By late 2023, generative AI had pushed enterprise AI teams from pilots toward production, and the infrastructure behind those workloads was changing quickly. A Fortune 500 enterprise technology company that sells infrastructure hardware wanted to know where its products fit in that shift. Its product teams needed a practitioner's view of how enterprises assemble an AI stack, which workloads they keep on their own hardware and which they send to the public cloud, and how they choose and pay for AI infrastructure.
The questions were specific. Did enterprise teams prefer specialized servers, storage and networking, or general-purpose equipment? How did requirements differ across training, fine-tuning and inference? Which factors decided vendor selection, and did AI purchases follow the same cycle as general IT? Did buyers want to own the hardware or consume it as a service? The company asked PP&A to put these questions to people who design and run enterprise AI systems, and to use the answers to inform the development of its AI infrastructure product lines.
PP&A built an interview guide that followed the AI stack from end to end: how teams integrate AI with their general IT stack, whether they build models from scratch or adapt foundation models, how architecture and performance needs differ across training, tuning and inference, how they choose vendors, which design features they require, from cloud or on-premises hosting to GPUs, other accelerators and liquid cooling, and how they purchase AI infrastructure.
Between December 19 and 27, 2023, PP&A led five interviews with senior AI practitioners in the United States and Europe: the head of AI for drug discovery at a global pharmaceutical company, the head of autonomous operations and emerging technologies at a large industrial manufacturer, a data science manager in the AI center of excellence of a bank, the vice president of AI products at a building technology company, and the engineering leader who runs the AI platform at an enterprise software company. The team synthesized the transcripts into a structured interview summary that set the five perspectives side by side across eight themes, and delivered it to the client on December 28, 2023.
The client received a structured summary that set the five practitioners' answers side by side across eight themes, one day after the last interview. It showed how each team splits its AI workloads between the public cloud and its own hardware, and why. It covered how much of each estate and budget goes to AI-dedicated hardware and where that share is heading, and how generative AI has changed model sizes, training times and the compute and storage that inference needs. On buying, it set out the factors that decide vendor selection, whether teams buy direct or through partners, and how long selection, contracts and hardware support run. The five enterprises followed no single hosting pattern. The summary gave the client a practitioner-level map of these trade-offs to test its AI infrastructure product plans against.
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