After two months of coding with Claude Code, the author experiences a frustrating slowdown when reviewing pull requests and troubleshooting issues, despite initially enjoying the speed boost. The reliance on the AI for coding tasks has become a double-edged sword, as the author must still serve as a quality assurance engineer, often correcting errors and enforcing code quality. Skepticism remains about the future capabilities of AI in automating complex integration testing.

Enterprises are increasingly evaluating AI coding solutions to enhance productivity across the software development lifecycle. The article outlines three categories of AI coding tools, offers criteria for selecting suitable solutions, and recommends a structured approach for proof of concept (POC) processes to ensure scalability and integration with existing workflows.