FEATURE: AI INFRASTRUCTURE
bottlenecks occur when AI workloads are deployed? How tightly integrated are systems? This evaluation should go beyond technical specifications, probing organizational readiness and skill gaps.
Next, CIOs must embed conscious decoupling into their AI roadmap. This is not a one-off IT project but a strategic capability that underpins AI success. Investment decisions, technology roadmaps, and organizational structures must all be aligned to support decoupled architectures.
Equally critical is choosing the right partners. AI-ready networks require expertise beyond basic connectivity. Enterprises need providers who understand edge computing, multi-cloud integration, distributed security, and the performance nuances of different AI workloads. Skilled talent is scarce, so collaboration with experienced managed service providers is essential to accelerate transformation.
Consider financial services, one of the most advanced AI adopters in APAC. Banks and fintechs rely on AI for fraud detection, algorithmic trading and personalized digital banking. These applications require split-second decision-making across petabytes of real-time data.
A monolithic network struggles under this load. By contrast, banks using decoupled architectures can dynamically route traffic between cloud environments, isolate sensitive workloads for compliance, and scale infrastructure instantly to handle demand spikes during trading hours. The difference is not just technical – it translates directly into reduced fraud losses, improved customer experiences, and higher profitability.
AI in smart cities and manufacturing
The rise of Smart Cities across APAC – from Singapore’ s Smart Nation program to South Korea’ s smart mobility initiatives – underscores the importance of modern infrastructure. Smart Cities depend on distributed AI to manage transportation, energy, and security systems. These workloads require real-time processing at the edge, which monolithic networks cannot support effectively.
Manufacturing provides another example. Predictive maintenance powered by AI depends on moving IoT data from thousands of sensors across global supply chains. Traditional networks introduce latency that undermines the predictive models. Decoupled networks enable faster, more reliable data flows, reducing downtime and cutting operational costs.
IDC findings: AI ambitions versus network reality
This perspective is reinforced by the IDC InfoBrief Enterprise Horizons 2025: Technology Leaders Priorities: Achieving Digital Agility, commissioned by Expereo. Based on a survey of 650 technology leaders across Europe, the US and APAC, the report reveals a striking disconnect between ambition and reality.
38 INTELLIGENTCIO APAC www. intelligentcio. com