AI is not a magic button that turns legacy spaghetti into cloud-native perfection, but it's more useful than you might expect. AI excels at grunt work such as code archaeology, pattern recognition at scale, and mechanical transformations. It can map dependencies across millions of lines in hours, spot inconsistencies, and standardize patterns. However, AI requires guidance on what good looks like and validation of its outputs. To get the most out of AI, teams need to deploy private models internally, fine-tune them on their codebase, integrate with knowledge sources, and secure development environments. A multi-model strategy that uses specialized pipelines for analysis, transformation, testing, and security can deliver results. The key is to treat AI as a capable research assistant who needs guidance on business context, not just generic input. Teams won't win by simply using the fanciest AI tools, but by learning and understanding the boundaries between roles and responsibilities within the human-AI working relationship. Developers focus on architecture, creative problem-solving, and understanding business requirements, while AI handles repetitive implementation work, documentation, and code analysis.