AI in APAC Contact Centers: 5 Practical Wins You Can Deploy Now
Contact centers across APAC are under pressure: call spikes, multilingual expectations, and agent churn. Building a customer-first culture is still hard, and the margin for error is thin.
AI is helping teams respond with speed and consistency. The key is targeted use cases that cut friction for customers and agents at the same time.
1) Real-time agent support cuts handling time and improves accuracy
When agents dig through docs during live calls, everything slows down. Telstra tackled this by building two AI tools on Microsoft Azure OpenAI Service: One Sentence Summary and Ask Telstra.
One Sentence Summary condenses interaction history so agents get context fast. In trials, 90% of staff reported time savings and higher effectiveness, with a 20% drop in follow-up contacts. Ask Telstra lets agents search internal knowledge with a simple query, and 80%+ of pilot users said it improved customer interactions.
- How to put this to work: Start with one high-volume queue and a concise summary template.
- Connect to your approved knowledge base; log sources in every AI answer for trust and QA.
- Measure impact on AHT, FCR, and repeat contacts before expanding.
2) Speech analytics optimizes agent performance
You can't coach what you can't see. National Telecom in Thailand implemented Verint Open Platform with AI-powered speech analytics to analyze ~75,000 interactions automatically.
Insights that took a week dropped to 24 hours. By spotting that internet issues drove the longest talk times and fixing processes, the team cut average talk time by 18% and lifted CSAT by 3%.
- How to put this to work: Define the outcomes you want (AHT, CSAT, compliance) and build a keyword/theme taxonomy.
- Automate scorecards and route coaching moments to supervisors within 24 hours.
- Close the loop: turn insights into updated scripts, workflows, or help-center fixes.
3) Multilingual AI delivers seamless cross-border service
Language gaps cost time and customer trust. DBS Bank built an in-house generative AI co-pilot (CSO Assistant) tuned to local languages and speech patterns.
It transcribed calls in real time and pulled answers from the bank's knowledge base, supporting 500 agents handling 250,000+ monthly queries. Pilot results showed near-100% transcription and solutioning accuracy, and ~90% of officers reported a positive impact. DBS then expanded to Taiwan and Hong Kong.
- How to put this to work: Prioritize your top languages and intents; tune prompts and examples to local phrasing.
- Provide an instant "human handoff" option for low-confidence cases.
- Track deflection, resolution accuracy, and customer sentiment by language.
4) AI-assisted call summarization simplifies high-volume workflows
High turnover and repetitive work drain teams and knowledge. Singtel implemented AI summaries that highlight key issues by topic so agents don't piece together context mid-call.
The goal wasn't to replace agents, but to help them focus on resolution, improve consistency, and make the job more sustainable. Cleaner notes also protect continuity when staff change.
- How to put this to work: Push auto-summaries into your CRM with clear sections: reason, actions, outcome, next steps.
- Use a standard summary rubric for QA and coaching; sample 5-10% for accuracy checks.
- Feed summaries into onboarding so new hires see "what good looks like."
5) Voice AI creates natural, accessible phone support
Phone calls still matter. Rakuten Mobile launched an AI voice assistant using OpenAI's Realtime API alongside its in-house Rakuten AI 7B model to handle natural speech in Japanese and English.
About 30% of inquiries still arrive by phone, so the assistant took on signup, onboarding, and routine support. Running on Rakuten's infrastructure minimized latency and kept data in-house, while integrating with existing contact-center flows.
- How to put this to work: Start with a short menu of high-volume intents (status checks, plan changes, simple troubleshooting).
- Set latency targets under 300 ms and provide instant escape to a human.
- Log every AI step with reason codes to improve routing and training.
For technical context on real-time voice systems, see the Realtime API overview from OpenAI: Read the guide.
What this means for support leaders
The pattern is clear: identify one painful workflow, ship a targeted AI assist, and measure results. Success hinges on data access, agent buy-in, and fast feedback loops-not big-bang projects.
- Pick one use case (summaries, retrieval, speech analytics, multilingual, or voice).
- Define 2-3 metrics (AHT, CSAT, FCR, containment, handle time to insight).
- Pilot with a small team; collect agent feedback weekly; iterate prompts and guardrails.
- Document privacy, retention, and handoff policies. A practical resource: the NIST AI Risk Management Framework View framework.
The contact center industry in APAC is on track for strong growth, and AI-powered automation is already the #1 investment priority. The real question is how quickly you can deploy well-scoped solutions while keeping human judgment and empathy at the core.
Level up your team's AI skills
If you're planning an AI pilot or rollout, upskilling agents and team leads pays off fast. Explore practical courses for support roles here: Courses by job or browse the latest AI programs: Latest AI courses.
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