Lau suggests running a short-term experiment by selecting a segment of the engineering organization to implement AI-backed tools and comparing the results with a control group. He emphasizes the importance of analyzing both quantitative and qualitative data from the experiment to guide the wider implementation of generative AI tools. Lau concludes that every team will need to find the right approach to integrate these tools into their workflows and optimize performance.
Key takeaways:
- Generative AI is reshaping engineering organizations in three ways: changing responsibilities, changing skill sets, and changing hiring priorities.
- Engineering leaders should view AI as an opportunity rather than a threat or a challenge, and should start experimenting with AI-backed tools.
- Companies that adopt generative AI early are likely to have an advantage over those that take a “wait and see” approach.
- Running an AI experiment within a segment of the engineering organization can provide valuable data on how to implement these tools on a wider scale.