AI is now a top priority for boards, rather than just a pilot project. Its integration into areas such as fraud detection and logistics is accelerating across Asia-Pacific. However, data gravity presents significant challenges in scaling AI operations. The issue arises from the increasing volume and complexity of data needed for advanced AI models, creating bottlenecks that complicate data handling across various infrastructures while ensuring security and compliance.

The concept of data gravity describes the pull of large datasets, which attract computation and services to their location. In the Asia-Pacific context, local regulations and clouds enforce data locality, complicating centralized data handling. This leads to performance inefficiencies and compliance difficulties for AI models.

To mitigate these challenges, application delivery controllers (ADCs) have evolved to manage intelligent data flow. Once tools for managing traffic, ADCs now optimize and secure data transfer across diverse environments, enhancing AI capabilities. Features like low-latency routing, encryption, and traffic shaping enable AI models to operate effectively in fragmented regulatory landscapes.

As AI becomes more prevalent, the risks of rogue AI and unmanaged tools accessing sensitive data increase. Organizations require visibility into AI operations to ensure compliance and security. ADCs provide centralized control, mitigating these risks without stifling innovation, particularly in sectors where data integrity is crucial.

Cost management is another major concern; the infrastructure needed for AI can rapidly inflate budgets. By integrating FinOps principles, businesses can better manage these expenses. ADCs contribute by optimizing data traffic and workload placement, ensuring cost-effective AI deployment across regions.

Building an AI-ready infrastructure necessitates more than just computational power. It involves designing systems that support compliance, latency requirements, and security across geographies. A robust infrastructure serves as both the backbone and enforcer of policy for AI applications, ensuring they remain scalable and effective in the real world.

Contributed by F5.