PE Spotlight: Why the AI push stalls at the critical application (it’s not the team)

AI coding tools can accelerate development tasks such as new services, testing, and backlog reduction, but they often struggle with modernization of large, long-lived Java and .NET applications because these systems contain accumulated architectural compromises, undocumented dependencies, and complex database usage. The passage argues that maintenance teams are not necessarily at fault, as they often lack a complete map of systems built by previous engineers, while AI tools similarly lack visibility into cross-module effects and real runtime behavior. It identifies gaps between intended and actual architecture, uncertainty over active database tables and stored procedures, the risk of reproducing legacy coupling through automated code conversion, and the substantial context required to guide coding agents. vFunction proposes addressing these issues through production-runtime analysis, target architecture definition, domain-level specifications and integration tests, incremental AI-assisted refactoring, and embedded engineering support that transfers capability to internal teams. It also emphasizes that rapid removal of organizational blockers, including access to repositories, environments, and stakeholders, is an important factor in modernization success.

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