Preparing for generative AI inferencing: A strategic approach to infrastructure

The adoption of generative AI is reshaping the way businesses operate, shifting AI from experimental pilot programs to large-scale, live deployments.  

For many organizations, the focus has moved beyond training models to the computational demands of inference—the phase where AI models provide actionable insights or perform tasks in real-world settings. This evolution brings forth new challenges that extend across IT, infrastructure, and strategic planning.   

Inference workloads, often underestimated in complexity, now demand diverse physical infrastructure strategies. While some tasks remain simple—such as batch processing—others involve highly sophisticated computations requiring greater power density, cooling, and rapid scalability.  

The question for leaders is no longer whether their infrastructure can handle AI workloads, but how to plan for future demands while balancing cost, performance, and resilience.  

This is the topic of a new report by Schneider Electric’s Data Center Research and Strategy organization, created in conjunction with World Wide Technology. Read on for a glimpse into the key takeaways from our research, before downloading the full report for all the juicy details.