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Enterprise AI in Southeast Asia: Six Use Cases Software Developers Can’t Ignore

Enterprise AI in Southeast Asia: Six Use Cases Software Developers Can’t Ignore

July 02, 2025

Artificial Intelligence (AI) is undergoing a rapid evolution across Southeast Asia (SEA), with private sector investment expected to reach US$3.2 billion by 2028. ABI Research has observed AI spending by businesses accelerating across industries like telecommunications, logistics, manufacturing, banking, and insurance.

Adoption of enterprise AI solutions on the continent is spurred by local government support and a boost in Foreign Direct Investment (FDI) from major players like Microsoft, Google, ByteDance, Huawei, and OpenAI. Natural Language Processing (NLP) is emerging as the top AI framework, driven by strong demand for chatbots and customer insight tools. With that said, computer vision and graph-based modeling are no slouches, growing 11% annually through 2028.

For software developers seeking to align their product strategies with this growth, it's critical to understand how enterprises in the region are applying AI today. Here are five enterprise AI use cases gaining traction in Southeast Asia that software developers should monitor closely.

 

1. Telecommunications

Thailand’s True Corporation is setting a new standard in telecommunications with the deployment of its Business and Network Intelligence Center (BNIC). The center leverages AI and Machine Learning (ML) to automate key network functions, improve infrastructure monitoring, and proactively detect service anomalies in real time.

 

Case Study Results:

  • Impact its 50 million customers nationwide, including corporate customers and nearly 4 million TrueOnline fiber Internet network uses
  • Run 24 hours a day for 365 days a year with AI acting as a copilot

 

Key Takeaway: Software developers focused on telco AI applications should take note of how anomaly detection algorithms and predictive analytics are transforming the telecom sector. Opportunities exist in offering models for bandwidth optimization, outage prevention, and Over-the-Top (OTT) performance tuning.

 

2. Logistics

AI is also reshaping the logistics industry, with Singapore Post (SingPost) being a notable example. As part of its digital transformation journey, SingPost migrated all of its Information Technology (IT) workloads to Google Cloud to access AI-optimized infrastructure. Using Google’s Vertex AI platform and Document AI, the company transformed unstructured trade data into a searchable, structured format. SingPost also deployed an NLP-enabled chatbot to assist with operations.

This AI-based solution enhances supply chain orchestration and accelerates deliveries. Additional use cases, such as legal document automation and personalized customer support, are also under exploration by SingPost.

 

Case Study Results:

  • 30% increase in cost savings in IT operations
  • Elimination of inconsistency in manual administrative paperwork

 

Key Takeaway: To cater to supply chain decision makers, AI software developers should prioritize building modular tools that combine document processing, chatbot orchestration, and multimodal input support.

 


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3. Insurance

Indonesia-based insurance business Oona Insurance has redefined digital insurance services by integrating Yellow.ai’s generative and conversational AI into its chatbot, Yoona. This NLP-powered solution streamlines a wide range of customer interactions—from purchasing policies to self-service support. It enhances both service efficiency and customer satisfaction.

 

Case Study Results:

  • Servicing of more than 3,000 users of Yoona chatbot in 2 months
  • 60% reduced operational costs of Yellow.ai’s Dynamic Automation Platform (DAP)

 

Key Takeaway: The key lesson for software developers is that insurance providers value scalable, multilingual, and self-service AI tools that can lower customer support costs, while driving product uptake. Designing customizable, secure AI chat solutions tailored to policyholder needs will resonate in this expanding vertical.

 

4. Banking

Vietnam International Bank (VIB) has developed an advanced Generative Artificial Intelligence (Gen AI) assistant, ViePro, built on Amazon Bedrock and Claude 3 Haiku. ViePro uses customized NLP models trained on proprietary banking data to offer real-time responses in Vietnamese. It supports customer queries related to mortgages, credit cards, and vehicle loans. VIB’s virtual assistant is evolving into a personalized financial advisor capable of managing customer spending and calculating loan capacity.

 

Case Study Results:

  • 40% boost in productivity through service efficiency
  • 80% increase in optimization of IT and human resources requirements
  • 20% growth in digital customer base in the next year

 

Key Takeaway: Developers looking to target banking/financial institutions should focus on secure, privacy-centric NLP deployments that support local languages, integrate with core banking systems, and deliver value-added financial insights.

 

5. Manufacturing

Singapore’s Agilent Technologies showcases how manufacturers are applying AI for precision engineering and testing. Its dedicated digital solutions team has deployed 250 Industrial Internet of Things (IIoT) stations that use AI algorithms to learn from previous testing data, conduct root cause analysis (the top Gen AI use case in manufacturing, according to ABI Research survey results), and model time-series trends.

 

Case Study Results:

  • 23% increase in work cycles with shortened product testing time
  • 51% reduction in production downtime
  • 53% decrease in recycled waste
  • 33% increase in productivity

 

Key Takeaway: Software developers must recognize the growing need for embedded AI systems that adapt to dynamic production environments. There is strong demand for AI tools that combine predictive modeling, real-time sensor integration, and NLP-based diagnostics.

 

6. Corporate Training

In the Business Processing Operations (BPO) sector, the Philippines’ S.P. Madrid is using AI to deliver agile performance feedback via Automatic Speech Recognition (ASR). Powered by Alibaba Cloud’s infrastructure, its NLP models analyze agent dialogues to measure micro-skills and generate personalized coaching recommendations during training.

 

Case Study Results:

  • 80% reduction in training time

 

Key Takeaway: For software developers, this example highlights an expanding market for AI applications in enterprise learning and development. Features like speech analytics, AI-powered feedback loops, and performance optimization tools are becoming must-haves.

 

Conclusion

From IIoT in manufacturing to conversational AI in insurance, enterprise use cases across Southeast Asia show that AI deployments are diverse, dynamic, and increasingly complex. AI software developers targeting this region must tailor their offerings to sector-specific needs, with a focus on NLP, anomaly detection, local languages, predictive modeling, and intelligent automation. Only through these technological lenses can vendors build advanced AI software tools with the target customer in mind.

For a more detailed study of how software developers are driving AI innovation in Southeast Asia (SEA) and what the trends to be mindful of are, take a look at the following ABI Research reports:

Tags: Southeast Asia Digital Transformation, AI & Machine Learning

Benjamin Chan

Written by Ben Chan

Research Analyst
Research Analyst Benjamin Chan is a member of the Asia-Pacific Advisory team focused on issues related to Artificial Intelligence (AI) and Machine Learning (ML) implementation and digital transformation. Benjamin also focuses on key technological developments within the Southeast Asian region.

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