Artificial intelligence (AI) is rapidly transforming the landscape of enterprise computing and reshaping the way businesses operate. From automating processes to customizing user experiences and analyzing massive data sets, AI has become a game-changing technology. Its impact is so profound that companies are increasingly identifying themselves as ‘AI-first’ organizations, with AI ingrained in every aspect of their infrastructure and strategy.
As AI gains momentum, businesses that fail to adopt AI-driven solutions face the risk of being left behind by competitors harnessing its potential to drive growth, efficiency, and leadership in their respective markets.
The Rise of AI-First Companies
In 2016, Google made waves when it announced a mobile-first strategy, acknowledging the dominance of mobile platforms. Today, the focus has shifted, with many organizations positioning themselves as AI-first, recognizing that their entire IT infrastructure must be designed to support AI’s growing demands.
Falling behind in adopting AI comes with significant risks, particularly as companies utilizing AI to innovate and streamline processes are surging ahead. The transition to an AI-centric strategy has its challenges, though, particularly around infrastructure requirements. AI workloads demand substantial processing power and storage, straining traditional enterprise computing systems.
AI’s Potential and the Challenges Ahead
For businesses, AI presents numerous opportunities. From enhancing productivity through automation to driving revenue and market share, AI is revolutionizing industries. However, these benefits come with their own set of challenges. AI workloads are highly resource-intensive, often overwhelming existing enterprise infrastructure.
AI is also expanding beyond centralized data centers, with deployments now reaching user devices like desktops, phones, and tablets. Moreover, AI on edge and endpoint devices is enabling faster data processing, reducing latency, and increasing reliability. This shift necessitates new discussions about infrastructure costs and computing power. IT teams must decide whether their AI solutions perform better in on-premises data centers, in the cloud, or at the edge.
Building an AI-Ready Infrastructure
To succeed in an AI-first world, businesses must overcome the significant hurdle of building the necessary infrastructure. While few organizations have the resources to construct new data centers for AI, adapting and modernizing existing ones is critical.
Cloud service providers (CSPs) offer a path forward. Early cloud solutions were designed for basic business workloads, but today’s CSPs are more sophisticated, offering AI-specific cloud solutions and hybrid setups combining on-premises IT with cloud services.
AI is complex, and no single approach fits all businesses. Companies must collaborate with strategic technology partners to craft AI applications tailored to their specific needs. A key role of these partners is guiding organizations through the modernization of their data centers.
Modernizing Data Centers for AI
A major part of AI adoption lies in upgrading data centers to handle AI workloads efficiently. New servers and processors, specifically designed for AI, can reduce the hardware footprint by delivering more computing power with fewer processors and servers. This results in a leaner, more energy-efficient data center setup, reducing both space and total cost of ownership (TCO).
Strategic partners also help businesses adopt graphics processing unit (GPU) platforms, essential for AI success. GPUs are especially critical for training AI models and real-time processing. However, not all AI workloads require GPUs, and businesses must identify where GPU acceleration is truly necessary. For example, AI inference workloads often run efficiently on CPU-only infrastructure when model sizes are smaller.
The Role of Networking in AI Applications
Effective networking is another vital component for delivering AI solutions at scale. From rack to campus levels, AI applications demand high-performance networking to ensure smooth operations. Technology partners provide advice on networking options, helping businesses strike the right balance between proprietary and industry-standard solutions.
Choosing the Right AI Technology Partner
Selecting a strategic partner for AI deployment is crucial for businesses aiming to adopt AI-first infrastructures. The ideal partner should have expertise in both cloud and on-premises data center solutions, ensuring comprehensive support across the IT landscape.
One example is AMD, whose EPYC™ processors help businesses consolidate their data center workloads, running AI applications on fewer servers without compromising performance. This enables organizations to free up space and power in their data centers, facilitating AI deployment without major infrastructure overhauls.
The Time to Adopt AI is Now
The demand for AI-driven solutions is increasing, putting pressure on aging infrastructures. To stay competitive, enterprises must embrace AI-first strategies and modernize their IT environments. Leveraging new data center technologies, companies can accelerate AI adoption while minimizing risk and investment costs.
The AI transformation is no longer optional. As more organizations adopt AI-first mindsets, the need to build infrastructure capable of supporting AI across data centers, user devices, and endpoints has never been more pressing. Enterprises that act now stand to gain a significant competitive advantage, while those that hesitate risk falling behind in an AI-driven world.
