AI Agent Expansion in Southeast Asia Hinges on Localization

AI adoption among companies in Southeast Asia has already surpassed the global average. Yet by country, wide gaps remain in infrastructure, talent, and regulatory maturity. The Asian Development Bank’s Southeast Asia Department laid out the current situation in its 2026 outlook report. The report positioned agentic AI as a new direction for the transformation of small and medium-sized enterprises. In coverage by Titanium Media’s Outbound Reference Singapore bureau, this gap became the starting point for discussion.

The United States remains the top priority destination for AI going global. As globalization enters a new phase, diversifying risk carries growing weight. With high adoption rates across the region, ASEAN has emerged as a new expansion destination. Over the past six years, industrial digitalization in the region has advanced rapidly. Mobile networks, e-commerce, digital content, and the digitalization of business operations continue to grow. Many companies are seeking to leapfrog legacy informatization stages with AI. The aim is to directly improve efficiency in customer engagement, marketing, operations, and decision-making. Chinese companies with experience in mobile apps, content production, and e-commerce services have transferable capabilities. Many untapped scenarios still remain.

Titanium Media’s “Talk To The World,” together with AI GRAVITY, the overseas brand of WAIC, visited Singapore. Jim Lim, co-founder and CSO of 59stVentures and Aurora’s Singapore partner, attended. Li Li, Head of Global Business Development at NetShort, Una Wang, founder of LingoAI, and Yin Jie, Vice President of Runjian’s AI business group, also joined. They discussed the issues from three perspectives: content production, enterprise agents, and enterprise AI infrastructure. The common understanding was that the closer you get to decision-making, user touchpoints, culture, and emotion, the narrower the scope for applying general-purpose capabilities. Foundation models, technology platforms, and general-purpose workflows can be delivered in standardized form. Once you face users, embed into corporate systems, and begin to take part in decisions, local data, partners, and cultural understanding determine whether adoption sticks.

Expanding AI Adoption in Southeast Asia

The diversity and fragmentation of regional markets are the starting premise. Language, religion, legal systems, and income levels differ from country to country. It is difficult to cover the entire region with a single product form. The division of labor between standardization and localization is at the center of strategy. Remote delivery of model capabilities and on-the-ground adoption are separate challenges. The latter depends on operational fit locally and organizational trust. This discussion illustrated that dividing line with concrete examples.

What Can Be Standardized and Where

Localization Begins

Foundation models provide the basis for multilingual support, reasoning, and generation. Technology platforms can standardize authentication, billing, and record-keeping. General-purpose workflows are effective for automating routine tasks. Dialogue with users, internal approvals, and final decisions still require tailored handling. Forms of address, politeness expressions, and communication habits differ by country. Data residency requirements and dominant contact channels also differ by country. What is needed is an architecture that preserves the standardized core while supplementing the final layer locally.

Industrialization of AI Short Dramas and the

Empathy Challenge

The content industry offers an intuitive illustration of the benefits of standardization. NetShort entered overseas markets in September 2024. That was one to two years later than major competitors. By the first half of this year, total revenue had reached $120 million. Behind this were efficiency gains and cost reductions across the entire production chain. Scriptwriting, storyboard generation, and character and scene production were all covered. AI has moved into everything from material distribution to analysis of user responses. Conventional live-action short dramas for North America required months of filming. Total production costs were typically $200,000 to $300,000. With AI dramas, costs have been held to $20,000 to $30,000 and production time shortened to a matter of days.

In addition to cutting internal costs, the tool-ization of assets has advanced. Accumulated content assets and production processes have been bundled as agentic tools. Long scripts can be broken down to generate storyboards and cutting proposals. Before release, material testing measures reactions. It determines after how many seconds users tap, and after how many episodes they drop off. Themes likely to lead to payment are also screened in advance. As a case study on generative efficiency, see also SenseTime’s 8B Image Generation Model SenseNova U1.5 Lite Officially Released. The more reproducible the tools become, the clearer where scarcity lies. Identifying what makes people tap and creating stories that move hearts are different capabilities.

Technical replacement is already not difficult. One example is replacing a Taoist priest from a Chinese xianxia tale with Zeus. Replacing an ancient sect with a modern business empire is similar. The hard part is judging whether the emotional core can travel across cultures. Judging discomfort over human relationships, social beliefs, and modes of expression remains. AI can convert language, but cannot convert humor, views of family, or lifestyle habits. In Thailand, Indonesia, and Latin America, the ideal self-image people aspire to differs. In Latin America, family carries extremely strong weight, and the idea of three generations living together is deeply rooted. Domestic content has almost no corresponding narrative stock to draw on.

NetShort bridges the differences with editorial review covering 17 languages by about 200 people. Li stressed the need for people who actually use local languages and understand daily life and context. It is also building an ecosystem of overseas creators at the same time. It opens its agentic production tools and content assets to local creators. The aim is to have them add their own experience on top of general-purpose tools.

Lack of Language Data and the Need for Local

Collection

Una Wang, founder and CEO of LingoAI, explained the same problem from the data side. Training data for mainstream foundation models is concentrated in English, Chinese, Spanish, and other major languages. Even though Indonesian has a huge user population, high-quality documents, audio, and cultural information are limited. LingoAI specializes in AI localization and data solutions. It provides multilingual, multimodal data set services for global markets.

