The conversation around AI voice has changed dramatically over the past year.

Not long ago, businesses wanted to know whether AI could hold a natural conversation. Today, that question has largely been answered. Modern voice agents can qualify leads, schedule appointments, resolve customer enquiries, and handle a growing range of routine interactions with remarkable fluency.

At Agora, we’ve noticed a corresponding shift in customer conversations. Businesses are no longer asking whether voice AI works. Instead, they want to know how well it performs when deployed across different markets, languages, and customer segments.

Industry data points in the same direction. Gartner found that 85 per cent of customer service leaders plan to explore or pilot customer-facing conversational AI in 2025, with 44 per cent specifically evaluating voice AI as part of their customer experience strategy.

As adoption accelerates, we’ve found that two assumptions frequently shape deployment decisions. Both deserve a closer look.

Misconception #one: Strong English performance means AI is ready for APAC

Many of today’s leading voice models achieve impressive performance in English. Demonstrations often showcase smooth, natural conversations that make the technology feel ready for immediate deployment.

Real customer conversations, however, are rarely that predictable.

Across Asia Pacific, people naturally switch between languages depending on the context of the conversation. A customer may discuss payment details in Bahasa Indonesia before mentioning a product feature in English. A caller in Singapore may move between English and Mandarin without thinking twice. Across Thailand, Vietnam, Malaysia, and the Philippines, regional accents, local vocabulary, and conversational habits add further variation.

These aren’t edge cases. They are everyday interactions.

For AI systems, however, these communication patterns introduce additional complexity. Speech recognition must accurately identify different languages, preserve context as conversations shift, and correctly interpret customer intent despite changes in pronunciation, vocabulary, or sentence structure.

This is one reason speech recognition continues to evolve. While recent advances have dramatically improved accuracy, real-world deployments still need to account for multilingual conversations, regional accents, background noise, and inconsistent network conditions that rarely appear in benchmark evaluations.

Performance in English, therefore, should be viewed as the starting point rather than proof that a voice agent is ready for every APAC market.

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Misconception #two: Supporting multiple languages is the same as localisation

Once businesses recognise the diversity of APAC, the next instinct is often to prioritise multilingual support.

Supporting more languages is certainly important. But localisation involves much more than expanding a language menu.

Customers who speak the same language do not necessarily communicate in the same way. Regional expressions, industry terminology, pronunciation, and code-switching all influence how conversations unfold. A system that performs well in one market may require further adaptation before delivering the same experience in another.

Research from Microsoft reinforces this point. The company found that multilingual users naturally switch between languages during conversations and respond more positively to conversational AI that adapts to those shifts instead of remaining rigidly monolingual.

Customer expectations reinforce the need for localisation. According to CSA Research, 76 per cent of consumers prefer buying products with information in their own language, while 88 per cent of Indonesian consumers prefer content presented in Bahasa Indonesia. Although the study focused on digital content, the same principle applies to voice interactions. Customers expect communication to feel natural, not translated.

Ultimately, customers don’t evaluate AI based on the number of languages it supports. They evaluate whether the conversation feels effortless. If they have to repeat themselves, avoid certain phrases, or adjust the way they naturally speak, the interaction becomes less effective regardless of how sophisticated the underlying model may be.

What businesses are prioritising now

One of the most noticeable changes we’ve seen is how conversations with enterprise customers have evolved.

A year ago, many discussions centred on whether voice AI could realistically replace traditional IVR systems or automate routine enquiries. Today, those questions have become far more operational.

Businesses want to understand how quickly voice agents can be adapted for new markets, how they perform across multilingual contact centres, how they integrate with existing customer workflows, and how consistently they serve customers who communicate differently from one another.

That shift reflects the broader maturity of the market. Organisations are moving beyond experimentation and focusing on deployment quality. Success is no longer measured by whether a voice agent can complete a demonstration. It is measured by whether it can deliver a consistently positive customer experience across thousands of real conversations.

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The next competitive advantage won’t be better voices, it will be better understanding

As foundation models continue to improve, the gap in conversational quality between voice AI platforms is likely to narrow. Natural-sounding speech will increasingly become an expected capability rather than a differentiator.

The next competitive advantage will come from understanding customers more effectively.

For businesses operating across Asia Pacific, that means recognising that localisation is not simply another feature to enable before launch. It is becoming a core deployment strategy that determines whether AI creates friction or removes it.

The organisations that succeed with voice AI will not necessarily be those that automate the greatest number of calls. They will be those that build voice experiences around the realities of how their customers communicate, market by market, language by language, and conversation by conversation.

As AI phone calls become a standard part of customer engagement, understanding people may prove just as important as understanding speech.

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