Why Edge AI is crucial for retail and POS systems in 2025

In recent years, the retail industry’s growth has been modest, with annual rates ranging from 1.5% to 3.5% depending on the sector. Competition and rising consumer expectations for seamless omnichannel experiences have squeezed profit margins. With AI advancing so rapidly, there’s a great opportunity to embrace innovative solutions that boost efficiency and help create new revenue streams. Accordingly, IDC (2025) expects that by 2026, 90% of retail tools will embed AI algorithms. Furthermore, by 2027, over 45% of major retailers will apply Edge AI for faster decision-making and store-specific assortment planning, selection, allocation, and replenishment. Let’s have a closer look at how retailers can leverage Edge AI no matter their size and budgets.

Defining Edge AI in Retail Contexts

Edge AI refers to decentralized artificial intelligence systems that process data locally on in-store devices, e.g. POS terminals, smart shelves, Raspberry Pis, mobile phones, or cameras, rather than relying on distant cloud servers. This architecture works independently from distant cloud servers or internet connectivity, and therefore offline with minimized latency. Both, offline-capability and speed, are critical for applications like fraud detection and checkout automation. Accordingly, IDC emphasizes that 45% of retailers now prioritize “near-the-network” edge deployments. There, AI models run locally on in-store servers or IoT devices, balancing cost and performance.

Key Components of Edge AI Systems

For Edge AI to deliver real-time, offline-capable intelligence, its architecture must integrate on-device databases, local processing, and efficient data synchronization. These three pillars ensure seamless AI-powered retail operations without dependence on the cloud, minimizing latency, costs, and privacy concerns.

1. Local Data Collection, Sync, and Storage

Retail generates vast real-time data from IoT sensors, POS transactions, smart cameras, and RFID tags. To ensure instant processing and uninterrupted availability you need:

• On-device data storage: All kinds of devices from IoT sensors to cameras capture data. Depending on the device capabilities, with small on-device databases, data can be stored and used directly on the devices.
• Local central server: A centralized on-premise device (e.g. a PC or Raspberry Pi, or more capable hw) ensures operations continue even if individual devices are resource-limited or offline.
• Bi-directional on-premise data sync: Local syncing between devices and with a central on-site server ensures better decisions and fail-safe operations. It keeps all devices up-to-date without internet dependence.

2. Local Data Processing & Real-Time AI Decision-Making

Processing data where it is generated is critical for speed, privacy, and resilience:

• On-device AI models: Small, quantized AI models (SLMs) like Microsoft’s Phi-3-mini (3.8B parameters, <2GB memory footprint) can run directly on many devices (e.g. tablets, and POS systems), enabling real-time fraud detection, checkout automation, and personalized recommendations.
• Local on-premise AI models: Larger SLMs or LLMs run on the more capable in-store hardware for security, demand forecasting, or store optimization. 
• On-device & on-premise vector databases: AI models leverage on-device vector databases to structure and index data for real-time AI-driven insights (e.g., fraud detection, smart inventory management), fast similarity searches, and real-time decision-making.

3. Hybrid Data Sync: Local First, Selective Cloud Sync

• Selective Cloud Sync: Bi-directional cloud data sync extends the on-premise data sync. Select data, such as aggregated insights (e.g., sales trends, shrinkage patterns), payment processing, and select learnings are synced with the cloud to enable Enterprise-wide analytics & compliance, Remote monitoring & additional backup, and Optimized centralized decision-making.
• Cloud Database & Backend Infrastructure: A cloud-based database acts as the global repository. It integrates data from multiple locations to store aggregated insights & long-term trends for AI model refinement and enterprise reporting, facilitating cross-location comparisons. 
• Centralized cloud AI model: A centralized cloud AI model is optional for larger setups. It can be used to continuously learn from local insights, refining AI recommendations and operational efficiencies across all connected stores.

Use Cases of Edge AI for Retailers

Edge AI is unlocking new efficiencies for retailers by enabling real-time, offline-capable intelligence across customer engagement, marketing, in-store operations, and supply chains.

