Demystifying AI: A practical approach for South and Southeast Asian retail

Mar 4, 2025 • 7 min

Retailers across South and Southeast Asia are working through a moment of transformation. While modern retail channels currently account for only 10-15% of total sales in emerging markets like Vietnam and India, this landscape is changing rapidly. With a massive population and rising consumer spending power, the growth potential for organized retail throughout Asia is exceptional. This expectation of growth has sparked significant interest across the region in artificial intelligence (AI).

We frequently hear from business leaders in Asia who urgently want to invest in AI capabilities to support that growth but are struggling to separate discussions of the practical tools they need from the broader AI hype cycle in the industry. Cutting through the noise to identify AI applications that deliver measurable value for their specific pain points is a practical approach that makes perfect sense. After all — AI isn’t a magic bullet, but a collection of specialized tools, each designed to address a specific challenge. And for retailers in South and Southeast Asia facing unique logistical hurdles, the right approach to AI offers a value proposition that standard statistical approaches simply cannot match.

The three pillars of effective AI

At RELEX, we believe every company should embrace a diversified approach to AI rather than looking for a one-size-fits-all “AI tool.” We have simplified the broad topic of AI into three essential components that must work together to address the complex challenges retailers face:

Specialized AI tools designed to solve specific retail challenges.
Generative AI assistants that help users analyze and complete their work via chat.
An AI-enabling platform that serves as the foundation of your success by housing all data needed by your specialized and generative AI tools.

Specialized AI: Purpose-built tools to solve specific problems

Specialized AI combines machine learning algorithms, mathematical optimization, and heuristics to solve specific problems. In 2025, we’re surrounded by specialized AI to the point that we may not even realize all the ways we interact with it: Phones autocorrect our typing, websites suggest related products or articles we might enjoy, and high-end washing machines adjust cycle settings based on automatic load size and fabric type detection, even optimizing for energy consumption! These are all examples of specialized AI that we seamlessly interact with every day.

In the retailing context, though, specialized AI tackles the challenges specific to core processes like demand forecasting, inventory management, or replenishment planning. What makes specialized AI effective is its ability to process vast amounts of data—historical sales, promotions, seasonality, weather patterns, local events, and more—to identify trends that even the most powerful human brains simply cannot.

For South and Southeast Asian retailers dealing with unpredictable monsoon seasons, the popularity of local festivals, and complex geographic challenges, these specialized AI tools can help make sense of complex data sets that read like pure chaos to planning teams, producing remarkably accurate predictions about what inventory is needed where, when, and in what amounts.

Generative AI: An intelligent assistant to bridge knowledge gaps

While specialized AI works behind the scenes to process data and create predictions, generative AI serves as an intelligent assistant that helps users interact with advanced systems. AI-based chat assistants let planners ask questions and receive guidance in any language, so they can quickly and easily access the information to make impactful decisions without needing technical expertise.

This is particularly valuable for retailers concerned about disruption to the business should they adopt a more advanced planning system their users are unfamiliar with. It’s also extremely valuable in markets where retailers struggle to hire analysts with the specialized knowledge required to drive optimal results. In our experience, generative AI is the key to bridging knowledge gaps — it accelerates user adoption and productivity. Our intelligent AI assistant, Rebot, was the first of its kind in the retail space in early 2024, and early customer feedback has been enormously positive.

Illustration of AI agent Rebot providing answers to questions — *AI assistants like Rebot bridge knowledge gaps, accelerating user adoption and productivity.*

A unified platform: The shared foundation that enables AI at scale

The last component is a platform that can connect previously disconnected planning functions through seamless data integration. This unified approach ensures that the insights generated by specialized AI tools flow across the organization and reach all the teams they need to, breaking down silos and enabling coordinated decision-making across the business toward shared goals.

For Asian retailers with an eye toward growth, this platform approach is crucial for scaling operations as they expand across diverse geographic regions and add new store formats.

Deep dive into AI at RELEX

Practical applications for specialized AI

Let’s move beyond abstract concepts to look at some specific, practical examples of how retailers across Asia can start using AI today to address the challenges unique to their businesses.

Forecasting and replenishment: Mastering complexity with AI

Let’s start in one of the most challenging categories: fresh foods. A grocer operating in Indonesia likely faces steep inventory management challenges due to extreme weather variations, frequent flooding, and the popularity of local festivals, all of which impact shopping patterns. High humidity and temperature fluctuations can accelerate spoilage, making it even more challenging to manage fresh inventory effectively. These regional factors require a sophisticated approach to inventory forecasting.

