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What does data center infrastructure management look like in the age of AI-driven power, density, and heat challenges? In this episode of Data Center Dialogues, host Alison Matte speaks with Himamshu Prasad, SVP of EcoStruxure IT Solutions at Schneider Electric, about how DCIM is evolving from a monitoring tool into a strategic platform. They explore how modern DCIM connects power, cooling, space, and compute into a unified view across the entire lifecycle. From design to operations while enabling smarter decisions through digital twins, predictive analytics, and real-time insights. The discussion highlights why vendor-neutral visibility is critical and what separates leading organizations from those still stuck in reactive monitoring. In this episode: Why AI and high-density computing have made DCIM a strategic imperative How unified visibility works across core, edge, colocation, and hybrid environments How digital twins and CFD modeling prevent costly planning mistakes Why vendor-neutral monitoring is essential in multi-vendor environments How DCIM supports sustainability goals, carbon reporting, and CapEx deferral What DCIM 4.0 means, and why IT/OT convergence is the next frontier Resources: EcoStruxure IT — Data Center Infrastructure Management White Paper: Calculating ROI of DCIM Monitoring for Distributed IT Sites White Paper: How Modern DCIM Supports CIOs in Managing Distributed, AI-Driven IT Environments Tool: DCIM Monitoring Value Calculator for Distributed IT Subscribe to Data Center Dialogues and share this episode with your IT, operations, facilities, and sustainability teams.

Inference was once expected to be the easier side of AI infrastructure. Training needed the specialized clusters, extreme compute, and unusual power and cooling profiles. Inference, by contrast, was expected to fit more comfortably into traditional data center environments. But that assumption is changing. In this episode of Data Center Dialogues, Alison Matte is joined by Wendy Torell, Senior Research Analyst at Schneider Electric, to discuss why generative AI inference is becoming more complex, more power-intensive, and more important to physical infrastructure strategy. They explore the varying classes of inference workloads, the decision factors behind cloud, colocation, and on-premises deployment, and why IT and facilities teams need to plan together from the start.  Key insights Why inference can no longer be treated as “business as usual” for every AI workload. How different classes of inference workloads create very different requirements for rack density, power, cooling, and scalability. Why agentic AI can drive higher compute demand, sustained power draw, and more complex infrastructure needs. How leaders should evaluate cloud, colocation, and on-premises deployment based on latency, security, compliance, business model, and control. Why future-ready infrastructure depends on flexibility, modular design, cooling evolution, high-density power distribution, and software visibility. Four practical steps CIOs and infrastructure teams can take now to prepare for AI inference at scale.  Read next Executive report: Generative AI Inferencing Ramp-up: A CIO’s Guide to Physical Infrastructure Considerations. White paper: 10 Ways to Harness the Energy and Water Efficiencies of Direct Liquid Cooling Visit the Insights Portal:  Preparing for generative AI inferencing: A strategic approach to infrastructure Take a Schneider Electric University class: Generative AI Inferencing Ramp-Up: A CIOs Guide to Physical Infrastructure Considerations

Why is the industry moving away from reactive or calendar-based maintenance toward AI-driven condition-based strategies? In this episode of Data Center Dialogues, Alison Matte is joined by Cristene Gonzalez Wertz, Director and Managing Editor of Data Center Research & Strategy, to dive into a topic that’s transforming how data centers operate: AI-driven condition-based maintenance. Follow the discussion: The big picture The tech behind It Real-world impact Industry trends & future outlook Is AI rewriting the playbook for data center services? If this sparked a new perspective, pass it along. What to do next: Read the executive report - Transforming Data Center Services: AI-Driven Condition-Based Maintenance: https://www.se.com/us/en/download/document/SPD_EB6_EN/ Visit the Insights Portal: https://www.se.com/ww/en/insights/ai-and-technology/artificial-intelligence/transforming-data-center-services-ai-driven-condition-based-maintenance/ -   Hear more: How six AI attributes are rewriting the rules of data center design  https://datacenterdialogues.podbean.com/e/how-six-ai-attributes-are-rewriting-the-rules-of-data-center-design/ 10 Ways to Harness Energy and Water Efficiencies with Direct Liquid Cooling https://datacenterdialogues.podbean.com/e/10-ways-to-harness-energy-and-water-efficiencies-with-direct-liquid-cooling/ AI at Scale | Schneider Electric: https://aiatscale.podbean.com/

