Singaporean customers collectively spend 40 million hours on hold due to inefficient customer service, costing SGD 1.3 billion (US 1 billion) in productivity, a wake-up call for IT leaders.
This inefficiency arises due to disconnected systems and limited self-service options, forcing customers to rely on live agents. The reliance on legacy IT systems and siloed applications makes it difficult for agents to access information and serve customers efficiently.
Overcoming these constraints require a unified platform that connects people, processes, and data, TZ Wong, head of analyst relations, APAC, at ServiceNow, shared in an interview with iTnews Asia.
This gives agents real-time visibility into customer context, reducing service friction and driving stronger customer loyalty, Wong said.
Wong estimates that in competitive markets like Singapore, a large majority, or 85 percent of consumers, would leave a brand after a poor service experience.
Tech-native startups hold a competitive advantage
Despite digital transformation efforts, enterprises continue to face fragmented customer service systems due to legacy infrastructure and organisational silos, where business units operate independently.
According to Wong, this fragmentation persists as IT leaders remain hesitant to overhaul legacy systems due to cost, complexity, and risk; hence, they prefer to build on top of existing systems rather than fully integrate.
This is where digital-native businesses and startups, such as digital banks, that are not burdened by legacy infrastructure, have a headstart.
These organisations start with unified architectures that help deliver superior customer service from the outset.
In competitive markets like Singapore, Wong said, where consumers usually leave a brand after experiencing poor service, these digital-first companies hold an advantage in being able to respond to consumers through connected and consistent support.
Focus on three metrics in your unified platform
Enterprises with legacy systems need to choose the right technology platform that connects systems, data, and business processes through a single data model, said Wong.
A single data model allows agents in the organisation to get a unified customer view and enables them to focus on resolving issues instead of navigating disconnected systems, he added.
Wong emphasises that IT leaders should focus on three key metrics to track and improve efficiency.
This includes mean time to resolution (MTTR) to understand how long it takes to fully resolve an issue, not just first contact resolution.
Second, agent throughput to look at the number of requests an agent can complete.
And third is the call deflection rate to find out how many calls are avoided by enabling self-service.
These metrics directly impact productivity, cost, and customer satisfaction, said Wong.
“To improve these metrics, AI-driven platforms can enhance customer and agent experiences by unifying workflows and reducing service resolution time.”
Learning from high impact use cases
A fully integrated self-service suite across all channels directly impacts the customer experience.
“When self-service options are disconnected and fail to meet expectations, AI can improve responsiveness and consistency, provided all channel data is unified and visible to agents during live interactions,” said Wong.
For agents, Wong outlines three high-impact AI use cases.
First, AI can automatically summarise cases for smooth handovers in complex industries like telecom.
Second, AI can consolidate customer data, including history, sentiment, purchases, and entitlements, into a single view, eliminating the need to switch between systems.
Third, AI can recommend the next best actions in real time, helping agents resolve issues faster, particularly for new hires unfamiliar with internal systems.
AI capabilities are only effective when built on a unified platform that integrates data, systems, and workflows. Without this foundation, AI cannot deliver meaningful improvements.
- TZ Wong, Head of Analyst Relations, APAC, ServiceNow
Griffith University manages over 500,000 service tickets
Wong has cited how Australia’s Griffith University had to manage over 500,000 service tickets annually for 45,000 students.
To address this, Griffith adopted an enterprise-wide platform approach to unify systems and streamline service delivery.
This integration enabled an 87 percent increase in overall self-service adoption, which translated to a 31 percent reduction in call volumes, said Wong.
It further resulted in a 46 percent decrease in email inquiries and a 26 percent drop in walk-in requests, he added.
With AI integrated across the platform, Griffith expects a 25 percent improvement in resolution time.
This enables consistent student support while scaling services without added headcount.
A complete overhaul can be too costly
For organisations with legacy systems, a complete overhaul can be too costly or complex, and the practical approach is to overlay a modern AI-powered customer service platform.
“Such integration happens through APIs, which pull data from systems of record into a single unified interface, giving agents a ‘single pane of glass’ to access all relevant customer information without replacing core systems,” said Wong.
In many cases, especially among global enterprises, organisations have adopted different customer service systems in different regions, he said.
He added that instead of forcing consolidation, these systems can be integrated into a unified platform layer that brings consistency without disruption.
When introducing AI capabilities, organisations should take a phased approach.
Begin with low-risk, high-volume areas, including self-service functions for password resets or login issues, Wong advised.
This allows teams to test and scale the implementation while avoiding disruptions to high-stakes operations.
Wong said forward-thinking companies can consider eventually transitioning to Contact Centre as a Service (CCaaS) model.
These cloud-based platforms can intelligently route interactions to the right agents, handle omnichannel interactions, and allow AI to power the front-end while humans focus on resolution.
“The key is to identify quick wins, like deploying AI for self-service, that show immediate ROI while building toward longer-term transformation,” Wong said.
