IHL Group estimates the US POS terminal market at $9.0 billion in 2025, projected to grow to $9.4 billion by 2030, representing 4.1% total growth at a 0.8% CAGR. The United States holds the number one largest POS installed base globally, reflecting the world’s most mature and sophisticated retail technology ecosystem. Total US retail sales reached $6.4 trillion in 2025, up 4.6% from 2024, with per capita retail sales of $18,610. Online sales represent 20.6% of US retail trade, generating significant omnichannel technology investment pressure that drives continuous POS modernization across all retail segments.

Over 90% of Payment terminals shipped in the United States now support NFC-based contactless payment acceptance, representing a fundamental shift from the US market’s historically slow contactless adoption relative to European peers. The next evolution beyond contactless is agentic AI at checkout: systems where AI autonomously handles order routing, inventory confirmation, and personalized offer presentation within the transaction flow. IHL research tracks active deployments of agentic AI checkout technology across quick-service restaurant, grocery, and mass merchandise segments, where the highest transaction volume justifies the infrastructure investment required for autonomous checkout operation.

Cloud-native POS adoption in the US varies significantly by segment: IHL data shows 70-95% cloud adoption depending on the retail vertical, with hospitality and quick-service restaurants leading and traditional grocery and mass merchandise more mixed due to legacy infrastructure scale. Cloud-native architectures enable faster feature deployment, centralized update management, and integration with AI services that on-premise systems cannot access. Order Management Systems are increasingly functioning as operational hubs connecting cloud POS to inventory, fulfillment, and customer data, making the OMS-POS integration the most strategically important architectural decision for US retailers modernizing their store technology stacks.

AI is affecting US retail labor management in several ways documented in IHL Group research. AI-enabled scheduling systems are optimizing checkout staffing to match transaction volume patterns, reducing both labor cost and customer wait time. Computer vision at self-checkout is improving shrink detection while reducing false positives that slow legitimate transactions. AI-driven demand forecasting is reducing inventory positioning errors that create out-of-stock situations at checkout, one of the most direct contributors to lost sales. IHL tracks that retailers with AI-enabled store operations show measurable advantages in both labor productivity and customer throughput compared to retailers without AI integration in store systems.

Cloud-native POS is growing in enterprise retail, but legacy on-premise systems still remain common in large chains with older store infrastructure. Current numbers show cloud-based systems in the 20-75% range depending on segment

Applied AI is typically used to improve labor scheduling, reduce out-of-stocks, and lower inventory distortion through better forecasting and exception detection. This impacts POS due to the greater inventory visibility associated with updated systems.

The latest data (2024) shows that over 90% of payment terminals shipped included EMV chip readers with built-in NFC and mobile wallet support, such as Apple Pay, Google Pay, and Samsung Wallet. Older systems will lag, but more than 50% of in-store transactions are contactless.

Retailers are moving from basic AI insights to operational AI execution by upgrading POS stacks, middleware, and connected workflows that can act in real time.

Agentic AI is starting to appear in assisted selling, product discovery, and customer support, but broad real-time deployment at the POS is still early.