Circei suggests carrying out a proof of concept before deciding to invest in AI development tools. This involves identifying what improvements are needed within the engineering team, and then using an engineering management platform or software engineering intelligence platform to track whether AI adoption is making a difference. He advises not to restrict the proof of concept to a specific coding stage or project, but to use it across diverse functions to see how AI tools perform under different scenarios and with coders of different skills and job roles.
Key takeaways:
- Investing in AI technology can significantly increase the efficiency of engineering teams, but enterprises need to understand how to budget for AI tools and how to ensure their training is effective.
- Who is using AI tools is a critical business decision, as less experienced developers can benefit more from AI than experienced ones.
- Before investing in AI development tools, it's crucial to carry out a proof of concept to establish whether the AI is generating tangible value and how much.
- It's important to track the impact of AI adoption on various aspects of the engineering team, such as code security, velocity, and developer well-being, using an engineering management platform or software engineering intelligence platform.