The rapid rise of generative AI has led to a global infrastructure transformation, accelerating demand for high-performance computing and next-generation data centers. As enterprises and governments invest in sovereign compute capabilities, India is emerging as a key player due to its expanding digital economy, progressive policy environment, and growing hyperscaler activity. This report explores how India’s data center ecosystem is evolving, from concentrated tier-1 city hubs to a more distributed, regionally balanced model aligned with AI demands. However, not all regions are equally equipped to support dense, latency-sensitive AI workloads. Power availability, network infrastructure, land access, and environmental resilience vary widely across cities. To evaluate these differences, Everest Group introduces the CALIBER-DC framework, a comprehensive model that assesses regional data center readiness across key dimensions. This Viewpoint offers strategic insights for infrastructure developers, policymakers, and enterprises seeking to scale AI infrastructure in India.

Generative AI has moved from an experimental tool to a core enterprise engine, unlocking business value across the entire IT stack. Yet every new parameter-rich model brings a heavy sustainability price tag: soaring electricity draw, intensified cooling loads, and water usage that stretches local resources. The very clouds that promise digital transformation risk casting a shadow over global net-zero goals. In this Viewpoint, Everest Group unpacks the contradiction. We trace how exponential AI workloads are stress-testing hyperscalers’ original green growth pledges, such as 100 percent renewable energy, water-positive campuses, circular hardware, placing these commitments at a pivotal phase of execution and accountability. Beyond this, this Viewpoint focuses on future opportunities. Hyperscalers, based on their scale and influence, hold immense power to reset ambitions and raise the bar for sustainable growth. We examine how hyperscalers are already investing in next-generation technologies that can improve energy and resource efficiency, as well as how they can do more in the future. Ultimately, this Viewpoint offers a forward-thinking playbook for technology providers and enterprises navigating the intersection of AI and sustainability. By turning intent into impact, hyperscalers, their ecosystems, and enterprises can work together to drive responsible innovation that not only meets the moment but defines the next era of cloud leadership.

In April 2025, IBM unveiled the latest evolution of its mainframe portfolio with the introduction of IBM Z17, advancing its long-standing leadership in mainframes. IBM has long been a cornerstone in the evolution of enterprise computing, with its mainframe systems being vital in supporting mission-critical operations across industries. Known for their reliability, security, and performance, IBM has consistently adapted its mainframes to meet changing business needs. IBM Z17 focuses on delivering alignment with enterprise hybrid cloud and sustainability goals. It stands out with features such as quantum-safe encryption through Crypto Express 8S, Watsonx Assistant for Z, Operations Unite, and AI-enabled processing at the core. It introduces enhanced security features and hybrid cloud readiness, offering enterprises a flexible, future-ready solution to modernize while empowering mission-critical workloads in a rapidly transforming digital landscape. This evolution in the Z series reinforces IBM’s commitment to autonomous IT, streamlining operations, enhancing user experiences, while addressing long-standing challenges such as talent shortages and legacy system constraints.

As enterprise AI adoption accelerates, organizations are ramping up investments in infrastructure capable of supporting a broad spectrum of AI workloads, from training to inference, across data center, cloud, and edge environments. AI is fueling demand for high-performance, low-latency network fabrics, streamlined operations, open, and secure architectures, and infrastructure that is energy efficient and sustainable. This report comprehensively analyzes Cisco’s networking infrastructure for AI announcements made at Cisco Live 2025. It examines Cisco’s current strategic positioning and highlights key innovations across switches, routers, and access points designed to enable AI-native infrastructure. The report also evaluates how Cisco is addressing key enterprise priorities such as operational simplification, architectural openness, extensibility, and sustainability. The report is intended to help infrastructure and technology leaders, architects, and ecosystem partners in evaluating Cisco’s portfolio strategy and its alignment with the evolving demands of future-ready, AI-powered enterprise environments.

As AI models become more complex and resource-intensive, enterprises must modernize their infrastructure to support high-performance, scalable, cost-effective workloads. Core challenges in modernization include integrating multimodal data, enabling autonomous agents, and optimizing the AI stack across diverse environments. Enterprises also aim to deploy AI in hybrid and edge settings, requiring flexibility, low latency, and data sovereignty. At Google Cloud Next 2025, Google announced AI infrastructure upgrades, including Ironwood TPUs for inference at scale, AI Hypercomputer improvements, expanded VM families, and re-architected networking with multi-shard architecture. Google also emphasized hybrid and distributed AI deployment with support for air-gapped environments and on-premises inference using NVIDIA Blackwell systems. These updates show Google’s intent to deliver an integrated AI stack, combining custom hardware, orchestration tools, and productivity platforms. However, these offerings also raise questions around interoperability with third-party tools, operational complexity, and cost transparency. In this report, we analyze Google’s AI infrastructure announcements at Google Cloud Next 2025, assessing their alignment with enterprise needs across performance, scalability, and deployment. The report covers key enterprise priorities, Google’s positioning, detailed product reviews, and Everest Group’s perspective on strengths and gaps of announced AI infrastructure-related products, offering a clear view of Google’s AI infrastructure maturity. Scope All industries and geographies Contents In this report, we examine: Key AI infrastructure-related products launched at Google Cloud Next 2025 Google’s current positioning and alignment in the AI infrastructure market AI infrastructure-related products’ specifications, benefits, and challenges

Pulses deliver forward-looking insights into the evolution and impact of science, technology, and trends on global transformation. By engaging with our Pulses, you will gain a deeper understanding of each topic's significance, the key innovators driving change, and the future direction we anticipate. These insights are designed to stimulate discussions within your teams, challenging you to consider your preparedness for impacts on new product development, innovation, vision, strategy, R&D, and beyond. Membership(s) Advanced SciTech

The rise in Artificial Intelligence (AI) and gen AI adoption has cast the spotlight on technology infrastructure’s demand to support AI workloads. AI workloads can put considerable strain on IT infrastructure, placing huge demands on data, storage, and network infrastructure during model training and inferencing. Enterprises are increasingly recognizing the need for dedicated infrastructure, which is vital for AI applications’ optimal functioning and forms AI deployment’s backbone. As AI’s demand grows, they are strongly emphasizing advanced compute hardware, high storage capacity, enhanced connectivity, and robust cloud platforms and data centers. In this report, we provide the global AI infrastructure market’s outlook along with enterprise concerns and challenges, adoption framework, recent developments, and implications for enterprises and providers. Additionally, the report provides an enterprise playbook for AI infrastructure adoption and the top 20 AI infrastructure providers’ overview. Scope All industries and geographies AI infrastructure Contents In this report, we provide: Global AI infrastructure market’s overview with demand drivers, enterprise concerns, and adoption framework The top 20 AI infrastructure providers’ overview Membership(s) Cloud and Infrastructure Services Sourcing and Vendor Management