Artificial intelligence has entered a phase of rapid, large-scale deployment, but without robust, secure, and high-performance infrastructure, its economic impact will be constrained. For Singapore and Southeast Asia, the next phase is all about execution.
Artificial intelligence (AI) has moved beyond experimentation. It is now being embedded into core business processes, public services, and critical infrastructure. What we are witnessing is not a short-term surge, but the early stages of an ‘AI supercycle’, a sustained period of compounding adoption and impact.
This cycle is driven by a reinforcing dynamic: AI generates exponential growth in data, which in turn requires more advanced infrastructure, enabling further AI deployment at scale.
The scale of adoption is already significant. According to McKinsey, 88 percent of organizations are using AI in at least one business function, up from 78 percent a year earlier. Yet many of these deployments remain fragmented or in pilot stages.
This creates a clear inflection point. The limiting factor is no longer access to AI models or use cases: it is the ability to deploy, scale, and operationalize AI reliably.
At its core, this is an infrastructure challenge. And to my mind, Singapore offers a useful lens through which we can understand what comes next, and how the AI-driven transformation of networks can help capture the value of the AI supercycle.
Singapore: From strategy to coordinated execution
Singapore has taken a deliberate and structured approach to AI adoption. Its National AI Strategy 2.0 provides a comprehensive framework for integrating AI across key sectors of the economy.
What is now emerging is a shift from strategy to execution.
The formation of a national AI Council, chaired by the Prime Minister, signals a move towards tighter coordination across government, industry, and research. This reflects a growing recognition that AI deployment at scale requires alignment across policy, infrastructure, talent, and governance.
This evolution is important, as I see that it marks the transition from AI ambition to operational delivery.
It also presents another example of Singapore as a trusted hub for global businesses, digital innovation, and AI adoption. Its approach has broader regional implications. Across Southeast Asia, governments and enterprises are accelerating digital transformation, with AI expected to play a central role in productivity and growth.
However, as momentum builds, a common constraint is infrastructure readiness.
Re-architecting the data center for AI at scale
This constraint is most visible in the transformation of data center infrastructure.
Traditional architectures, optimized for predictable workloads and storage, are not designed for the intensity and variability of AI.
AI’s high workloads require high-performance computing, real-time data processing, and seamless integration across cloud, edge, and core environments. As a result, the data centre is undergoing a fundamental redesign.
Key requirements, for example, such as those provided by our data center network solution, deliver the flexibility needed to build and deploy network infrastructures that can support both today’s and tomorrow’s AI workloads, including:
- Ultra-low latency to support real-time inference
- High-throughput networking for large-scale model training
- Scalable architectures that can flex with demand
- Advanced automation to reduce operational complexity and eradicate human error
In this context, data centers are evolving into resilient strategic assets that are central to competitiveness in the AI era, rather than simply supporting infrastructure.
We are already seeing this shift in investment patterns. New AI-focused facilities – including sovereign ‘AI factory’ initiatives in Australia; deploying AI for superior digital services in Philippines ; partnerships to modernize data center infrastructure in Asia, and enhance security and future-proof data centres in Malaysia – illustrate how quickly the landscape is evolving.
However, performance gains alone will not be sufficient to support sustained AI adoption. Because as AI scales, so too does risk.
Cybersecurity as a strategic enabler of AI
Indeed, AI introduces new layers of complexity into the cybersecurity landscape. It expands the number of connected endpoints, increases data sensitivity, and accelerates the speed at which threats can evolve.
Recent incidents highlight the urgency of this challenge. Cyberattacks targeting major telecommunications operators in Singapore, linked to UNC3886, demonstrate the potential impact on critical infrastructure.
Across the Asia Pacific, the threat environment is intensifying. Recent threat intelligence analysis shows that the region is a particular hotspot. We found that this region shows the highest rates of abuse in signaling protocols like SS7 (Signaling System No. 7) and Diameter (at 58%), and a high prevalence of malware specifically adapted for telecom systems.
For many operators, more than 8 in 10 in the region, AI-driven threat detection is now a priority. The message is clear: resilience is a precondition for innovation.
In practical terms, this means trust becomes a prerequisite for scale. Without it, AI deployment will be constrained by risk, regulation, and loss of confidence.
The path forward: From opportunity to execution
The AI supercycle is no longer a theoretical concept. It is already reshaping competitive dynamics across industries and geographies.
Singapore is leading with strong governance, reinforced by the new AI Council, but the next mountain to climb is execution at scale. For the wider region, this is a pivotal moment to accelerate growth and build AI-native capabilities.
The next phase, however, will be defined by execution. This includes:
- Investing in high-performance, AI-ready infrastructure
- Ensuring security and resilience are built in from the outset
- Aligning public and private sector efforts to scale deployment
The organizations and economies that succeed will not necessarily be those that adopt AI first, but those that build the capabilities to sustain and scale it effectively.
Infrastructure sits at the center of that equation. Without it, AI remains fragmented and limited in impact. With it, AI becomes a transformative force across sectors, from transportation and manufacturing to energy and public services.
The task now is clear: to move from ambition to delivery, and to build the digital foundations that will support the next phase of growth.
Ming Kin Ngiam is the Head of Nokia South East Asia (SEA) at Nokia, where he oversees the company’s business and operations in Singapore, Malaysia, Thailand, the Philippines, Cambodia, Laos, Bangladesh, and Sri Lanka.
Ming Kin has held several leadership positions at Nokia over the past 15 years, including roles in solutions, sales, and operations. He has a proven track record of leading high-performing teams and cultivating strategic partnerships to achieve sustainable business success.
Outside of work, Ming Kin is passionate about volunteering and sports.
Based in Singapore, Ming Kin holds a Bachelor of Engineering (First Class) from the University of Birmingham, U.K., and a Diploma in Management from the Singapore Institute of Management.
TNGlobal INSIDER publishes contributions relevant to entrepreneurship and innovation. You may submit your own original or published contributions subject to editorial discretion.
Rethinking the future of 5G through the lens of extended reality (XR)