Enhancing Customer Experiences in Retail Stores with Edge AI – Examples

Edge AI transforms the shopping experience, enabling retailers to offer more streamlined and more personalized services based on real-time data, thereby boosting customer satisfaction and sales. Key benefits include:

• Realtime Product Recommendations: Using cognitive neural networks, retailers can respond instantly to a customer’s actions, such as browsing and purchasing, to recommend products that align with their preferences. An Accenture study found that 75% of consumers wish they could identify options that meet their needs more quickly and easily.
• In-store experience: AI tracks customer movement and analyses purchase patterns, optimizing store layout and product placement. A large global furniture retailer’s in-store analytics led to a more than 10 percent rise in in-store traffic and high sales growth within a month.
• Contactless Checkout: AI-driven self-checkouts allow customers to select products captured by cameras. Thus, bypassing the need for scanning product codes, which streamlines both standard and automated checkout processes. For example, Amazon’s Just Walk Out technology allows customers to pick up items and leave the store without traditional checkout, enhancing convenience and reducing wait times. 
• Real-Time Inventory Tracking: Smart shelves monitor inventory levels in real time, triggering automatic reorders and preventing stockouts. For example, a study proposed a smart shelf design capable of detecting the location and weight of items, ensuring accurate inventory counts and timely replenishment.

Retail operational excellence and cost optimization with Edge AI – Examples

Edge AI also significantly enhances operational efficiency, especially operational in-store efficiency, reduces losses, and helps lower costs (while at the same time enhancing sustainability):

• Supply Chain Management: Edge AI enhances supply chain operations by decentralizing data processing, enabling real-time analysis and faster decision-making. This leads to optimized inventory levels, more accurate demand forecasting, and reduced operational costs. For example, Walmart’s pioneering use of GenAI in supply chain management has driven a 100x productivity boost, enabling more accurate demand forecasting, optimized inventory, and reduced waste. As reported in its Q2 2025 earnings call, these improvements trimmed operational costs by 20%.
• Loss Prevention: Retail shrink, exacerbated by inflation-driven shoplifting and self-checkout vulnerabilities, costs the industry over $100 billion annually. Advanced sensors and intelligent cameras combined with Edge AI help detect early signs of theft or fraud. Thus, allowing security measures to intervene promptly, and independently from an internet connection. 
• Waste Reduction: Grocery chains like Tesco use Edge AI to analyze the expiry dates of goods and ripeness of produce, dynamically pricing items nearing expiration. This approach can reduce food waste by up to 40%. Food waste is a huge social, economic, and environmental challenge. If it was considered as a country, would be the world’s third greatest emitter of greenhouse gases. Edge AI in retail could play a pivotal role in food waste avoidance.
• Energy Savings: Smart sensors and Edge AI can be used to optimize the use of energy for lighting, heating, ventilation, water use, etc. For example, 45 Broadway, a 32-story office building in Manhattan, implemented an AI system that analyzes real-time data. That included temperature, humidity, sun angle, and occupancy patterns – to proactively adjust HVAC settings. This integration led to a 15.8% reduction in HVAC-related energy consumption. Plus, saving over $42,000 annually and reducing carbon emissions by 37 metric tons in just 11 months.

Conclusion: Edge AI as Retail’s Strategic Imperative

Edge AI is a true game-changer for retailers in 2025. Faced with rising costs and fierce competition, stores need faster insights and better local experiences to stand out. Therefore, according to IDC, 90% of retail tools will embed AI by 2026, with edge solutions expected to help 45% of retailers optimize local assortments. Meanwhile, according to McKinsey, 44% of retailers that have implemented AI already reduced operational costs, while the majority have seen increases in revenue. 

Yet, Edge AI isn’t just about running AI models locally. It’s about creating an autonomous, resilient system where on-device vector databases, local processing, and hybrid data sync work together. This combination enables real-time retail intelligence while keeping costs low, data private, and operations uninterrupted. To stay ahead, businesses should invest in edge-ready infrastructure with on-device vector databases and data sync that works on-premise at their core. Those who hesitate risk losing ground to nimble competitors who have already tapped into real-time, in-store intelligence.

Hybrid systems, combining lightning-fast offline-first edge response times with the power of the cloud, are becoming the norm. IDC projects that 78% of retailers will adopt these setups by 2026, saving an average of $3.6 million per store annually. In an inflation-driven market, Edge AI isn’t just a perk – it’s a critical strategy for thriving in the future of retail. By leveraging Edge AI-powered on-device databases, retailers gain the speed, efficiency, and reliability needed to stay competitive in an AI-driven retail landscape.