Review existing codebases to determine where AI-assisted tools can be effectively applied, taking into account architecture, language mix, and code complexity
Explore and benchmark AI solutions that support different stages of the engineering lifecycle, such as automated documentation, test creation, and code quality analysis
Run practical experiments using AI tools on selected components to assess:
Output quality and accuracy
Test coverage and completeness
Time savings compared to traditional methods
Work closely with engineering teams to:
Identify suitable candidates for pilot initiatives
Validate AI-generated outputs
Feed insights into broader engineering practices
Contribute to the rollout of AI-enabled engineering practices, including: