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THE EQUINIX INDICATOR - VOL 2
How Can You Future-Proof Your AI-Driven Data Strategy?
Welcome to the second volume of The Equinix Indicator. A place for conversation around topics enterprises are prioritizing. In this volume, industry experts share their thoughts on how enterprises can future-proof their data strategies to be AI-ready.
Jon Lin
Chief Business Officer, Equinix
Industry Voices
Tiffany Osias
VP - Data Center Services, Equinix
Karim Beguir
CEO & Founder, Instadeep
Chris Campbell
Senior Director - AI Solutions, World Wide Technology
Vanessa Santos
Senior Manager, Equinix Research Group
Savannah Peterson
Host & Analyst, theCUBE Research
Aaron Delp
Director - AI Technical Solutions, Equinix
Tony Paikeday
Senior Director - AI Systems, NVIDIA
Zeus Kerravala
Principal Analyst, ZK Research
Abby Kearns
CTO, Alembic
Karim Beguir
CEO & Founder, Instadeep
Jo Peterson
VP Cloud & Security, Clarify360
Dave McCarthy
VP, Cloud and Edge Infrastructure Services, IDC
Infrastructure
Infrastructure is the foundation that supports and enables your AI-driven data strategy. To future-proof this strategy, enterprises need an infrastructure designed for scalability, resilience, and flexibility, ensuring it can handle the demands of evolving AI workloads.
Tiffany Osias
VP, Data Center Services, Equinix
Place infrastructure strategically to support hyperscale, private, and edge AI workloads globally
Chris Campbell
Senior Director - AI Solutions, World Wide Technology
A robust and well-planned infrastructure is essential for AI readiness
“Infrastructure plays a crucial role in enabling enterprises to be AI-ready by providing the necessary foundation for performance, scalability, and security. The challenge is that traditional data centers are not equipped to manage the requirements of AI. This includes using high performance architecture to manage data demands, meeting power and cooling requirements, and having access to proven facilities infrastructure that allow them to move quickly. A robust and well-planned infrastructure is essential for AI readiness and having the right infrastructure allows companies to manage their resources and capabilities without limitations.”
Dave McCarthy
VP, Cloud and Edge Infrastructure Services, IDC
AI-ready data centers are essential
“To prepare for AI’s impact on digital business, organizations must rethink their digital infrastructure to support next-generation data-intensive applications. AI-ready datacenters are essential, as traditional designs lack the capacity to handle the power-intensive needs of GPU accelerators and advanced CPUs. Flexibility in deploying hybrid, multicloud, and edge architectures is also critical, emphasizing high-bandwidth, low-latency interconnections to support distributed applications and users effectively.”
Karim Beguir
Co-Founder and CEO, InstaDeep
Our supercomputing cluster is more than infrastructure — it’s a catalyst for accelerating the shift to an AI-first world
“Our supercomputing cluster is more than infrastructure — it’s a catalyst for accelerating the shift to an AI-first world. By taking full control of our computing power, we can innovate faster, optimise resources, and push the boundaries of AI’s potential. This investment isn’t just about solving today’s challenges; it’s about driving tomorrow’s breakthroughs. We’ve built a scalable, flexible, and solid foundation to lead in a rapidly evolving landscape.”
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Flexibility
Leveraging cloud connectivity and multicloud environments enables organizations to adapt and scale quickly to changing demands and optimize costs.
Vanessa Santos
Senior Manager, The Equinix Research Group
Jo Peterson
VP Cloud & Security Services, Clarify360
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.”
Savannah Peterson
Host & Analyst. theCUBE Research
The key to making AI real and future-proof is inference
“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.”
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Governance
Governance ensures trust and accountability in your AI strategy. By implementing robust policies for data access, usage, and compliance, organizations can maintain transparency and safeguard against risks.
Aaron Delp
Director - AI Technical Solutions, Equinix
Zeus Kerravala
CEO & Principal Analyst, ZK Research
Establish clear data governance policies that adapt to evolving regulations
“To future-proof your AI data strategy regarding governance, ensure robust privacy and compliance frameworks are integrated into your systems. Establish clear data governance policies that adapt to evolving regulations, such as GDPR or CCPA. Prioritize data quality and transparency, incorporating explainability for AI models and decision-making processes. Continuous monitoring and auditing are employed to detect and address potential risks and biases. Regarding data location for AI workloads, consider adopting a flexible residency strategy that complies with local data sovereignty laws. Utilizing cloud services with region-specific data centers can help maintain compliance, optimizing data storage and processing locations based on regulatory requirements and operational needs.”
Abby Kearns
CTO, Alembic
In an evolving regulatory environment organizations should prioritize implementing transparency and control over their data
“AI’s ability to extract deep insights from data increases the need for strict data residency and data governance policies. In an evolving regulatory environment organizations should prioritize implementing transparency and control over their data. At Alembic, we maintain a rigorous US-based data residency, and do not store any personally identifiable information. This approach streamlines compliance and ensures the proper safeguards are in place.”
Tony Paikeday
Senior Director, AI Systems, NVIDIA
Businesses need a simpler, faster path to accessing AI infrastructure
“Enterprises committed to AI need their own AI “factories” to scale application development leveraging tools, accelerated computing, and facilities optimized for these new demands. Businesses therefore need a simpler, faster path to accessing AI infrastructure that’s data-proximal, securely hosted, and maintained within facilities that are multicloud interconnected, while ensuring sovereignty of their data and intellectual property.”
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