An advanced demand forecasting solution using specialized AI can break down and analyze these complex factors, including weather patterns, festival schedules, and regional preferences, to produce remarkable improvements — up to 99% forecast accuracy. AI-based forecasting and replenishment is a proven game-changer for fresh food retailers, ensuring fresher products for customers and far less waste for the business.

While grocery sales are driven by frequent, necessity-driven purchases, apparel retail is more seasonal and trend-driven, with more emphasis on brand positioning and merchandising. A fashion retailer in Bangkok may struggle with rapidly changing inventory and Thailand’s distinct high and low tourist seasons. AI-driven replenishment systems can painlessly account for these seasonal shifts and automatically adjust order proposals accordingly. Should planners have questions about unusual demand patterns, a generative AI assistant like Rebot can provide immediate insights that could otherwise cost hours of manual analysis.

Poor inventory data? AI can help

From years of working with retailers across the world, we hear from many customers that their inventory accuracy is poor—and this is a persistent challenge for retailers in Asia as well. Whether operating in Kolkata or Kuala Lumpur, most retailers struggle to effectively understand and control their “phantom inventory“—the products that show as available in their systems but aren’t actually on shelves.

The specialized AI in a predictive inventory solution can help by analyzing transaction data, shelf capacities, and historical patterns to proactively identify these inconsistencies without requiring constant physical counts. After identifying discrepancies, the solution alerts store staff to exactly which products they need to check first. This level of visibility and efficiency saves enormous amounts of time and labor, a critical concern for retailers as staffing costs continue to rise globally.

For some retailers, poor inventory data is a prime inhibitor of AI investment. After all, we’ve all heard the saying: “If bad data goes in, then bad data comes out.” What excites us the most about the potential of AI is that we can now finally put that problem behind us. Because predictive inventory creates a “synthetic inventory” that’s actually more accurate than your real inventory, retailers no longer have to figure out how to get their inventory counts right, saving time while helping teams across the business make smarter, more impactful decisions.

From analytics to diagnostics: What’s really behind your problems?

Retailers often struggle to understand why stockouts occur across their extensive networks. Until now we’ve had to rely on fragmented analytics that push us to focus on traditional assumptions rather than stepping back to analyze what is actually driving issues in our supply chains.

Recent advancements in AI allow retailers to diagnose the root causes of supply chain challenges—whether forecast inaccuracy, supplier delivery issues, or unexpected demand spikes. Advanced diagnostics enable companies to directly address their underlying problems rather than continuously treating the symptoms quarter after quarter.

A fresh food retailer in Vietnam, for example, faces significant spoilage challenges due to the heat and humidity. AI-powered diagnostics can pinpoint where in the supply chain products are deteriorating, identifying higher spoilage rates in a particular warehouse due to temperature fluctuations or delays in certain transportation routes that lead to product expiration. It can even detect patterns human analysts might not think to assess, such as the correlation between specific packaging material and increased spoilage rates. These insights and recommendations enable leaders to take concrete, targeted actions to solve their underlying issues.

Getting started: Practical first steps on your AI journey

We recommend these practical steps for retailers in South and Southeast Asia looking to implement AI effectively:

Start with clear business objectives: Rather than pursuing “AI” broadly, identify specific challenges where better prediction and automation could drive value.
Focus on data readiness: AI can work with imperfect data, but make sure you have the basic information needed for your priority use cases.
Choose partners with regional understanding: Look for partners who understand your unique retail environment and have proven success in similar markets.
Plan for user adoption: Select tools that offer intuitive interfaces and built-in assistance to accelerate adoption across your organization.
Think platform, not point solutions: As your AI journey progresses, having a unified platform will enable you to expand capabilities without creating technological silos.

Illustration of the benefits of AI-enabled solutions for retailers — *The benefits of AI are broad, impacting both short-term goals through accelerated ROI and long-term goals through improved resilience and scalability.*

For South and Southeast Asian retailers, AI represents an opportunity to far surpass traditional approaches and build more efficient, responsive operations that can scale with shifting business needs caused by the region’s immense growth potential. The key isn’t to chase the latest AI trend but to identify the specific business problems that matter most to your organization and apply the right technology to solve them.

With a thoughtful, diversified approach to AI implementation—combining specialized AI for complex calculations with generative AI for user assistance within a single platform—retailers across Asia can drive tangible improvements in availability, efficiency, and profitability. As modern retail continues to expand, retailers who implement practical AI solutions now will be better positioned to capture a massive growth opportunity and stake their competitive advantage early.

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