In an age when AI chatbots respond at lightning speed and algorithms triage service requests, customer support is undergoing a profound transformation. But even as artificial intelligence (AI) becomes ever more capable, one truth persists: some problems still demand a human touch.
A recent Star article highlights this tension through the lens of the travel tech company Trip.com, which has integrated AI into its support operations—yet insists that human agents still matter most when situations get messy.
This story is not just about travel, it is a microcosm of a broader existential question for service industries: Where do machines shine, and where must humans take over?
The Rise of AI in Support: Efficiency First
Trip.com’s Malaysia support centre is a vivid example. What began in 2023 as a modest 10-person team grew tenfold in two years. The expansion owes much to AI integration, which helps handle routine queries, provide instant updates, and anticipate customer issues.
The company cites features like TripGenie, an AI assistant built into its mobile app, which offers real-time itineraries, flight updates, hotel changes, and even language translation.
For many travelers, especially younger or tech-savvy ones, this speed and convenience is indispensable. According to a Trip.com report, only 3% of Malaysian users avoid AI-based tools during trip planning, compared with 10% in the wider Asia-Pacific region.
In such scenarios, AI is not just helpful, it is expected. A traveler stranded by a cancelled flight or delayed connection wants answers fast, often before they even think to ask.
The “Edge” Where Humans Still Rule
Yet every system has its limits, and the realms where AI struggles are instructive. Trip.com acknowledges this candidly. When disruptions grow complex, changes in flight schedules, multi-leg reroutes, cross-stakeholder bookings—AI is insufficient. That’s when human agents must step in.
Human agents do more than just problem-solve. They can read tone, infer frustration, manage expectations, and provide reassurance. They can coordinate across airlines, hotels, third-party providers, and navigate policy exceptions. That kind of emotional intelligence remains beyond most AI systems today.
One Trip.com executive put it plainly:
“AI frees our human agents to focus on complex, high-touch cases—where empathy and personalised care are essential.”
In other words, AI is a tool, but humans are still the ones who heal.
AI + Human: A Hybrid Model
Trip.com’s approach is illustrative of what many are calling “hybrid support”: let AI handle what it can, escalate to humans when it must.
In practical terms:
- Tier 1 (AI): Routine queries, booking status, cancellations, itinerary changes, FAQs.
- Tier 2 (Humans): Edge cases, multi-leg disruptions, exceptions, refunds, inter-provider coordination.
- Back-end augmentation: AI helps human agents by surfacing predictive insights (e.g. “customer’s flight at risk”) so agents can preempt issues.
Trip.com uses predictive AI to flag potential disruption and proactively reach out to customers with alternatives before problems escalate.
This hybrid model addresses two essential demands: speed and empathy.
Cultural and Local Touch in Support
Another insight from the article: situating human support teams locally matters. Trip.com’s Kuala Lumpur support centre operates with full local staff fluent in Bahasa Malaysia and English. Because they understand culture and expectation, they can offer more refined solutions.
This matters especially in a region as linguistically and culturally diverse as Southeast Asia. A support agent who knows local idioms, norms, and holiday schedules can defuse tension more effectively than a generic overseas script.
In this sense, local human agents become the bridge between machine logic and human experience.
The Risks of Over-Automation
Blindly automating every interaction carries its own perils:
- Dehumanisation: When support feels robotic, customers can feel alienated rather than helped.
- Escalation fatigue: If many queries bounce through AI systems before finally being transferred to humans, it lengthens frustration.
- Context loss: AI sometimes lacks memory or awareness of prior interactions, which humans can recall.
- Bias or error propagation: AI trained on imperfect data can misinterpret tone, misclassify urgency, or reinforce inequity.
In high-stakes categories, healthcare, finance, legal, travel, mistakes are more costly. That’s why the “human fallback” is not a feature; it’s a necessity.
What This Means for Service Design
- Design with escalation in mind
From day one, systems should be built to escalate gracefully. AI should flag when thresholds are hit, cue handoff, and pass context to human agents seamlessly. - Train for empathy
Human agents should not just know policy, they should be able to demonstrate care. Soft skills training is as important as technical certifications. - User-first transparency
Let users know when they are interacting with AI versus human. Explain limitations. Offer visible “contact a human” options early. - Feedback loops
Human post-mortems on edge cases should feed back into AI training for continuous improvement. - Local context matters
Support centers must reflect language, culture, and tone of their markets. Uniform “global voice” models often fail in local nuance.
In the End, AI Enhances, Humans Endure
The Star article’s narrative is simple but profound: AI brings speed, accuracy, and foresight, but cannot replace human warmth, judgment, or care.
In an era where service is often judged by milliseconds, human agents remain the ultimate differentiator, especially when things go wrong.
The journey of customer support is no longer a race between human and machine. The future is a partnership, where artificial intelligence handles the routine, and human beings handle the meaning.
Because in the end, a chatbot may resolve a ticket, but only a person can repair trust, restore patience, and rehumanize the experience.
