Asia is leading the Agentic revolution by helping SMBs. What organizations around the world can learn.

SINGAPORE, Sept. 15, 2026 /PRNewswire/ -- Asia's financial institutions are rethinking their approach to AI. The following is an article by Maxim Afanasyev at Google, and Zack Yang, Co-Founder at FOMO Pay, exploring that shift, and how FOMO Pay and Google are working together to bring autonomous, agent-initiated payments to Asia's SMBs.

Asia has emerged as a digital and AI adoption leader, outstripping developed markets that are weighed down by technical debt and legacy bureaucracy. This leadership is grounded on a foundational shift in how the region approaches change management and the meaning of AI innovations.

Asian financial institutions, for example, initially rushed into AI just like their peers in developed markets — by treating AI as a standard IT project with a pipeline of use cases, which were often prioritized using profit margin versus ease of deployment assessment. But then Asian organizations quickly realized that the right approach to AI success looks different. 

Today, most Asian financial institutions start their AI journeys by studying how their customers, partners and competitors adopt AI, and how such adoption changes the meaning of the industry ("new economy").

Say you're running a small business, like a cozy neighborhood restaurant, a sporting goods retailer, or an automotive mechanic shop. The way your market operates is changing, and the reason is agentic AI. So financial institutions in Asia are shifting their AI strategies towards helping small and mid-size businesses (SMBs) to adapt to these changing markets.

This 'aha' moment for Asian financial Institutions happened a couple years ago when they realized that treating AI transformation as a set of isolated IT use cases means joining the 95% of organizations getting no positive returns from AI (MIT, 2025, BCG, 2025). Indeed the use case pipelines might be convenient for developers but the approach ignores changes in business context, incentives, value proposition and even dependencies between use cases. Productivity use cases frequently top under margin vs ease of deployment criteria - scare employees about their jobs or outsource basic services to unpaid customers (Frey, 2026, Moradi et al, 2025). Organizations focused on AI use case pipelines struggled because motivations of employees and customers were ignored. Or think about a bank deploying AI Agents to identify SME prospects and send messages to them about its legacy financial products. The competing banks are also using AI Agents for sending similar messages, and overloaded with content, prospects not only get upset with the banks but lose motivation to act (Iyengar, 2020).

As Asian organizations navigated through their journeys, leadership realized that AI Agents assessed for productivity gains without the human context risk bringing challenges instead of value. On the other hand, they observed that as AI adoption among people spreads across Asia, it is transforming not just our economies but how our society thinks and acts. In this new world, financial firms focused on solving pre-Agentic ("old economy") use cases are at risk of their offerings becoming irrelevant to the wider market, similar to Kodak's and Blockbuster's a couple decades ago. The need to pivot AI strategy from focusing on internal optimizations to new value propositions for customers became obvious.  

Reasons which helped Asian financial institutions to embrace this new way of thinking include supportive and progressive regulatory environments (think about Project Mindforge in Singapore), a young and mobile-first population quickly adopting AI Agents for daily routines (the average age in Asia is about 30 years, compared to 40 to 45 in developed markets), regional diversity promoting new ideas, inherent agility due to rapid economic change over the past two decades, and an extensive fintech community. With AI adoption reaching 60%+ of population in certain Asian markets (Stanford University, 2026), local Financial Institutions have learned first that AI transformation thrives when organizations move beyond viewing it as a technical IT project and instead foster a decentralized culture capable of rapid, business-driven adaptation (Afanasyev, 2026). Such culture naturally includes guardrails grounded on AI governance principles and human-in-the-loop. The new way of thinking has accelerated AI successes in Asia, and the highlights include "AI for good cause" case studies such as Thai SCB Abacus, which is utilising data to expand credit access to more people, or Indonesian Amar Bank, which opens access to digital banking for underserved customers. One of the lessons learned is that AI pays back best when used for a good cause such as helping SMBs to grow their businesses. And this pivot in the AI agenda of leading financial organizations is good news for ~200mn SMBs powering Asian economies. 

Let's think through an example: Consider the payments process at a small restaurant in the Holland Village neighborhood of Singapore. When an AI agent decides to reorder ingredients for a restaurant, creating just a purchase order is not enough. The agent needs to pay. Automatically, without a need for a human in the loop to decide whatever rail works best: card, bank transfer, or stablecoin. Most payment systems today were not built for this. They were built for humans who analyze, click, confirm, and authenticate. As AI agents become active participants in commerce, the infrastructure underneath needs to catch up in order for small businesses to remain competitive.

It's for this kind of use case that FOMO Pay and Google Cloud are building solutions. In March 2026, FOMO Pay launched the FOMO AI Soundbox, Singapore's first single device to accept cards, PayNow, e-wallets, and stablecoins at a single point of sale (POS). But the device itself is only part of the story. Beneath it sits a merchant portal that captures the patterns of every transaction across the platform, and an AI layer in development that will surface real business insights for merchants, without requiring them to hire analysts or build complex systems. For SMEs that have been running on manual processes and gut feel, that is a meaningful shift. The shift provides SMBs with capabilities previously available to large corporations only. FOMO Pay believes that payment rails should not just be fast and connected, but intelligent enough to support a world where AI agents, businesses and consumers move as one.

To take the solution a step further, FOMO Pay has adopted Google's Agent Payment Protocol (AP2), an emerging open standard for autonomous, agent-initiated payments. In an early implementation demo, the restaurant sets a simple rule: when cabbage stock drops below 10 units, the system automatically identifies the right supplier, reorders and pays. The agent monitors inventory, places the order, and completes the payment with minimal human intervention. When merchants and suppliers are both participants in FOMO Pay, settlement occurs as a direct internal transfer. This is an early glimpse of how entire procurement and payment workflows can run on their own. The next generation of payment infrastructure should not just move money. It should make decisions: programmable, intelligent, and ready for a world where agents transact on behalf of businesses.

Agentic payments, however, require clear guardrails. AP2 addresses this through cryptographically signed mandates, user-approved authorizations that bind an agent to predefined merchants, amounts, and conditions. Before any mandate is issued, explicit human consent is required. Each transaction then produces a signed audit trail to support verification and dispute resolution. In this model, transactions are anchored to deterministic proof of intent, not the probabilistic outputs of an AI system.

This work sets an example of a "customer needs first" approach to AI adoption and contrasts with IT-led AI use cases that still exist at some legacy financial institutions. FOMO Pay's approach is grounded on the Business, Transformation, and Technology framework (Afanasyev, Milind, 2026). For Business — analyzing changed context and redefining the company's model, value to customers, and competitive edge in an Agentic AI world. For Transformation — building the right processes and ways of working within and outside the organization. Finally, for Technology identifying tools and technology partners to make it all work.   

Asian organizations such as FOMO Pay have moved away from AI use case pipelines for "old economy" towards analyzing how AI adoption by customers, partners and adversaries is changing the business model. This approach, which naturally focuses on customer needs, good causes and needs of "new economy", is a secret source for Asia leadership in AI - a sharp contrast with legacy organizations struggling with AI in developed markets.

SOURCE FOMO Pay