AI adoption in software engineering is nearly universal, with 90% of survey respondents using AI and over 80% believing it has increased their productivity, according to the 2025 DORA report. However, these perceived productivity gains often do not translate into actual improvements across the entire software delivery lifecycle, as the system-level challenges such as flaky pipelines, poor documentation, and organizational bottlenecks remain unaddressed. The report highlights that AI adoption can lead to increased delivery instability, especially if organizations fail to adapt their workflows and quality gates. AI tools can also introduce new forms of waste, like prompt-response latency and validation overhead, which can hinder productivity rather than enhance it. Successful AI integration requires clear strategy, healthy data ecosystems, robust version control, and alignment between teams and systems. AI acts as both a mirror and a multiplier, enhancing well-structured systems while exacerbating issues in flawed ones, underscoring the need for comprehensive system design rather than just tooling upgrades to achieve lasting performance improvements.