In the tech world today, some folks think the cloud is not as cheap as before. But, it is crucial to understand that the cloud played a big role in cool innovations like Uber and Netflix. Some companies switched back to in-house solutions because they were worried about storage costs. But, the cloud was super important in their early growth and ability to expand.
The rising costs of cloud computing often result from operational inefficiencies, which have given rise to the concept of cloud cost optimization, or FinOps. As companies transition to AI adoption, they encounter similar challenges, particularly when dealing with large-scale machine learning. Learning from cloud computing adoption, it is crucial for companies to establish foundational principles, practices and skills for effective cloud usage before diving into AI. Technology leaders should consider these fundamentals when making decisions about their cloud infrastructure and AI integration.
Many organizations mistakenly use cloud computing solely for cost control rather than strategic innovation. To avoid a similar error with AI, companies should clearly define how AI adds customer value. Just as they embraced principles like DevOps for cloud adoption, a robust cloud architecture and high-quality data foundation are crucial for AI.
Traditional lift and shift cloud migrations often yielded poor results, leading to a growing emphasis on cloud cost optimization. The high cost of AI highlights the need for effective model optimization and governance. Skill gaps in data, analytics and other areas highlight the importance of building cloud expertise within organizations as cloud fluency is essential and just like it is for AI.
To succeed with AI, skills development should involve not only technologists but also business leaders and stakeholders to seamlessly integrate AI with human intelligence through ongoing education. To sum it up, the discussion about how well cloud computing works is similar to the talk about the good and tricky sides of new ideas and inventions. Cloud repatriation is not a failure but a reflection of complexity in getting it right and similar challenges apply to artificial intelligence, which requires addressing hurdles to unlock its full potential.