As AI goes mainstream in Asia Pacific, organizations often grapple with infrastructure governance and ROI challenges.
With AI adoption accelerating across the enterprise landscape, many organizations in Asia aim to deploy AI at scale while staying cost-effective, secure and compliant.
How can they achieve this without overwhelming technology infrastructure or creating operational chaos? DigiconAsia finds out in the Q&A with Matthew Hardman, Chief Technology Officer, APAC, Hitachi Vantara.
How are savvy enterprises building AI-ready environments without creating “chaos”?
Matthew Hardman (MH): As AI moves from hype to reality, the real challenge for enterprises isn’t just adopting it; it’s doing so in a way that doesn’t overwhelm IT systems or create operational chaos. A big part of that comes down to having the right data foundation. AI is only as good as the data it’s trained on, but the reality is that many organizations are still struggling with messy, unstructured, and unreliable data.
Our research found that, in Asia, only 30% of data is structured, which means most AI systems are working with information that’s incomplete or difficult to process.
Beyond data, AI lifecycle management is another major factor. AI isn’t just about building models – it’s about deploying, monitoring, and continuously improving them. Without a strong MLOps framework, businesses can quickly run into bottlenecks, where AI projects get stuck in pilot mode and never scale.
Governance is equally important, because without proper oversight, AI can lead to compliance risks, security issues, and even unintended bias in decision-making.
Scalability is also a key consideration. AI workloads can be resource-intensive, and with data storage demands in Asia expected to rise 123% in the next two years, many enterprises risk overloading their infrastructure. The smartest companies are tackling this by optimizing cloud strategies, leveraging AI-accelerated infrastructure, and ensuring their systems can handle increasing AI demands without spiraling costs.
The goal isn’t just to adopt AI – it’s to do it in a way that is scalable, structured, and aligned with real business outcomes.
