The importance of hybrid IT in a successful AI strategy

Historically, people interacted with their infrastructure and data in a LAN or a WAN inside an office building. Today, we’re seeing the distribution of the workforce and individuals utilizing dynamic 5G access to services and applications that may or may not be cloud-based worldwide. Currently, 50% of all user interfaces are being powered by AI. Additionally, 90% of enterprises will have an AI architecture and infrastructure deployed by the end of the year.

There’s been tremendous growth in AI utilization, especially in robotics, healthcare, internet services, finance, media, and autonomous vehicles. These are just a few near-term examples of how AI, edge computing and distributed architecture drive our day-to-day lives. Because of the exponential growth in AI and new data sources, it’s essential to make sure your AI strategy is ready.

There are several functional challenges to consider when building a roadmap to an AI and an edge strategy. Here are four key components to keep in mind.

• Design complexity: Ensure predictable performance that scales quickly
• Connectivity: AI services are becoming more real-time and need broad network capabilities
• Data center readiness: Not all data centers are optimized for AI workloads
• Escalating costs: Becoming “AI-ready” can be a challenge for CapEx-constrained organizations

It’s important to think about where and how you’re interacting with infrastructure, where and how you need to make decisions, and most importantly, where the data you’re utilizing is being created and how you’re interacting with it. As you start to think about those concepts, there are some key components that you should focus on. 

First and foremost is colocation and the data center itself. Historically, companies have had their enterprise data center in an office park, campus, or physical building. As the interconnection world has evolved, more companies are putting their colocation infrastructure inside a highly interconnected third-party data center. It’s essential to understand the connectivity models that are associated with those data centers, as well as the geographic distribution. 

The second area of focus is around cloud IT solutions. Whether people realize it or not, every IT service uses cloud infrastructure, whether it’s a public cloud, like Amazon, Google, Microsoft, and Oracle, or a private cloud that utilizes compute and storage inside a private infrastructure or by a third party. Much of a corporate IT infrastructure resides in the cloud, and you’d be surprised how much of your personal life also resides in the cloud. That gives you context when deciding what an AI and data gravity architecture strategy looks like long-term.

The third area to focus on is data protection. Security is critical in today’s day and age. With the increased number of ransomware attacks and access to personal identification information, you must have a security strategy limiting your overall security exposure vector. 

There are two additional capabilities you need in order to execute a successful AI strategy. One is the managed services component. As you look at outsourcing capabilities and trusted advisors that can help you build an AI and edge strategy, ensure it is with experts who eat, live, and breathe this stuff every day. Having a partner with a very deep professional services capability is also essential. These are teams that have compartmentalized focus areas that can help with the AI construct and how data will be interacted with. They can also help with the configuration of the public and private cloud capabilities. Think of it as a virtual CTO/CIO function that allows you to grow and build your strategy as the overall roadmap continues to evolve.

Finally, the geographic distribution of your data is crucial. It’s best to address this not with one centralized data center location but with a data center platform that has access to many different markets and a high-capacity, interconnected backbone between its facilities. Data gravity created in an ecosystem format and the ability to transfer that data between locations and compute nodes is essential for a successful data center platform and AI strategy.