AI is moving from isolated pilots to enterprise-wide initiatives, but infrastructure strategies often lag. Many organizations face compute constraints, fragmented tooling, and unclear deployment or sourcing approaches. As AI workloads grow in scale and complexity, the need for purpose-built, scalable infrastructure aligned with business goals becomes essential. This report introduces AIMPACT, Everest Group’s framework for structured AI infrastructure planning. It offers a clear lens on how enterprises can profile AI workloads, assess readiness, and make informed decisions on deployment models, sourcing strategies, and stack design. The report also explores the broader shifts influencing how infrastructure is designed, operationalized, and governed in an AI-first world. This report provides a practical roadmap to move from fragmented pilots to scalable execution. CIOs, infrastructure leaders, AI teams, and service partners can use this report to bring structure to decision-making, align technology investments with business goals, and build infrastructure strategies that are grounded, adaptive, and future-ready.

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.

In the rapidly evolving life sciences landscape, where the pursuit of innovative therapies and life-saving drugs never ceases, science and technology are increasingly playing a vital role. High-performance Computing (HPC) stands at the forefront of this technology revolution, empowering pharmaceutical and medical device enterprises, as well as research and academia, to unravel the complexities of biology, medicine, and multi-omics like never before. HPC is no longer confined to successful pilot phases but is transitioning to wider adoption, addressing essential business needs across industries, including life sciences. The pharmaceutical industry, dealing with vast volumes of complex and heterogenous data, is an ideal sector for harnessing the benefits of HPC – particularly around intricate biological interactions, complex data analysis, and sophisticated simulations. In this viewpoint, we study HPC’s high-potential use cases in life sciences and how pharmaceutical companies can derive success from their HPC initiatives. Scope Industry: life sciences Geography: global Contents In this report, we explore: HPC’s evolution, including the examination of an HPC-enabled system and the catalysts driving its adoption across industries The current supply ecosystem, highlighting prominent pharmaceutical and medical device HPC use cases The drivers and barriers to HPC adoption and important enterprise considerations before transitioning to HPC A step-based adoption roadmap for life sciences enterprises and a peek into HPC’s future in life sciences Membership(s) Life Sciences Information Technology Outsourcing Excellence