The “training wheels” era of AI is officially over. Over the past three years, business leaders have developed and tested AI solutions, transforming AI into an integral part of the global economy’s daily operations. Business leaders, accordingly, are no longer asking how to use AI in their operations. Instead, they’re asking how to make the most of the investments they’ve made already.

In that question, we see the major projected trendline for AI in 2025: less emphasis on generating novel use cases and more emphasis on seeing a robust return on pre-existing AI expenditures. It’s the difference between building a car and driving it. We are out of the era of lofty AI abstractions; the focus has now shifted to making this novel technology work.

To that end, we can expect a renewed emphasis on optimizing and streamlining the vast infrastructure that enables AI integration. While business leaders recognize comprehensive AI integration is a prerequisite for marketplace success, they must now learn the importance of AI infrastructure.

Optimizing the Tech Stack

Servers are a key component of this infrastructure, and in a very short period of time, AI has completely overhauled the server market. According to analyst firm IDC, AI servers made up 23% of the entire server market share in 2023, and that number is expected to grow substantially in the coming years.

In this arena, business leaders are faced with the question of finding the servers that can bring their expanding AI visions to life. This can be challenging, as AI workloads are highly intensive. 

Despite this, many businesses are still using servers that aren’t optimized for AI. For instance, high-performance GPU-equipped servers are widely recognized as the gold standard for AI functionality, but some businesses persist in using CPUs.

The benefits of GPU servers in an AI context are undeniable. Though initially used primarily for graphics rendering tasks, in the AI era GPUs have become foundational to such AI functionalities as machine learning, data analysis, simulations, and more. 

Of course, for business leaders it is not simply a matter of deploying GPU servers, but of deploying the right GPU servers. It’s important to recognize that AI encompasses both software and hardware aspects. GPU servers need to be capable of running AI frameworks and models while also integrating with platforms to enable end-to-end AI processing. Intelligently deploying AI will mean taking a deep look at internal AI objectives and making informed decisions about the tools needed to achieve them. 

For most businesses, flexibility will be key here. Third-party server contracts are an increasingly major line item, and it is imperative that businesses avoid getting locked in to paying more than is strictly required for their needs. Crucially, these same considerations apply to every other component of the AI tech stack, from high-performance accelerators to storage.

Data Centers and Sustainability

Equally as important to AI budgeting and planning are the data centers that host these servers. In 2025, we can expect one word more than any other to attach itself to this space: sustainability.

AI data centers require massive amounts of power. According to a recent report from Goldman Sachs, the overall AI revolution is poised to increase data center power demand by a staggering 160%. As that report points out, a single ChatGPT query requires close to ten times more watt-hours of electricity than a single Google result.

However, powering the servers in these data centers is just one-half of the sustainability equation: there is also the matter of cooling them. According to McKinsey and Company, cooling accounts for close to 40% of the total energy consumed by data centers today. These two things are, of course, interlinked. As more thermal energy is generated by data center equipment through AI deployments, more power will need to be directed toward cooling efforts.

Legislators concerned about sustainability have already taken note, leading to a wave of global regulations designed to enhance the sustainability and resiliency of data centers. 

While the onus here is on the data center themselves, who will need to make substantial investments in renewable energy, business leaders are not exempt from these considerations. ESG continues to loom large in the business world, with investors increasingly factoring ESG compliance into business decisions. It is also the case that the cutting-edge natural cooling technology deployed by sustainable data centers allows for more cost-effective services. The combination of reduced energy consumption and significantly lower cooling costs makes a strong case for sustainable data centers as the way forward for companies investing heavily in AI. 

Three years into the AI revolution, we know what this technology is capable of. Already, AI is not only facilitating, but in many cases independently designing go-to-market strategies, and its capacities for reasoning and self-correction have expanded dramatically in just the last twelve months. As business leaders have come to realize, the bright new world will remain inaccessible without deep strategizing around the kinds of tech stacks that truly enhance these AI capabilities. The time has come to think about not just how to use AI, but the tools and systems that will fuel its next phase of growth.