Liquid cooling isn’t brand-new, but it’s clearly getting a lot more attention right now. What’s changed in data centers that’s driving the adoption? In this episode of Data Center Dialogues, Alison Matte is joined by Wendy Torell, Senior Research Analyst at Schneider Electric, and Maria Torres Arango, Research Analyst at Schneider Electric, to unveil ten practical ways to harness energy and water efficiencies with direct liquid cooling. Follow the discussion: The main changes in data centers that’s driving the need for liquid cooling. Breaking down the ten ways to improve energy and water use with liquid cooling: seven design factors and three operational choices. Future trends in liquid cooling. With AI workloads growing fast and sustainability targets in sharper focus, are data centers under pressure to rethink how they cool? If this sparked a new perspective, pass it along. What to do next: Read the white papers: 10 Ways to Harness the Energy and Water Efficiencies of Direct Liquid Cooling: https://www.se.com/ww/en/download/document/SPD_WP211_EN/ Direct Liquid Cooling System Challenges in Data Centers: https://www.se.com/ww/en/download/document/SPD_WP210_EN/ Navigating Liquid Cooling Architectures for Data Centers with AI Workloads: https://www.se.com/ww/en/download/document/SPD_WP133_EN/ Read the executive report - Optimizing AI Infrastructure: The Critical Role of Liquid Cooling: https://www.se.com/us/en/download/document/SPD_EB3_EN/ Visit the Insights Portal: https://www.se.com/ww/en/insights/ai-and-technology/artificial-intelligence/air-isnt-enough-liquid-cooling-revolution-data-centers/ Take a Schneider Electric University class: Liquid Cooling: Essential Architectures for AI-Driven Data Centers: https://schneider-electric.csod.com/ui/lms-learning-details/app/course/e18390cc-fb4f-4499-ac54-84363a7c37af Direct Liquid Cooling System Challenges in Data Centers: https://schneider-electric.csod.com/ui/lms-learning-details/app/course/425a93cb-1986-4dc5-9c67-c8b893e5d58f -   Hear more: How six AI attributes are rewriting the rules of data center design  https://datacenterdialogues.podbean.com/e/how-six-ai-attributes-are-rewriting-the-rules-of-data-center-design/ AI at Scale | Schneider Electric: https://aiatscale.podbean.com/

Why is AI such a disruptive force for data center design compared to traditional workloads?  In this episode of Data Center Dialogues, Alison Matte is joined by Victor Avelar, Senior Research Analyst at Schneider Electric, to unpack how six AI attributes are changing data center design: from power density to cooling and sustainability. Follow the discussion: Why AI changes the game for power, cooling, and infrastructure planning The six attributes shaping AI-ready data center design Cooling strategies for higher heat density (and what’s changing in design) How operators balance performance with sustainability goals How to future-proof infrastructure as AI workloads evolve Is AI reshaping everything from rack design to energy strategy? If this sparked a new perspective, pass it along. What to do next: Download the white paper: https://www.se.com/ww/en/download/document/SPD_WP110_EN/ Insights Portal: https://www.se.com/ww/en/insights/ai-and-technology/artificial-intelligence/how-six-ai-attributes-are-changing-the-rules-of-data-center-design/ Schneider Electric University: https://schneider-electric.csod.com/ui/lms-learning-details/app/course/c01fca52-2d7a-437d-bfd1-81154a8cc105 - Hear more: AI at Scale | Schneider Electric: https://aiatscale.podbean.com/