The race for AI leadership is on. Across the Asia Pacific region, countries and businesses alike are racing to dominate AI. The proof is in the pudding: IDC projects that AI and generative AI (GenAI) spending will hit a whopping US$110 billion in the APAC region by 2028, growing 24 percent annually.
However, while ambition is universal, wanting does not lead to winning. The gap between big plans and real execution will separate the leaders from the followers.
Enterprises today face a fundamental challenge: data remains siloed across on-premises infrastructure, multiple cloud platforms, and edge networks. This fragmentation can have severe repercussions for the potential success of AI initiatives, which demand unified, accessible data to succeed.
For organisations relying on AI initiatives to drive growth, the stark reality is that up to a fifth of these projects are doomed to fail without an intelligent data infrastructure capable of bridging these silos. What has changed is the timeline. Organisations that once had years to address data complexity are now in a tight race and have months or even weeks to get it right.
In my conversations with technology leaders across the region, there is a critical characteristic of successful AI transformations: Close alignment between business leadership and technology execution.
Yet in Singapore, we are seeing significant gaps. While CEOs declare AI readiness and announce ambitious initiatives, their IT counterparts are painting a different picture of infrastructure preparedness. When leadership teams are not aligned on both opportunity and challenge, AI initiatives either stall or deliver disappointing results.
- You Qinghong, Solutions Engineering Lead for Greater China, ASEAN and South Korea, NetApp
Three AI success pillars influencing the intelligent infrastructure imperative
Organisations that are pulling ahead in the AI race do not get there by accident. Their success is built on three strategic pillars that connect their technological foundations directly to business goals.
- Modernise data architecture for unified access and insight
Organisations may recognise that data is fuel for AI, but the former is often trapped in separate systems. Take global manufacturing, for example, data siloed across factory-floor systems, separate third-party logistics trackers, and corporate HQ planning applications can create supply chain friction. This fragmentation therefore impacts AI models’ capabilities to effectively forecast demand, predict maintenance, or optimise delivery routes.
The solution is to modernise data architecture for AI-native operations. This means moving beyond legacy systems to an intelligent data infrastructure that unifies these disparate sources. The goal is to allow data to be accessed securely and efficiently wherever it lives, helping the organisation scale insights without adding new complexity.
- Embed security and governance from day one
Forward-thinking organisations build security into their data infrastructure from the start rather than adding it as an afterthought, as traditional cybersecurity best practices are not enough for the unique threats facing AI systems.
This requires building security resilience directly into AI workflows, which involves implementing a zero-trust mindset, where every access request is verified, and deploying AI-specific governance that tracks data from its source through to the model's output.
For industries with strict data privacy rules like financial services or healthcare, this also means using capabilities that allow models to be trained on sensitive data across distinct locations, but without ever moving or exposing the data itself.
- Align leadership to drive scalable, cost-effective AI
AI workloads present unique infrastructure challenges due to their unpredictable nature—requiring massive compute and storage resources during model training, then shifting to consistent requirements for inference operations. Managing this efficiently is as much of a business challenge as it is a technical one. A common roadblock is the disconnect between a CEO’s AI ambitions and the reality of their existing IT infrastructure.
To overcome this, leadership must accelerate alignment with IT. This starts with joint AI readiness assessments that evaluate infrastructure capabilities against business goals. When CTOs function as strategic partners, and not just service providers, the organisation can build an elastically scalable infrastructure that automatically provides resources when needed, while optimising costs when models are running normally. This ensures that AI initiatives are not only powerful but also economically viable.
Executing these three pillars can create a compounding advantage, building a sturdy foundation for sustained competitive differentiation in an increasingly AI-driven marketplace.
The competitive advantage of getting data right
APAC’s AI leaders will not be determined by budget size or ambition alone, but by those who recognise that AI success begins with data excellence — making their data simple, secure, and sustainable at scale. Organisations with mature data infrastructure are already seeing faster AI deployment, lower costs, reduced carbon footprints, and greater market agility.
Across APAC, different markets are taking distinct approaches to AI and its required infrastructure. Singapore, for example, committed up to S$500 million toward AI-ready infrastructure, emphasising seamless data sharing across government and private sectors. In contrast, Japan's Society 5.0 framework aims to create a "super-smart society" where AI and digital technologies seamlessly integrate across all sectors — from healthcare to manufacturing — to solve ageing population challenges while driving economic growth.
These varying strategies mean organisations must navigate not just their own AI transformations but also position themselves within their market's specific competitive dynamics. Intelligent data infrastructure therefore, becomes the universal differentiator that enables faster innovation and sustainable growth, regardless of regional approach.
The AI leadership race is just beginning, but the foundation for success is being built today. Organisations that align leadership teams, modernise data infrastructure, and execute with precision will pull ahead in a competition that will define the next decade of business success.
You Qinghong is NetApp’s Solutions Engineering Lead for Greater China, ASEAN and South Korea.
