AI View

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Meet the Experts

Photo of Peter Rutten
Peter Rutten

Research Vice-President, Performance Intensive Computing, Worldwide Infrastructure Research

Photo of Ryan Caskey
Ryan Caskey

Research Manager, BuyerView Operations, Worldwide Infrastructure Research



IDC's AI View provides an overview of artificial intelligence (AI) infrastructure adoption and trends among IT buyers via annual worldwide primary research surveys. It looks at current and future AI infrastructure investments and adoption rates, workloads, and economics by IT customers and service providers. This product delivers key insights into challenges, considerations, and opportunities in adopting AI systems platforms and technologies. This product helps IT vendors of on-premises, edge, and cloud-based AI infrastructure develop and market their AI infrastructure offerings and provides insights on the overall impact on infrastructure decisions, workloads, personas, solution selling, open source versus commercial software markets, deployment locations, and so forth. It seeks to gain insights on the impact of machine and deep learning workflows, AI-infused applications, and analytics technologies on the infrastructure software and hardware markets in a quantitative manner at a worldwide level. Additional focus for 2023 will be on the increasing use of storage solutions designed specifically for AI, the convergence of AI and HPC, emerging AI platforms from recent start-ups, and the use of new computing paradigms for AI, such as quantum computing. AI View includes both worldwide and regional insights.


Markets and Subjects Analyzed


  • The entire AI infrastructure stack, including AI technologies (e.g., accelerators), platforms (e.g., servers), systems (e.g., storage infrastructure), and infrastructure software (e.g., operating systems)
  • General-purpose, heterogeneous, and accelerated computing stacks used in support of established and emerging AI workloads
  • Infrastructure types (discrete or converged/integrated), array types (all flash, hybrid storage, or HDD), data organizations (block, file, and object), and in-memory technologies supported for AI and analytics workloads
  • Deployment location (e.g., cloud, edge, and traditional) and consumption model (traditional/as a service) preferences
  • Artificial intelligence data pipeline (including edge, core, and cloud)

Core Research


  • Worldwide executive summary
  • Digest of survey findings
  • Banner book with worldwide and regional insights, where available
  • Readout/webinar
  • Unlimited access to analysts for inquiries

In addition to the insight provided in this service, IDC may conduct research on specific topics or emerging market segments via research offerings that require additional IDC funding and client investment.


Key Questions Answered


  1. What are customers thinking, planning, and investing for their AI infrastructure?
  2. What are the major drivers of and inhibitors for AI infrastructure adoption on premises and in the public cloud?
  3. Which financial metrics will matter in AI infrastructure investments, and how will they evolve?
  4. What are the major drivers of and inhibitors for AI infrastructure adoption based on different buyer personas?
  5. What are the top AI workloads and use cases today and in the future?
  6. What are infrastructure trends for emerging use cases such generative AI, computer vision, NLP, and so forth?

Companies Covered