How Asia’s retailers can prepare for AI-first commerce

Meeting customer demands now requires more than just digital storefronts or marketing. 

As the home to approximately 60% of the world’s consumers, the Asia Pacific (APAC) sits at the centre of global retail growth. For retail leaders, success is defined by the ability to navigate high-intensity shopping seasons such as Lunar New Year and Single’s Day, which compress demand, fulfilment, and decision-making into a narrow window.

Whilst retailers once had years to adapt to the internet and months for the cloud, the window to operationalise artificial intelligence (AI) is shrinking to weeks. Coupled with the region’s mobile-first culture and fast-moving commerce cycles, meeting customer demands now requires more than just digital storefronts or marketing. Retailers must pivot towards AI-first systems capable of powering real-time operations at scale.

This reality is already shaping strategic priorities. In Singapore alone, 85% of retailers say they are deepening AI investments. AI-first commerce will increasingly determine who capitalises on peak demand and who discovers the limits of their systems when it matters most. The industry has moved past the question of “if” AI will be adopted; the challenge now is whether existing architectures have the foundational maturity to support an "agentic enterprise", where AI moves from simply assisting work to autonomously operating business and making decisions at scale.

Peak demand as the proving ground for AI-first commerce
Peak periods function as real-world stress tests. Commerce activities that usually take weeks are condensed into days or even hours: Traffic surges across channels, inventory fluctuates in a single afternoon, and customer service volumes spike.

This is where agentic commerce delivers its greatest value. Unlike traditional chatbots that merely retrieve information, AI agents can execute transactions, manage dynamic pricing across vast catalogues, and optimise supply decisions in real time. Together, these capabilities allow agents to personalise customer experiences at scale, for example, by triggering contextual promotions based on live inventory or pushing personalised mobile offers when a customer enters a store.

As these capabilities mature, customer journeys will shift from the Zero Moment of Truth (ZMOT) model, where customers search and compare products online before buying, to an Agent Moment of Truth (AMOT) model, where an autonomous agent synthesises data and recommends a purchase decision. When a customer’s personal AI agent does the shopping, by comparing prices, checking reviews, and selecting the best option, websites and marketing become less relevant as discovery channels.

The reason is simple. Agents do not browse homepages the way people do; they rely on structured data queries. As search-led discovery loses influence, retailers that cannot serve structured, real-time, trustworthy data to external agents simply will not be considered. This is the real disruption: Advertising and promotions shift from consumer campaigns to agent-to-agent interactions.

To win, brands must ensure their data is unified, current, and trustworthy for agents to act with confidence.

Why some retailers scale under pressure, and others stall
Most retailers share AI ambitions, but peak periods widen the gap between modernised retailers and those relying on legacy systems. Retailers operating on modern, flexible data infrastructure are equipped to innovate iteratively, scale capacity without downtime and respond to market signals in real time. This divide is already translating into measurable business impact.

A research found that APAC’s AI leaders are generating three times more digital revenue than their peers by investing in modern data foundations.

Today, AI agents are essential to drive speed and competitive advantage. As retail decisions become automated, agents need a unified view of all business and customer data to act accurately and safely. In many legacy environments, context is fragmented across multiple systems. Real-time inventory signals are not connected to promotions, pricing rules sit in a separate system from product content, and customer preferences cannot trigger dynamic offers at the moment of purchase. When agents operate on incomplete or inconsistent data, it increases operational risk, limits the scale of automation and erodes customer trust.

Legacy environments are further constrained by rigid schemas, complex dependencies and fragile integrations, making it difficult for them to keep pace with the velocity of an agent-led market.

The contrast between modern and legacy retailers is visible amongst large-scale retailers in the region. This contrast is best illustrated by a retailer in Indonesia and the Philippines, serving 18.1 million customers and processes about 4.6 million transactions daily. Faced with exponential online growth, they moved to a more flexible, auto-scaling platform and reported a 1,000% efficiency improvement, reducing member scan times during peak hours from about 10 seconds to under one second – a critical gain when compounded across millions of interactions at the moment of purchase.

This kind of sub-second responsiveness is not just an infrastructure win, but a prerequisite for enabling AI-driven personalisation on top of every transaction, and an AI-first mindset across the organisation.

What an AI-first mindset looks like in practice
An AI-first mindset focuses on building systems that operate reliably under real-world pressure, especially during demand spikes when speed and accuracy matter most.

In practice, AI-first means designing systems where AI and agents can access real-time, high-quality data by default rather than retrofitting systems after the fact. This could mean making product catalogue data, pricing rules, inventory availability, and promotional logic accessible in one place, rather than locking them each behind separate systems.

AI-first also requires a culture of continuous modernisation and innovation of IT systems, as opposed to one-off upgrades. This reduces the reliance on manual upgrades and brittle point-to-point integration. It also ensures teams can focus on higher-value work to improve customer experience and strengthen business resilience before the next demand spike.

This mindset shift is already underway, as APAC businesses recognise that failing to modernise only compounds tech debt over time. Research found that 89% of organisations now see tech debt as a major obstacle to modernisation and their AI ambitions.

Finally, AI-first retail leaders prioritise platform-level capabilities over short-term pilots. Many retailers can prove value in a lab, but stall when production demands real-time data that legacy architectures cannot deliver. Retailers that align their business strategy with AI-first principles are the ones moving beyond experimentation and towards meaningful, scalable innovation.

Preparing for the next wave of retail experiences
Agentic commerce will define the next decade of retail transformation. The winners will recognise that AI’s impact is unlocked within the data and systems that power every retail decision. Peak seasons will accelerate this shift by forcing clarity. Demand spikes reveal which systems scale, which teams can respond quickly, and which data can truly be trusted.

Retailers that modernise now will deliver the next generation of personalised, resilient, and intelligent shopping experiences. With its massive scale and fast-moving commerce cycles.

The choice is clear: Retailers can either continue to patch legacy stacks or build the foundation that enables autonomous operations. Those who act now will turn the region’s most intense shopping moments into a sustained competitive advantage and help set the pace for AI-first commerce globally.