An AI overhaul at Macy’s is fueling the 168-year-old retailer’s turnaround

Macy's is implementing AI across its operations, including a virtual try-on assistant that increases spending per session by five times and machine learning tools for demand forecasting and manager training. This modernization effort represents a significant strategic pivot for the 168-year-old retailer to compete in digital commerce.

Macy's deployment of AI across its business model demonstrates how legacy retailers are leveraging machine learning to reverse declining performance. The virtual try-on assistant's ability to quintuple spending per session suggests that AI-driven personalization directly addresses friction points in online shopping, particularly for fashion items where fit uncertainty drives cart abandonment. This technology converts passive browsing into active purchasing by reducing purchase risk.

The retailer's broader AI implementation—from demand forecasting to manager training—reflects a shift in how traditional businesses modernize. Rather than isolated digital initiatives, Macy's treats AI as an infrastructure layer optimizing inventory, staffing, and operations simultaneously. Demand forecasting reduces overstock and markdowns, two persistent challenges in retail that compress margins. Training managers through AI tools standardizes execution across dispersed locations, improving consistency in customer experience.

For the broader retail sector, Macy's case illustrates that AI adoption isn't merely incremental improvement—it can fundamentally alter unit economics. A five-fold increase in session spending suggests the technology removes conversion barriers that have persisted despite years of traditional optimization. This has implications for investors evaluating whether legacy retailers can compete with digital-native competitors through technology investment rather than store closures.

Watch whether Macy's can sustain this momentum as it scales these tools. Implementation challenges—data quality, employee adoption, integration with legacy systems—often derail enterprise AI projects. The success or failure of this turnaround will signal whether traditional retail can achieve meaningful digital transformation or whether competitive pressures ultimately require structural business model changes.

• →Virtual try-on AI increased spending per session by 500%, directly addressing online fashion retail's primary conversion barrier
• →Machine learning demand forecasting reduces inventory inefficiencies that have historically pressured retail margins
• →AI manager training tools standardize operations across Macy's dispersed store network
• →Legacy retailers demonstrate viability through technology-driven transformation rather than traditional cost-cutting
• →Success depends on overcoming enterprise AI implementation challenges across systems and workforce adoption