Customer service teams across the Asia-Pacific region are increasingly using artificial intelligence (AI) in customer support to address common problems. These teams face mounting pressure, with high inquiry volumes and multilingual customer bases. Factor in the constant rise of customer expectations, and it’s easy to see why the risk of agent burnout is becoming increasingly high.
However, by streamlining processes and easing the load on frontline staff, AI is helping businesses respond to customers faster and more consistently, while also increasing capacity.
Here, we look at some examples of how APAC brands have successfully applied AI to tackle persistent customer service challenges and deliver improvements.
Reducing wait times through smart automation
Long wait times are one of the most persistent problems facing customer service teams, especially during peak periods or crisis events. From rescheduled flights to broadband outages, routine queries can quickly overwhelm agents and have a negative effect on customer experience.
AirAsia responded to this challenge by automating key customer communications through AI. The airline worked with Infobip to implement a system with the capability to deliver real-time updates via SMS, email and chat whenever flights are delayed or rescheduled.
The impact was impressive: AirAsia achieved a 90 percent success rate in message delivery and saw its Net Promoter Score (NPS) climb to around 50. Over time, the airline also upgraded its communication tools, introducing richer media formats such as hyperlinked SMS messages directing customers to chatbots, as well as interactive HTML emails.
Similarly, in Singapore, telecom provider StarHub turned to AI to help manage mounting pressures on its customer support channels. The company worked with Haptik to launch a chatbot designed to handle more than 3,000 different customer queries, from those relating to broadband plans and contract eligibility to technical support and promotions. It also integrated over 50 built-in user journeys to help customers activate services, access roaming options and troubleshoot issues.
By taking care of routine questions, the chatbot allowed human staff to focus on more complex problems and, in turn, give customers the kind of thoughtful service that helps to build loyalty.
Moreover, the new system made a significant difference: support wait times were nearly cut in half, and the company saw its NPS jump from -40 to +10.
Streamlining workflows to improve consistency
As service teams scale, inconsistency often creeps in, especially when staff need to spend valuable time on manual, repetitive tasks.
To address this challenge, Globe Telecom, one of the Philippines' largest providers, adopted Salesforce's Einstein and Agentforce AI tools, creating an end-to-end case handling system capable of automating key support workflows.
The new platform had the ability to automatically identify customer intent, filter out duplicate tickets and non-actionable emails, and escalate complex cases where needed. It could also create fresh cases when customers replied to closed tickets with new issues, so no enquiry was lost.
The strategy paid off: Globe saw a 28 percent drop in incoming ticket volume, a 34 percent reduction in agent workload and more than 80 percent accuracy in automated case classification. The company achieved 60 percent automation of its operations using a combination of AI and robotic process automation (RPA), easing pressure on staff and improving service quality across the board.
Using AI in customer support in real-time
For China Airlines, improving the quality of agent interactions – particularly during high‑stress situations such as flight disruptions – was identified as a key priority. In March 2025, it was announced that the airline had signed a memorandum of understanding with Chunghwa Telecom Laboratories to introduce an AI‑powered voice response system aimed at elevating customer service standards.
One of the key reasons for the move was the system's capability to analyze calls in real‑time, categorizing customer queries as conversations unfold. This functionality was designed to improve issue resolution, inform agent response strategies and contribute insights to enhance staff training programs.
When AI is used in this way, it helps agents become more confident, less mentally fatigued, and better equipped to handle customer emotion in real time.
Improving multilingual support through co-pilots
In a region as linguistically diverse as APAC, multilingual customer support is a distinct advantage and, in many cases, a necessity – but it can be a major operational strain.
DBS Bank, which serves customers across Singapore, Hong Kong and Taiwan, needed a scalable solution to support voice and digital interactions in multiple languages.
In response, the bank developed the CSO Assistant, a generative AI‑powered virtual assistant built entirely in house. It was designed to support voice and digital customer service channels by transcribing calls in real time, retrieving relevant knowledge articles, and generating post‑call summaries – all aimed at reducing manual documentation and enhancing agent productivity.
Developed using a large language model trained on DBS’s internal knowledge base, the tool was designed to understand languages and colloquialisms used in different markets.
Piloted in Singapore from October 2023, the CSO Assistant achieved near 100 percent accuracy in transcription and issue resolution, helped reduce call handling times by up to 20 percent, and received positive feedback from agents – with around 90 percent saying it improved their workflows.
The success prompted DBS to roll out the CSO Assistant to other markets, starting with Taiwan and Hong Kong, with full deployment by the end of 2024.
AI – a key ally to support staff
These examples show how AI is helping APAC brands elevate customer service not by replacing agents, but by supporting them. Whether it's through proactive communication, workflow automation, real-time coaching or language support, AI is enabling support teams to deliver faster, more accurate and more consistent service.
In addition, these tools help to improve the employee experience. They eliminate repetitive tasks, reduce burnout and empower agents to focus on meaningful, high-value interactions. So the result is not just better outcomes for customers, but also more sustainable, rewarding roles for employees.
As AI continues to evolve, these examples offer a glimpse of what modern customer support should look like: tech-enabled, human-centred and fit for the complexity of today’s service demands.
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