Over the past decade and a half, cloud computing has become a foundational technology. It started as a way to rent servers but has evolved into a complex ecosystem that supports everything from basic infrastructure shifts to transformative AI initiatives. Having advised enterprises on thousands of cloud projects over the years, I have seen that most projects fall into a handful of categories. I can say with certainty that success depends less on hype and more on understanding each project’s nature, risks, costs, and lessons.
Cloud migrations
Enterprises continue to migrate existing workloads from data centers to public, private, or hybrid environments. This can involve rehosting (lift and shift), replatforming with minor changes, or full refactoring into cloud-native architectures. The goal is usually cost reduction, scalability, or the end of hardware refresh cycles. The risks here are well documented. Many projects underestimate dependencies, leading to performance surprises or integration failures. Data egress fees and unexpected operational costs can wipe out projected savings.
Cost profiles vary widely. Initial migrations often run 20% to 50% over budget due to discovery gaps and testing. Ongoing expenses can decline through rightsizing and reserved instances, but poor management often leads to 25% to 35% waste from idle resources. These lessons underscore the importance of modeling the total cost of ownership up front, including people, training, and change management.
