Alibaba Cloud Recommends Creating Business Advantages with Strong AI and Cloud

In the booming era of AI, organizations must accelerate infrastructure transformation! Alibaba Cloud reveals the secret to creating a powerful, secure and sustainable cloud to support the increasing demand for AI and open opportunities for Thai businesses to become leaders in the digital age.

February 5, 2568. Everyone now recognizes that AI is transforming every industry like never before. IDC projects global AI spending to reach $632 billion by 2571, with generative AI (GenAI) growing at a remarkable rate of 59.2% per year. However, the increasing capabilities of AI are placing a heavy burden on the infrastructure required to support them, impacting the speed at which organizations can leverage AI.

Sean Yuan, Vice President of International Business and General Manager for South Pacific and Japan at Alibaba Cloud Intelligence, revealed that the use of AI in Thailand is rapidly increasing, mirroring trends in other countries worldwide. A 2024 survey on AI readiness for digital services, conducted by the Electronic Transactions Development Agency (ETDA) in collaboration with the National Science and Technology Development Agency (NSTDA), found that more than half of the Thai organizations surveyed recognize the need for AI adoption and can identify areas where it would be used.

It was also found that 17.8% of agencies have already implemented AI, slightly higher than last year. The majority of organizations, 73.3%, have plans to implement it in the future. The top three goals for AI use by Thai organizations are to manage the organization internally (69.6%), increase work efficiency (59.8%), and create added value for products or services (56.8%).

GenAI is a technology that is driving significant growth, but it requires massive processing power, massive storage, and highly capable algorithms, which have significant impacts on energy consumption, cost, sustainability, and efficiency. Traditional infrastructure is not up to par with these demands, so any implementation must be accompanied by infrastructure modernization. As you can see, changes are necessary to ensure that you get the most out of your AI investments.

Spending on AI infrastructure, which includes hardware such as servers and cloud infrastructure to support AI applications, is growing at a much slower pace than GenAI adoption. AI infrastructure is projected to grow at a compound annual growth rate (CAGR) of 14.7% through 2571 (according to research from IDC), reflecting prior investments by cloud service providers.

Of all AI spending, 24% was spent on AI hardware and Infrastructure-as-a-Service (IaaS), so AI hardware and IaaS are critical to AI capabilities. As GenAI increases, AI infrastructure spending remains critical to supporting AI growth and the widespread adoption of AI applications.

Investing in a robust, scalable, and secure cloud infrastructure is now essential for businesses to successfully deploy AI solutions. But what does AI infrastructure look like? What specifically does AI require, and how can businesses adapt to meet those needs?

Standardized safety and compliance

AI models process large volumes of data, so data security and compliance are critical for businesses to ensure throughout the process of deploying AI solutions. A secure infrastructure that includes encryption, strong access controls, and compliance with global data protection regulations (such as GDPR) is essential to help protect both the models themselves and the data they process.

Therefore, designing an AI infrastructure must not only be efficient and scalable, but also secure. These are fundamental attributes that must be in place and upheld, because failure to secure AI applications or the infrastructure that supports them can lead to data breaches, non-compliance fines, and loss of customer trust. Once customers lose trust, it is very difficult to regain it.

Cloud-native is the foundation that supports AI adoption

Businesses must adopt cloud-native infrastructure to meet the growing demands of AI. Cloud-native infrastructure includes powerful compute, high-performance networking and storage, containers, and data management systems, and provides the flexibility and scalability needed to support the increasing compute and storage demands of AI.

Legacy infrastructure struggles to handle large volumes of data and doesn't support the high performance demands of modern AI applications. Cloud-native enables businesses to quickly scale their infrastructure to meet evolving demands and ensure they have the processing power they need to run GenAI models and other data-intensive AI applications.

Cloud-native environments not only support the computationally intensive tasks that AI requires, but also offer significant agility, allowing businesses to more efficiently deploy, manage, and update AI applications. Importantly, cloud-native platforms are designed to integrate seamlessly with AI development workflows. This means businesses can innovate faster, without being constrained by infrastructure constraints.

Scalable, reliable and cost-effective data management infrastructure

As AI adoption increases, the need for scalable and cost-effective cloud infrastructure to manage and analyze data becomes increasingly important. Scalable Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) ensure that data can be stored, processed, and accessed seamlessly, enabling faster and more accurate model training. Efficient data pipelines, robust storage solutions, and powerful retrieval systems are essential to managing this massive amount of data before it is used to train models. These new infrastructures also offer the ability to fine-tune models for specific applications, increasing the quality and relevance of AI applications, and simplifying AI model development.

A robust AI infrastructure not only supports productivity but also helps manage costs. By optimizing compute resources through distributed systems, containers, and serverless architectures, businesses can avoid overspending on cloud or hardware resources. This spending efficiency is critical to scaling GenAI applications without impacting budgets.

Energy efficiency and sustainability are becoming more important.

Energy consumption and costs are increasing as AI workloads grow. AI models, particularly GenAI, are energy intensive, raising concerns about the environmental impact of AI growth. Businesses are recognizing the need for more energy-efficient infrastructure to support their organization's AI deployments without significantly increasing their carbon footprint. Green datacenters, renewable energy sources, and energy-efficient hardware are becoming key components of their AI infrastructure strategy.

Improving energy efficiency and investing in sustainability practices can help businesses reduce operating costs while meeting sustainability goals. As the world increasingly uses AI, a focus on energy-efficient infrastructure will become a key differentiator for businesses looking to align their innovations with their corporate social responsibility and the need to manage costs more carefully.

AI continues to evolve, so organizations need to not only address current infrastructure challenges, but also anticipate future changes to the AI ​​landscape, which should include security and compliance, technical requirements, and sustainability. The convergence of real-time decision-making, augmented working environments, and increased sustainability demands mean that organizations need to take a proactive infrastructure strategy.

The risk of being left behind is real, but so are the opportunities to be at the forefront of AI transformation. The question is no longer whether to invest in modernizing cloud infrastructure, but how much organizations can sustain and exponentially increase their competitive edge.

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