The retail sector across Asia-Pacific (APAC) is rapidly moving beyond basic analytics and pilot programs, with artificial intelligence now deeply embedded in daily workflows and core operations. This accelerated adoption is fueled by the unique pressures of dense urban environments, high staff turnover rates, and the intensely competitive landscape of quick-commerce. A recent survey from GlobalData indicated a significant consumer appetite for AI-driven recommendations, with 45% of consumers in Asia and Australasia expressing a strong likelihood of purchasing a product based on AI suggestions.
Jaya Dandey, a Consumer Analyst at GlobalData, highlights that machine-learning systems have long been influencing consumer behavior, from dictating purchase timing and product visibility to managing discount availability. “Now,” Dandey notes, “agentic systems are capable of completing shopping-related tasks end-to-end.”
**Computer Vision and Store Automation Drive Efficiency**
Companies exploring computer vision and machine learning can find compelling early examples within the APAC region. In Japan, Lawson introduced “Lawson Go” stores in 2022, leveraging AI to eliminate traditional checkout lines and cashiers, thereby enhancing the customer experience. This initiative was further bolstered in 2025 through a collaboration with CloudPick, integrating advanced AI, machine learning, and computer vision capabilities.
South Korea has also seen innovative deployments, with Fainders.AI launching a compact, cashier-less MicroStore within a gym in 2024. This move democratizes autonomous retail, making it accessible to a broader range of business types.
Beyond customer-facing automation, AI is proving invaluable in forecasting and automating retail replenishment – a critical function in the APAC market, characterized by smaller store footprints and a high frequency of restocking needs. Japanese food retailer Coop Sapporo utilizes “Sora-cam,” a camera-based AI system developed by Soracom. This system monitors inventory levels, helping the chain to prevent overstocking and minimize unsold merchandise. An analytics team reviews the images generated by Sora-cam to optimize shelf display ratios. Crucially, the system also identifies food items nearing their expiry date, alerting staff to apply discounts and reduce waste.
By meticulously tracking waste and optimizing markdown timing, AI significantly improves promotion efficiency. In Southeast Asian markets, where price sensitivity is a major factor, even marginal gains in promotion effectiveness can lead to substantial improvements in profit margins.
Furthermore, AI-driven labor optimization is addressing structural shortages in countries like Japan and South Korea. These systems assist with scheduling, task prioritization, and workload balancing, delivering efficiency benefits that are also highly relevant in the rapidly growing Southeast Asian markets.
**Agentic AI Enhances Consumer Interaction**
Dandey describes agentic AI in food retail as an “AI operator” that can comprehend a goal, devise a plan, adhere to constraints such as budget or allergen restrictions, execute actions across various systems, solicit clarification, and adapt to user preferences over time.
This advanced AI capability allows consumers to articulate their overall needs rather than searching for individual items. For instance, a customer could request an agent to “Plan five dinners for a family of four, mostly Asian recipes, no shellfish, under 45 minutes.” The agent would then generate suitable recipes, construct a shopping cart, adjust quantities, and automatically add any missing pantry staples.
This functionality resonates strongly with regional consumer habits, as many APAC households frequently cook at home and prioritize fresh ingredients. AI agents that recognize local culinary traditions – such as Korean banchan, Japanese bento components, or Indian spice bases – offer a more tailored experience than generic Western meal plans.
“In many APAC markets, shopping is already deeply integrated with digital wallets, messaging apps, ride-hailing, and delivery ecosystems, making it easier for agentic AI to plug into daily routines,” Dandey explains. However, she cautions that “ensuring private data sharing consent, minimizing hallucinations in terms of allergens and ingredients, and implementing proper localization of the system with language nuance” remain key challenges to be addressed.
Original article, Author: Samuel Thompson. If you wish to reprint this article, please indicate the source:https://aicnbc.com/19080.html
