First, get your house in order. The next three to six months should be spent deep-diving into current cloud spending and utilization patterns. I’m talking about actual numbers, not the sanitized versions you show executives. Map out your AI and machine learning (ML) workload projections because, trust me, they will explode beyond your current estimates. While you’re at it, identify which workloads in your public cloud deployments are bleeding money—you’ll be shocked at what you find.
Next, develop a workload placement strategy that makes sense. Consider data gravity, performance requirements, and regulatory constraints. This isn’t about following the latest trend; it’s about making decisions that align with business realities. Create explicit ROI models for your hybrid and private cloud investments.
Now, let’s talk about the technical architecture. Your focus must be on optimizing data pipelines, integrating edge computing, and meeting AI/ML infrastructure requirements. Multicloud connectivity isn’t optional anymore—it’s a requirement for survival. But here’s the catch: You must also maintain ironclad security and compliance frameworks.
