Innovation
How LLMs accelerate discovery and refactors for monoliths and long-lived services—and the review gates that keep production safe when AI suggests changes.
Generative AI for legacy modernization is reshaping how teams attack unfamiliar codebases—if governance keeps pace. We outline where assistants help most and where human-led review stays non-negotiable.
Large language models can summarize unfamiliar modules, sketch tests, and propose refactors faster than manual archaeology alone. That speed is valuable when documentation drifted or original authors moved on.
The risk is confident wrongness: a plausible-looking migration that misses edge cases in batch jobs, licensing checks, or implicit contracts between services. Treat model output as a draft that must pass review, tests, and staged rollout.
Strong patterns merge AI assistance with governance: narrow scopes per change request, mandatory diff review by someone who knows the domain, and canary deployments with observability on error budgets.
Teams seeing the best outcomes use AI for exploration and scaffolding—not unsupervised edits to critical paths until coverage and monitoring catch up.
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