Enterprises are racing to adopt AI, but many hit the same roadblock: AI-ready infrastructure. However, AI leaders see 1.5 times higher revenue growth and 1.6 times greater shareholder returns¹, showing what’s possible when technology and systems align. Most of this value comes from core business functions, with operations, sales and marketing, and R&D at the forefront. Support functions also contribute, led by customer service, IT, and procurement.¹
 

For many organisations, data silos are a major barrier. 44% of financial firms struggle to manage information stored across multiple locations.² Failed AI projects increase operational costs for 38% of enterprises² and often hurt customer satisfaction and retention³. With data trapped in silos, insights may be delayed, inconsistent, or inaccurate—making it harder for AI investments to deliver value without the right infrastructure.

Traditional cloud strategies were built for a different era. They focused on centralising computing and storage, assuming applications could handle the latency and bandwidth of sending data to distant data centres.⁴ AI workloads do not fit that model.
 

Cloud-smart infrastructure starts with edge computing as a foundation. It places processing closer to where data is generated and consumed. It also involves intelligent workload orchestration that adapts to AI’s unique demands. This approach reduces delays, improves efficiency, and allows organisations to scale AI without being limited by legacy cloud design.

AI workloads demand more from networks than traditional designs can deliver. Enterprises need connectivity that does more than move data—it must feed GPUs directly while providing high-speed access across storage networks.⁵ Without the right network, AI performance suffers, and investments in infrastructure fall short. 
 

Modern software-defined networks give organisations the flexibility to match connectivity to workload needs. GenAI tasks can be routed within specific countries to meet regulatory or security requirements. Networks can monitor usage to control costs and optimise efficiency. Latency-sensitive workloads like AI inferencing can automatically request extra capacity, a capability that will be essential as inferencing grows to dominate AI workloads by 2030, up from 15–30% in 2023.⁶

Financial firms see AI as more than a cost-cutting tool. Most executives now view it as a driver of revenue through personalisation, product innovation, and enhanced security.⁷ Unlocking that potential, however, is not simple.

Sensitive customer data brings strict compliance requirements under GDPR and KYC/AML. AI introduces layers of risk related to bias and misuse, making governance frameworks and regulatory collaboration critical. Its “black box” nature can undermine trust and complicate compliance, forcing firms to invest in explainability tools, bias mitigation, and human oversight.⁸

Deploying AI at scale is further constrained by legacy IT systems, specialised hardware needs, and fragmented data infrastructure. Moving from prototype to production-ready AI that meets regulatory standards remains a common hurdle. At the same time, AI strengthens defences, enabling real-time threat monitoring and better fraud detection. But it also introduces new vulnerabilities, including data poisoning and adversarial attacks. “Security by design” is becoming a requirement rather than an option, balancing innovation with safety.⁸

Overcoming these hurdles is what separates AI pilots from business impact. Firms that get the infrastructure, governance, and network right can scale AI safely, unlock new revenue streams, and strengthen trust with customers and regulators.

Fragmented infrastructure, siloed networks, and complex platform management make scaling AI difficult. Enterprises spend more time managing connectivity, security, and data than delivering business outcomes. Singtel CUBΣ breaks down these silos with a unified portal, enabling seamless management of a comprehensive suite of connectivity services across multiple vendors.

• Simplified connectivity and security management – unified SASE-as-a-Service, SIM-based identity, and 24/7 AI-driven managed security break down silos and provide a single point of accountability.
• Intelligent and predictable data routing – AI algorithms optimise cloud Internet traffic, prioritise 5G slices, and ensure workloads run efficiently.
• Unified digital front – consolidates diverse data streams and uses AI/ML for real-time insights and predictive analysis.
• Streamlined operations – multi-vendor managed services, orchestration, observability, and analytics simplify network deployment, monitoring, and troubleshooting.
• Scalable and cost-efficient – usage-based pricing and adaptable connectivity align resources with dynamic AI workloads.

With CUBΣ, enterprises can move from fragmented systems to an AI-ready environment, optimising performance, reducing complexity, and scaling securely. Scale AI across your enterprise with Singtel.