# Introduction Delivering machine learning based projects is often a team effort. In this part I'll be writing about some of my thinking how to best organize people and projects. My main insight is that data science based work is different from "normal" engineering work because there is much higher amount of uncertainty that can require a lot of experimentation to figure out. Other questions are how to best structure teams, and organizations. Introducing data science into an organization is a change process that is not just about hiring data scientists (which in itself is already a big challenge). You need to gain experience with data science, gain trust as an organization that there is some tangible return on investment, and gradually being to scale out data science across the whole organization. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://book.mlinpractice.com/people-and-processes/introduction.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.