Enterprise AI is still in its infancy, with emerging use cases poised to drive fundamental shifts across industries that are comparable to the transformative effects of the internet, mobile, and cloud technologies.
The internet changed everything about how business was done. AI is going to change everything about how business is done. It’s just that fundamental. What we’re seeing is a similar pattern to the dot-com era, where there are companies trying to capitalize on the early wins and others that are in it for the long term. Those with a long-term view are taking a more structured and strategic approach to build a solid foundation.
I’ve been skeptical about many past technology waves—blockchain, far-edge computing, 5G—largely because their practical applications didn’t seem compelling enough at the time. But AI is different. This is a technology I truly believe in, and the potential for it to drive profound economic impact and solve real-world challenges is what excites me most.
The acceleration of AI adoption will require unprecedented levels of computational power, data storage, and networking. The criticality of purpose-built, sustainable data center infrastructure cannot be overstated. These facilities are essential for supporting the massive workloads that drive AI applications, and will help pave the way for a smarter, more connected future.
Accelerating the evolution of AI, from training to inference
Until recently, developing AI training models was the primary focus for most of the infrastructure built in the first wave of AI. But that’s changing. Now that models are more mature, vertical-specific, and trusted, we expect to see enterprises spending more time on AI inference in 2025. These sophisticated models are being applied to use cases that go beyond chatbots and funny photos to tackle complex problems and create tangible value across nearly all industries.
AI is changing how enterprises operate, from developing innovative products and services to automating processes to streamlining workflows that fuel productivity gains. And it’s shaping data center infrastructure trends as we head into 2025.
To train AI models and get the most out of AI inference, enterprises will need to:
- Consider cloud rebalancing for best-fit workload placement and data storage.
- Comply with data sovereignty regulations.
- Implement observability across the threat landscape
- Deploy infrastructure at AI-ready data centers where they have access to sufficient power.
Trend 1: Cloud rebalancing optimizes workload and storage distribution
During the past two years, many enterprises have spent time experimenting with generative AI and testing proofs of concept in the public cloud. They’ve learned a lot, including the need for cloud rebalancing, to redirect some of their infrastructure to on-premises or colocation data centers, just like they saw with other workloads before generative AI. An IDC report from June 2024 found that about 80% of survey respondents “expected to see some level of repatriation of compute and storage resources in the next 12 months.” [1]
Cloud rebalancing (also known as cloud repatriation) is the strategy of placing critical workloads, including AI development and storage, where they can perform with the best price-performance. Other reasons for strategic workload and storage placement include the ability to:
- Use your proprietary data to retrain AI models privately.
- Control and protect your distributed data despite rapid growth and increasing complexity.
- Know what data you own, the source for acquired datasets, and where it’s all stored.
- Improve data locality to comply with data sovereignty and privacy regulations.
- Transfer data instantly and seamlessly from vendor-neutral storage to workloads running across multiple clouds.
- Maintain an authoritative data core that allows you to move specific data to workloads running in a public cloud or at the edge.
I expect we’ll see enterprises focus on developing and implementing cloud rebalancing strategies to support their AI-ready data strategies in 2025.
Trend 2: Formalizing data governance processes strengthens data management
Understanding an enterprise’s entire data estate is more important than ever because of AI. To train AI models and create valuable and potentially life-changing products, you must be able to feed the models useful data. Knowing what data you own, the sources of any acquired data, and how your data is structured and set up inside your systems can reveal the privacy or regulatory risk associated with that data. Once you have that understanding, having the necessary tools to provide a robust perspective of your entire data estate will be increasingly important.
Data governance supports all aspects of data management, including data privacy and compliance with data sovereignty regulations. Yet, many enterprises have not gone through the process of establishing their data governance policies. In McKinsey’s findings from their State of AI survey, 70% of respondents said they have experienced difficulties with data, including defining processes for data governance, developing the ability to integrate data into AI models quickly, and an insufficient amount of training data.[2] These findings highlight the essential role that data plays in capturing value from AI.
Looking ahead to the second half of 2025, enterprises will need to increasingly formalize their data governance policies and processes, making them an essential part of their AI strategies. Doing so will help them ensure data privacy, avoid unexpected regulatory penalties, and drive the highest quality results out of their AI investments.
Trend 3: Observability must span the entire threat landscape
Understanding how to secure assets and networks in the best way possible is more important than ever. The risk profile for attacks is increasingly high as enterprises continue to expand the number of companies they interface with, such as cloud and network service providers and SaaS companies. Doing so exponentially increases the complexity and size of their threat landscape and vectors.
Observability is more critical than ever to understand what’s happening across the landscape. Enterprises must pair command and control of their technology and infrastructure estate with observability tools that provide the necessary coverage for their entire threat landscape. They’ll need to see attacks earlier and use their data and workflows to predict when and where the greatest exposure will be.
In 2025, rallying the industry to work together on deploying best practices and tools will be crucial for strengthening protection against these attacks.
Trend 4: Increasing power capacity for AI requires a broad scope of stakeholders
AI training requires significantly more energy to produce new data. For example, a ChatGPT response to a prompt requires 10x the electricity of a Google search.[3] This power crunch requires stakeholders across the industry and government to be open-minded about using alternative power sources and reducing impact on the grid by:
- Integrating new, more efficient energy sources such as hydrogen and nuclear.
- Expediting power infrastructure and data center builds in communities with access to land and power that want the investment and job creation benefits that development will bring.
The growing demand is creating urgency to generate and transmit energy in new ways. Solving for energy shortfalls and harnessing the full potential of AI will require collaboration across the industry, engagement with policymakers, and active partnerships with communities. The industry also needs strong public policy support to ensure a sustainable and equitable path forward to grid modernization and the development of additional utility sources.
We’re advocating for governments to create policies that address this power capacity challenge with a focus on utility and transmission development and making direct investments. Otherwise, they risk falling behind in the digital economy and the future of AI.
I believe our separate and joint efforts to address power capacity in 2025 will shape a landscape of opportunity, innovation, and security for future generations.
AI growth relies on global data center infrastructure
Expanding the availability of high-performing data center infrastructure that supports the growth of AI will continue to be a priority for Equinix. It’s more than a trend, as we continue to develop and equip data centers with power and networking that provide incredible contributions to the economy. By 2027, $8.75 trillion of the world’s economy will be dependent on data centers.
As you start or continue to integrate AI into your business, focusing on these four trends will help set your course for 2025 and enable even more innovation and business growth.
Visit here to learn more about how Equinix is developing high-performing data center infrastructure.
Read More from This Article: How AI is influencing data center trends in 2025
Source: News