With the accelerating evolution of AI and its voracious appetite for data, connecting AI workloads and data is more critical than ever. Doing so privately and securely is a given, as is the need for high performance and low latency.
As businesses expand their operations, their sources and volume of data increase in parallel. They rely on an increasing number of service providers and industry partners to help fast-track their growth, and they must be able to connect with those partners and providers on demand. They need to consider several factors as they plan their AI-ready network infrastructure, such as:
- Best-fit clouds for running specific applications or workloads
- Networks with the fastest and most reliable connectivity
- Data storage in proximity to clouds
- Access to cloud on-ramps for the major providers
- Physical and virtual accessibility
Finally, there’s the decision about where to deploy their AI infrastructure. They’ll likely need different types of data centers for different AI workloads, and those distributed data centers need to be connected to one another.
Secure, flexible connectivity is crucial to bringing together the data, workloads, multicloud environments, providers, partners and infrastructure that businesses need to future-proof their AI-driven data strategies. Leveraging connectivity to multiple cloud providers enables organizations to adapt and scale quickly to changing demands and optimize costs.
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Industry experts share their thoughts on how enterprises can future-proof their data strategies to be AI-ready
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To continue the conversation around the need for flexible, secure connectivity, we asked three industry experts to share their thoughts on why it’s crucial for AI success. Here’s what they had to say:
Transforming data insight requires connecting data and AI workloads
“Transforming data insight into business results requires bringing together data from multiple sources and AI workloads from different locations, then using the right tools and making choices that align with the desired business and technical outcomes.”
— Vanessa Santos, Senior Manager, The Equinix Research Group
Hear more from Vanessa in this video:
Cloud connectivity architecture forms a foundational element
“Forward-thinking IT executives recognize the complex interconnection of the overall cloud ecosystem. Securing every technical interaction is a top priority, especially with the rise of AI workloads. Cloud connectivity architecture forms a foundational element of this footprint, and as multicloud adoption grows, organizations increasingly demand seamless connections to major providers. However, ease of connection alone isn’t enough—those connections must also be private, secure, and high-speed. This ensures enterprises can safely and efficiently manage sensitive data while supporting the performance needed for AI-driven operations.”
— Jo Peterson, VP Cloud & Security Services, Clarify360
Prioritize meeting the data where it is for faster time to value
“There’s more to AI ROI than GPUs. The key to making AI real and future-proof is inference. Systems that allow for adaptation and prioritize meeting the data where it is — in the cloud(s), on prem, or at the edge will have a shorter time to value and longer overall return. Bringing AI to clean data, safely and securely, is the key. Doing that with a strong foundation of governance is also imperative. The regulation around AI is still wet clay. Choosing the most secure and compliant systems today will save companies millions as they scale.”
— Savannah Peterson, Host & Analyst, theCUBE Research
For more insights on how to future-proof your AI-driven data strategy, read the Equinix Indicator.
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