Businesses must adopt cloud-native infrastructure, which includes powerful computing, high-performance network and storage, container and data management systems, to meet the growing demands of AI. 

 

Cloud-native infrastructure provides the flexibility and scalability needed to support AI’s increasing computational and storage requirements, writes Eric Wan, General Manager of the Middle East, Turkey and Africa, Alibaba Cloud Intelligence.

 

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. This allows businesses to deploy, manage, and update AI applications more efficiently. Importantly, cloud-native platforms are designed to seamlessly integrate with AI development workflows, which means businesses can innovate faster without being held back by infrastructural limitations.

 

$150 billion in real value for GCC

Research indicates that AI could create $150 billion in real value for GCC countries, equivalent to 9 percent of the countries’ combined GDP. In fact, Generative AI alone could be worth 1.7% to 2.8% of annual non-oil GDP in the GCC economies today, according to McKinsey.

 

Companies in the GCC are hungry to adopt AI with around three quarters of businesses reporting using Gen AI in at least one area of operations, while 57% of GCC companies already allocate over 5% of their digital budgets to AI. The investment case for businesses is a no-brainer, with the region forecast to realise approximately $9.9 of economic growth for every $1 invested in Gen AI.

 

Yet, as appetite for AI surges, the infrastructure needed to support this appetite is straining under the weight. And this could have an impact on how quickly organisations can benefit from AI.

 

GenAI requires immense computing power, vast data storage and advanced algorithms. This has a huge impact in terms of energy consumption, costs, sustainability and performance. Traditional infrastructures are ill-suited to support these demands, so any progress has to happen hand-in-hand with infrastructure modernisation. Transformations are needed to ensure that any investments in AI are maximised.

 

Spending on AI infrastructure, which includes hardware such as servers and cloud infrastructure to support AI applications, is substantial but growing at a slower pace than GenAI adoption. Globally, AI infrastructure will see a 14.7% compound annual growth rate (CAGR) through 2028 (according to IDC research), reflecting earlier investments by cloud service providers.

 

In the Middle East, the data centre market is projected to grow from $5.6 billion in 2023 to $9.6 billion by 2029, according to a Turner & Townsend report. Meanwhile, Saudi Arabia has plans to spend $100 billion on AI, with a focus on infrastructure. Even so, data centres across the region are virtually full, and AI adoption rates ultimately remain chained to the pace of infrastructure development.

 

So, what does that AI infrastructure look like? What specifically does AI need and how can businesses transform accordingly?

 

Security and compliance capabilities as standard

AI models process vast amounts of data. Ensuring data security and maintaining compliance with regulatory standards is essential for businesses throughout the entire process of deploying AI solutions. Secure infrastructure that includes encryption, robust access controls, and compliance with global data protection regulations (such as GDPR) 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 infrastructure supporting them can result in data breaches, regulatory fines, and loss of customer trust. Once trust has gone it is almost impossible to regain.

 

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 these 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.

 

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 over-spending on cloud or hardware resources. This cost efficiency is vital for scaling GenAI applications without breaking the budget.

 

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. Businesses are increasingly aware of the need for energy-efficient infrastructure to support their AI initiatives without significantly raising their carbon footprints. Green data centres, renewable energy sources, and energy-efficient hardware are becoming essential components of AI infrastructure strategies.

 

By optimising power consumption and investing in sustainable practices, businesses can reduce operational costs while meeting their sustainability goals. As AI adoption accelerates globally, 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. This should include 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 businesses must be proactive in their infrastructure strategies.

 

The risk of falling behind is real but so is the opportunity to lead 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.--TradeArabia News Service