It doesn’t take a genius to work out 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 a remarkable annual rate of 59.2%. Yet, as AI capabilities surge, the infrastructure needed to support them is straining under the weight. And this is having an impact on how quickly organizations can benefit from AI.
As McKinsey revealed recently, North American and Asian companies are champing at the bit for AI with 76% of North American companies and 70% Asian companies already starting their AI transformations. However, to maintain their edge, leaders will have to actively pursue transformation, says McKinsey. As of now, less than 10 percent of Asian organizations have found a way to drive value from multiple gen AI use cases. Those that do are likely to build a competitive advantage.
GenAI is certainly driving much of the growth but GenAI also 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 modernization. Transformations are needed to ensure that any investments in AI are maximized.
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. AI infrastructure will see a 14.7% compound annual growth rate (CAGR) through 2028 (according to the IDC research), reflecting earlier investments by cloud service providers. AI hardware and Infrastructure-as-a-Service (IaaS) represent about 24% of overall AI spending, underlining its importance in enabling AI capabilities. So, while GenAI is attracting increasing attention, AI infrastructure spending remains critical for supporting broader AI growth and applications.
For businesses eager to implement AI-driven solutions, investing in a robust, scalable, and secure cloud infrastructure is now critical for success – but what does that AI infrastructure look like? What specifically does AI need and how can businesses transform accordingly?
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.
To meet the growing demands of AI, businesses must adopt cloud-native infrastructure, which includes powerful computing, 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. 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.
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, containerization, 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.
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 datacentres, 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 modernization but how quickly organizations can make the leap to stay competitive.
This article was originally published on Alizila writtern by Alizila Staff.
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