Organizing Generative AI: 5 Lessons Learned From Data Science Teams

After executive leadership vaguely promised stakeholders that new Gen AI features would be incorporated across the organization, your <a href="https://www.lucidchart.com/blog/what-is-a-tiger-team#:~:text=A%20tiger%20team%20is%20a,Apollo%2013%20mission%20in%201970." rel="noopener ugc nofollow" target="_blank">tiger team</a> sprinted to produce a MVP that checks the box. Integrating that OpenAI API into your application wasn’t that difficult and it may even turn out to be useful. But now what happens? Tiger teams can’t sprint forever. Each member has another role within the organization that will once again require the majority of their time and focus. Not to mention, there is a reason for the typical processes and structures that were ignored expedited for this project. It turns out they are pretty critical to ensuring product fit, the transition from development to operations, and cost optimization (among other things). Come to think of it, now that the project is complete there really isn’t any platform infrastructure that can help scale the next round of LLM models or other Gen AI product features. <a href="https://towardsdatascience.com/organizing-generative-ai-5-lessons-learned-from-data-science-teams-2f271874ae4a">Click Here</a>