to poor planning and measurement.

Only 32% of commerce organizations have fully defined AI success metrics, and just 27% have unified customer data across departments, making it difficult to assess AI investments' effectiveness, according to Salesforce data.

Experts advise that retailers can make progress using existing commerce infrastructure without a complete AI transformation, with reported conversion gains between 5% and 15% from AI-enabled experiences, as noted by BCG and InMobi's CEO.

Improving product data quality and commerce systems enhances how products appear across AI assistants, search engines, marketplaces, and retailers' digital channels, providing immediate value for retailers.