Deciding on the right hybrid cloud architecture for AI will be critical for AI success and 56% of European IT decision makers found that their cloud strategy has had a positive impact on their AI strategy. Where is the best place to run AI workloads, in the public cloud, at the edge, in a private cloud? There are many options and IT decision makers need to take a hard look at AI workload placement. The most important decision criteria will be the availability of security and compliance offerings from the IT partner.

AI-centric environments are spurring the adoption of new workloads across cloud platforms. Public cloud services are increasingly utilized as edge locations, allowing organizations to scale services closer to their business needs efficiently. As businesses consider integrating more data-intensive workloads, factors such as network costs and storage limitations play pivotal roles in application placement decisions.

A hybrid cloud approach to application modernization is the key to developing a modern application landscape that is fit for the AI era. Deciding on the right modernization path for each application class is one of the main decisions that IT decision makers need to take. Approaches range from lift and shift to public cloud with minimal modifications to developing cloud-native applications.