
The Next Now of Retail
Operationalize Agentic AI & Unified Commerce at Enterprise Scale

January 11–13, 2026

Jacob K. Javits Convention Center, NYC
Events / NRF 2026:Retail's Big Show
Four Pillars of Retail AI
From demand sensing to autonomous fulfillment, here's what production-ready retail AI actually looks like, and what it delivers.
Intelligent Demand Sensing
Autonomous AI agents that compress demand planning cycles from days to hours, continuously learning from transactions, inventory signals, and market conditions.
Self-Driving Retail Operations
AI workflows that don't just recommend, they execute. Pricing, allocation, and replenishment decisions fire automatically with human governance for exceptions.
Unified Commerce Intelligence
Break down silos between stores, digital, and supply chain. Real-time visibility across channels enables coordinated execution and consistent customer experience.
Resilient Supply Networks
Fulfillment networks that anticipate disruption before it hits. Predictive exception handling and dynamic routing keep promises to customers, profitably.
Solutions Portfolio
Intelligent Retail Operations at Scale
How a leading grocery retailer transformed reactive operations into a continuously learning AI system, embedding autonomous decision-making directly into daily workflows.
Siloed data across 800+ stores made it impossible to understand what was happening, let alone why. Teams spent more time reconciling reports than making decisions.
Real-time signals across transactions, pricing, inventory, and demand were unified into a single intelligence layer. Causal analytics enabled teams to understand not just what changed, but where and why, moving beyond static reporting.
Agent Garage-powered decision intelligence now continuously monitors retail performance, surfaces emerging risks and opportunities, and recommends actions in near real time, embedding AI-driven decision-making directly into daily operations.
Ready to Scale Your AI Operations?
Meet us at NRF 2026 to discuss how we can help you move from experimentation to enterprise execution











