1. Research infrastructure often grows faster than the strategy behind it.
Clusters, storage, cloud accounts and specialized systems accumulate over years. Eventually the organization isn’t just dealing with old technology; it’s dealing with an environment that no longer reflects how researchers actually work.
2. “Move it to the cloud” isn’t a research computing strategy.
Some workloads belong on-prem, some are great cloud candidates, and some benefit from hybrid approaches. In the AMC case study, BioTeam specifically evaluated which research and clinical workloads belonged on new HPC infrastructure versus cloud resources.
3. AI readiness exposes problems that were already there.
AI doesn’t create fragmented data, inconsistent metadata, governance gaps or inadequate infrastructure. It makes those weaknesses much harder to ignore. BioTeam sees this across life sciences organizations preparing for AI.
4. Researcher experience matters as much as raw computing power.
This is a GREAT AMC point. In the case study, lack of graphical interfaces created barriers for clinical users. The resulting architecture was designed to be accessible to researchers with different levels of technical expertise.
5. The people and operating model have to scale with the infrastructure.
I especially like this one because it separates BioTeam from vendors selling hardware. The AMC didn’t only need new compute and storage. BioTeam identified needed services, internal skills, resource gaps, SLAs and where vendor-managed services made sense.
See how BioTeam helped the organization build an infrastructure strategy designed to support research at scale. Read the case study: Research at Scale: Architecting HPC Infrastructure for a Leading Academic Medical Center
