Inframodernization contributes to the development of artificial intelligence

It doesn’t take a genius to realize that artificial intelligence (AI) is transforming industries at an unprecedented rate.

According to IDC, global spending on AI is expected to reach $632 billion by 2028, with generative AI (GenAI) growing at an impressive annual rate of 59.2 percent.

However, as AI capabilities grow, the infrastructure needed to support them is strained, impacting how quickly organizations can realize the benefits of AI.

BUILDING THE FUTURE. While GenAI provides significant growth, it requires enormous computing power, huge data warehouses, and advanced algorithms. PHOTO COURTESY FROM ALIBABA CLOUD

North American and Asian companies are aggressively adopting artificial intelligence, with 76 percent of North American companies and 70 percent of Asian companies already beginning their AI transformation. However, to maintain their edge, leaders must actively pursue transformation, says McKinsey, a business consulting firm. Less than 10 percent of Asian organizations have found a way to leverage cross-generational AI. Those who do will likely have a competitive advantage.

In the Philippines, companies are already realizing the value of AI transformation. According to accounting and consulting firm Deloitte, two-thirds (62 percent) of business leaders are excited about the use of AI, and more than three-quarters (79 percent) expect GenAI to lead to significant organizational change in less than three years.

Similarly, a report by another business consulting firm PWC found that 78 percent of Philippine executives believe that AI will improve the quality of their products and services, and 77 percent believe that AI will increase the intensity of competition in their industries. However, most companies have yet to implement AI in the workplace (61 percent) and change their strategies (65 percent).

While GenAI provides significant growth, it requires enormous computing power, huge data warehouses, and advanced algorithms. Traditional infrastructures will not be sufficient to meet these needs, which can lead to huge energy consumption, high costs, sustainability issues and even reduced overall productivity. Transformations of business infrastructure are necessary to ensure that any investment in AI is maximized.

Spending on AI infrastructure, including hardware such as servers and cloud infrastructure to support AI applications, is significant but growing slower than GenAI adoption. According to IDC research, artificial intelligence infrastructure will grow at a compound annual growth rate (CAGR) of 14.7% through 2028, reflecting previous investments by cloud service providers. AI hardware and infrastructure as a service (IaaS) account for about 24 percent of total AI spending, highlighting their importance in unlocking the power of AI. So while GenAI is gaining more attention, spending on AI infrastructure remains critical to supporting broader AI growth and applications.

For companies looking to implement AI solutions, investing in a reliable, scalable and secure cloud infrastructure is now critical to success. But what does this AI infrastructure look like? What exactly does AI need and how can businesses transform accordingly?

Security, compliance capabilities

Artificial intelligence models process huge amounts of data. Data security and regulatory compliance are essential for businesses implementing artificial intelligence solutions. Protecting models and the data they process will require a secure infrastructure that includes encryption, strong access controls, and compliance with global data protection regulations (such as GDPR).

Therefore, AI infrastructure must be designed for performance, scalability, and security. This should be a standard consideration, as failure to secure AI applications or the infrastructure that supports them can result in data breaches, regulatory fines, and loss of customer trust. Once trust is lost, it is almost impossible to restore it.

Artificial Intelligence Transformation Foundation

To meet the growing demands of AI, enterprises must implement cloud infrastructure that includes powerful computing, high-performance networking and storage, and container and data management systems. Cloud infrastructure provides the flexibility and scalability to support growing artificial intelligence computing and storage requirements. Traditional infrastructures struggle to manage the massive data flows of modern AI applications and the high performance demands.

However, cloud architecture allows enterprises to quickly scale their infrastructure to meet changing needs, ensuring they have the computing power needed for GenAI models and other data-intensive AI processes.

Cloud environments support the complex computing operations required for AI and provide the necessary flexibility that allows enterprises to more efficiently deploy, manage and update AI applications. Importantly, cloud platforms are designed to seamlessly integrate with AI development workflows, meaning companies can innovate faster without being held back by infrastructure constraints.

Scalable, reliable and cost-effective infrastructure

As the number of AI use cases increases, the need for scalable and cost-effective cloud infrastructure for data management and analytics becomes more critical. Scalable infrastructure-as-a-service (IaaS) and platform-as-a-service (PaaS) offerings ensure seamless data storage, processing, and access for faster, more accurate model training.

Efficient data pipelines, robust storage solutions, and optimized search engines are critical to managing these large volumes of data before they can be used for model training. The innovative infrastructure allows models to be customized and fine-tuned for specific use cases, improving the quality and relevance of AI applications and simplifying the development of AI models.

AI applications must be built on a reliable infrastructure to provide a consistent and trustworthy user experience. Downtime and disruptions can undermine user confidence and disrupt operations. Reliable infrastructure minimizes the risk of failure by ensuring that resources are always available, thereby ensuring high availability and uptime.

An efficient AI infrastructure not only supports productivity, but also helps manage costs. Enterprises can avoid overspending on cloud or hardware resources by optimizing computing resources using distributed systems, containerization, and serverless architectures. These cost efficiencies are vital to scaling GenAI applications without breaking your budget.

Energy efficiency and sustainability

As AI workloads increase, energy consumption and costs increase. Artificial intelligence models, especially GenAI, are energy-intensive, which has led to concerns about the environmental impact of artificial intelligence development. Companies are increasingly recognizing the need for energy-efficient infrastructure to support their AI initiatives without significantly increasing their carbon emissions. Green data centers, renewable energy, and energy-efficient equipment are becoming important components of artificial intelligence infrastructure strategies.

By optimizing energy consumption and investing in sustainable practices, businesses can reduce operating costs while achieving their sustainability goals. As AI adoption accelerates around the world, focusing on energy-efficient infrastructure will be a key differentiator for companies seeking to reconcile innovation with corporate social responsibility and the need to manage costs more carefully.

Therefore, as AI continues to evolve, enterprises need to address current infrastructure challenges and anticipate future changes in AI. These changes must include security, regulatory compliance, and technical and sustainability needs. The convergence of real-time decision making, an expanded work environment and the growing demand for sustainability mean that enterprises 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 artificial intelligence. The question is no longer whether to invest in modernizing cloud infrastructure, but rather how quickly organizations can step up to remain competitive.

Allen Go is the Philippines general manager of Alibaba Cloud Intelligence, a global leader in cloud computing and artificial intelligence providing services to enterprises, developers and government organizations.