Artificial intelligence (AI) has been a true driver of transformation in the past year for many industries, with global spending on AI expected to reach $632 billion by 2028, and further boosted by generative AI’s (GenAI) projected annual growth rate of more than 59% according to the latest insights from IDC.
The rapid rise of GenAI has also unlocked many opportunities for Singapore’s economy, with 44% of large enterprises having already adopted AI and underpinning the momentum of the local digital economy which makes up almost 18% of Singapore’s Gross Domestic Product. Looking towards the future, Singapore is also looking to position itself as a regional hub for AI leadership and innovation.
As GenAI capabilities continue to evolve, it is critical to be mindful that enabling latest developments and success stories for AI will require immense computational power, vast data storage and advanced algorithms.
What this means for Singapore’s government and businesses, is the need to allocate resources on a vast scale for energy, sustainability and performance and the endeavour will not be easy given Singapore’s limited resources and small market size.
Legacy cloud infrastructures are however ill-suited to support these demands, so empowering GenAI for businesses has to happen simultaneously with infrastructure modernisation to maximise the investments in AI.
Singaporean organisations hence need to realise that investing in the right hardware such as servers and cloud infrastructure to support all AI use cases will be equally important. With IDC estimating that 24% of overall AI spending globally is used for AI hardware and Infrastructure-as-a-Service (IaaS), robust infrastructure has a crucial role to play in supporting enterprise-grade AI capabilities. So, while GenAI use cases currently get all the spotlight and the accolades, infrastructural investment still remains crucial for supporting broader AI growth and applications, and as a bedrock for Singapore’s AI leadership ambitions.
What then will AI infrastructure that is optimised to implement AI-driven solutions look like? It certainly will have the features of robust, scalable, cost-efficient and secure cloud infrastructure, but organisations also will need to understand what specifically does the AI deployment need and how can the organisation transform accordingly.
Security and compliance capabilities as standard
As AI models process vast amounts of sensitive data, ensuring data security and maintaining compliance with regulatory standards is essential throughout the entire process of deploying AI solutions. Secure infrastructure that includes encryption, robust access controls, and compliance with global and local data protection regulations (GDPR and PDPA) will be needed to safeguard both the models themselves and the data they process.
In this regard, AI infrastructure must be designed not only for performance and scalability but also for security. It should be a standard consideration as failing to secure AI applications or the supporting infrastructure can result in data breaches, regulatory fines, and loss of customer trust. For such a reputable and thriving economy as Singapore, maintaining trust will be invaluable.
Cloud-native a foundation for AI transformation
To meet the growing demands of AI, Singapore’s organisations must adopt cloud-native infrastructure, which includes powerful graphics processing units (GPUs), high-performance network and storage, container and data management systems. Cloud-native infrastructure provides the flexibility and scalability needed to support AI’s increasing computational and storage requirements.
Traditional infrastructures struggle to manage the massive data flows and high-performance needs of modern AI applications. Cloud-native architecture, however, allows businesses to rapidly scale their infrastructure to accommodate fluctuating demands, ensuring that they have the computing power necessary for GenAI models and other data-heavy AI processes.
Cloud-native environments not only support the compute-heavy operations required by AI but also provide essential agility. These features allow organisations to deploy, manage, and update AI applications more efficiently.
Importantly, cloud-native platforms are designed to seamlessly integrate with AI development workflows, which means Singaporean enterprises are able to innovate faster without being held back by infrastructural limitations.
Scalable, reliable and cost-efficient infrastructure for data management
As AI use cases multiply, the need for scalable and cost-efficient cloud infrastructure for data management and analytics becomes increasingly critical. Scalable Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) offerings guarantee that data can be stored, processed and accessed seamlessly, enabling faster and more accurate model training.
Efficient data pipelines, robust storage solutions, and streamlined retrieval systems are crucial for managing large volumes of data before they can be used for model training. An innovative infrastructure also provides the ability to customise and fine-tune models for specific use cases, improving the quality and relevance of AI applications and simplifying AI model development.
For AI applications to provide a consistent and trustworthy user experience, they must be built on reliable infrastructure. Downtime and crashes can erode user trust and disrupt operations. A solid infrastructure minimises the risk of disruptions by ensuring that resources are always available, thus maintaining high availability and uptime.
In addition, efficient AI infrastructure not only supports performance but also helps manage costs. By optimising computing resources through distributed systems, containerisation, and serverless architectures, businesses can avoid overspending on cloud or hardware resources. The enhanced cost efficiency is vital for scaling GenAI applications while staying within budgets.
Energy efficiency and sustainability increasingly key
As AI workloads increase, so does energy consumption and costs. AI models, particularly GenAI, are power-hungry and this has led to concerns about the environmental impact of AI growth, especially net-zero carbon emissions being a key objective of the Singapore Green Plan 2030.
Enterprises hence are increasingly aware of the need for energy-efficient infrastructure to support their AI initiatives without significantly raising their carbon footprints, and there has been a focus in incorporating green datacentres, renewable energy sources, and energy-efficient hardware into AI infrastructure strategies.
By optimising power consumption and investing in sustainable practices, organisations can reduce operational costs while meeting their sustainability goals. As AI adoption accelerates for Singapore, the focus on energy-efficient infrastructure will become a key differentiator for businesses looking to align innovation with corporate social responsibility and a need to manage costs more closely.
So, as AI continues to evolve, businesses must not only address current infrastructure challenges but also anticipate future shifts in the AI landscape, including security and regulatory compliance as well as technical and sustainable needs. The convergence of real-time decision-making, augmented working environments and the rising demand for sustainability means that enterprises must be proactive in their infrastructure strategies.
The risk of falling behind is of course real and present, but there are also opportunities for Singapore to leapfrog ahead in this transformative era of AI. The question is no longer whether to invest in cloud infrastructure modernisation but how quickly organisations can make the leap to stay competitive.
