Including Private AI in Your Digital Infrastructure Strategy

Digital transformation has been at the center of business strategy for the past decade. It involves using digital technologies to fundamentally change how a business operates and delivers value. The concept represents a culture shift so significant that the term has evolved from digital transformation to digital business. A digital business doesn’t just go through a one-time transformation; it adopts a continuous innovation mindset that is able to adapt to rapidly changing business requirements and operating environments.

The Impact of AI

Simultaneously, there have been rapid advances in AI. These two worlds collided in 2023 as business leaders were introduced to the power of a very specific AI discipline: generative AI (GenAI). GenAI introduced a new chapter in the digital business journey because of its ability to drastically reduce the time and long-term costs associated with developing solutions across a wide range of use cases associated with automation and intelligence. IDC is projecting that GenAI alone will add nearly $10 trillion to the global GDP over the next 10 years.

Of course, AI capabilities extend beyond just GenAI. According to the IDC Future Enterprise Resiliency & Spending Survey (January 2024), 34% of AI investments will be allocated to predictive AI over the next 18 months. Another 39% of investments will be allocated to interpretive AI use cases. Organizations must develop a strategy that accounts for the various ways AI will become integrated into the business.

To prepare for the impact of AI on a digital business, organizations need to rethink how they architect the underlying platform: the digital infrastructure to support the next generation of data-intensive applications that will be critical to business operations. To help address this, IDC created a digital infrastructure framework (Figure 1).

Figure 1: IDC Digital Infrastructure Framework

Source: IDC, 2024

A key element of the digital infrastructure framework is the need for AI-ready datacenters. Traditional datacenter designs do not account for the power-intensive needs of AI infrastructure based on GPU accelerators and next-generation CPUs.

For many organizations, the costs of retrofitting existing datacenters to meet the needs of AI workloads will be prohibitive, especially in the context of meeting sustainability goals.  This will incentivize enterprises to work with service providers that have the expertise and investments in these capabilities.

In addition to AI-ready datacenters, digital infrastructure also needs the flexibility to deploy in hybrid, multicloud, and edge architectures. This puts an emphasis on high-bandwidth, low-latency interconnections that can support distributed applications and users wherever they may be.

The Need for Private AI

One of the key aspects of designing digital infrastructure is developing a data strategy. It is critical to identify the sources of data needed to enable AI use cases and ensure it is accessible, validated, and secure. This is where privacy concerns enter into consideration. IDC has predicted that by 2025, 70% of enterprises will form strategic ties to cloud providers for GenAI platforms, developer tools, and infrastructure, requiring new corporate controls for data and cost governance. The need for tighter corporate governance is driving interest in private AI.

Private AI aims to balance the business gains from AI with the organization’s practical privacy and compliance needs using technology that focuses on stringent data privacy and control. It is part of a strategy of deploying tailored, secure, and high-performance solutions that can scale to meet the critical aspects of AI operations, including sustainability.

In most cases, private AI is a complement to an organization’s AI activity in the public cloud. For example, the ability to leverage large language models that are trained on publicly available data in the cloud can save time and money. However, proprietary corporate data can be kept on systems where the enterprise has more control over the environment. Cloud adjacent service providers that can offer colocation and interconnection to the public cloud are filling this need.

Private AI in Industry

Private AI puts an emphasis on customization and control of proprietary information. Here are a few situations in an industry context:

• Manufacturing: Imagine a company that manufactures jet engine turbines. Their public AI solution might identify generic anomalies in machine sensor data. Private AI, trained on the company’s specific turbines and historical data, could predict a unique thermal signature that precedes a potential blade failure in their engines.
• Healthcare: A public AI solution might be used to analyze general trends in medical imaging data, assisting radiologists in identifying potential abnormalities like masses in X-rays. A hospital system, however, could leverage private AI trained on its specific patient data and imaging equipment. This private AI could achieve a higher level of accuracy for its own patients.
• Financial Services: Banks could use a public AI solution to analyze generic data for fraud detection. This AI could flag suspicious transactions based on common patterns like large international purchases or unusual spending habits. Private AI could create a more sophisticated level of fraud detection tailored for its customer base with enhanced context settings and personalized risk assessments.

The Path Forward

There is little doubt that AI will remain a critical element of business and technology going forward. As organizations prepare for an AI-enabled future, they have a responsibility to protect proprietary data, reduce regulatory risk, and optimize performance and cost efficiency. This will result in a digital infrastructure strategy that brings together both public and private resources to balance the needs of the business.

Message from Equinix

To get AI-ready digital infrastructure, choosing the right partners is essential. Working with Equinix, customers can deploy in strategic global locations to meet their compliance requirements and maintain control over their data. They can connect with public cloud services on demand via our global network of low-latency cloud on-ramps. And inside our data centers, they can access GPU services in partnership with NVIDIA and advanced cooling capabilities to support dense AI workloads. Learn more about Private AI with Platform Equinix.