Legacy modernization often looks like a technical task: update old code, rewrite the system, move it to the cloud, or replace outdated dependencies. In practice, it is always work with risk.
A legacy system often holds business logic, old integrations, hidden dependencies, data flows, users, security rules, downtime limits, costs, and migration constraints.
AI can speed up part of this work. It can help with documentation, code analysis, dependency mapping, test generation, duplicate logic detection, and explanations of old code.
But AI does not have enough context to decide what should be rewritten first, which architecture should be chosen, which risks are acceptable, and which areas should not be touched before proper discovery.
Legacy Modernization Is Risk Management, Not Just Code Cleanup