“Behind language lie history, religion, humor, and emotion, and these cannot be fully captured through online scraping or literal translation alone.”

A car voice-wake function was cited as a concrete example. Even a seemingly mature smart cockpit cannot be voice-activated by local drivers in Thailand or Indonesia. The background is a lack of local language data. Getting models to understand local culture still requires participation by local communities. Participation in data collection, annotation, evaluation, and consent is indispensable. AI models and agents can industrialize and scale up production. What determines whether empathy exists is creators’ local understanding, and human involvement in cultural judgment remains.

Five Technology Layers That Determine

Enterprise Adoption

If emotion is what is hard to standardize in the content industry, touchpoints, execution, and trust are what cannot be standardized for enterprises. Companies in Southeast Asia can directly use open models such as OpenAI, Claude, and DeepSeek. What they get is only general-purpose intelligence, not a product that can perform business tasks on its own. One layer is missing between models and agents that work inside companies. Aurora and Jim divided this layer into five: the enterprise knowledge layer, the systems integration layer, the workflow execution engine, multi-agent collaboration, and the enterprise governance layer. The enterprise knowledge layer consists of knowledge bases and knowledge graphs. The systems integration layer handles integration with CRM and ERP. The workflow execution engine handles closed loops of multi-step tasks and approvals. Multi-agent collaboration splits complex tasks among agents from different departments. The enterprise governance layer handles permissions, audits, and human intervention mechanisms.

In terms of product structure, EngageLab focuses on customer touchpoints and multi-channel reach. GPTBots.ai focuses on building enterprise-grade AI agents and process automation. The higher the layer, the less the challenge is about technical implementation alone. The more it involves organizational processes, permission boundaries, and trust-building. As a move dealing with visibility and control of corporate spending, Rippling Announces AI Spending Tracker ‘AI Spend Console’ is a useful reference. The view was shared that operational design matters alongside tooling.

Gradual Expansion of Authority Toward

Building Trust

Aurora’s approach treats an AI agent as a new employee. First, assign simple, high-frequency, low-risk tasks. Gradually expand permissions as trust accumulates. Do not aim for full autonomy in one leap. In addition to technical safety and trust, localization of touchpoints is a challenge. The most labor-intensive part is the language and culture layer. An example of forms of address was given. In Singapore, addressing a customer directly by name in customer service is sufficiently polite. In Indonesia, depending on age, the other party must be addressed as “Pak” or “Ibu” to show respect. Translation alone cannot complete local handling. Understanding local forms of address, politeness expressions, and communication habits is required.

Regulatory compliance and infrastructure differences also affect the final stretch. Each country has different data residency requirements. Dominant digital infrastructures also differ. Users in China use WeChat daily. Users in Singapore use WhatsApp, and users in Thailand use LINE. To complete the last mile of agent adoption, collaboration with local channel partners is indispensable. In August this year, Aurora signed a three-year memorandum of cooperation with Malaysia’s Exabytes. In Thailand, Indonesia, and Singapore as well, it grasps real demand through local partners. The aim is to complete localized touchpoints and execution. The approach preserves a standard foundation of safe models and general-purpose workflows. Local implementation options, channel connections, and local partners supplement the final layer. Model capabilities can be delivered remotely, but fit and trust determine adoption rates. One is whether it can work in ways familiar to local companies and users. The other is organizational trust.

Accumulating Business Assets on an Enterprise

Brain Infrastructure

Runjian’s Apollo 11 pushes the issue further inside the enterprise. Its position differs from supporting the introduction of external agent services. It accumulates historical data and business logic from ERP, CRM, and other systems as a company-specific business entity. It supports running AI capabilities on a foundation called the enterprise brain infrastructure. Yin Jie, Vice President of the AI business group, cited companies with complex operations and large accumulations of business assets as examples. He raised issues with the approach of layering additional purchases of AI features from external SaaS providers. The latter part of the detailed remarks is cut off in the public record. In context, the contrast between adding external features and accumulating internal assets was the point at issue. The theme was that models are replaceable, but capabilities should be accumulated locally. From the perspective of automating the cleaning and management of tools, improvements in operational tooling such as FluentCleaner, the New Open-Source Alternative to CCleaner are not unrelated. The view was shared that information and processes remaining inside the company are the source of competitiveness.

Editorial Opinion

On short-term impact: over the next three to six months, partnerships with local collaborators are expected to accelerate. Connections to contact channels and adaptation of forms of address and approval processes will become conditions for adoption deals. As differences between general-purpose models narrow, the sales focus will shift to adoption support. From a long-term perspective: accumulation of data assets will become the dividing line. Companies with a cycle of local data including annotation, evaluation, and consent will gain an advantage. Simply adding external features will make it hard to overcome the walls of cultural fit and trust. The enterprise brain infrastructure concept can be seen as an expression of that. Three questions from the editorial team: who should be responsible for judging empathy. Who should decide the pace of expanding authority. Whether compensation for data use will be returned to local communities. The issue is whether the role of local talent can be reframed as an asset rather than a cost.

References

• “智能体出海东南亚，还缺懂本土落地的人 ｜SEA Frontline”, by 出海参考新加坡站 — 钛媒体, 2026-09-07T22:45:11.000Z (ARR)
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