> For the complete documentation index, see [llms.txt](https://www.mlcompendium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.mlcompendium.com/ai-product/project-and-program-management.md). # Project & Program Management Big projects still need a delivery shape. The page starts with how big tech actually runs projects, then separates the PM, TPM, and tech-lead roles, and ends with the single-threaded owner model and an agile extension for AI teams. The same notes are in [Data Program Management](/data/engineering-intro/data-program-management.md) and [Project Management](/ai-product/management.md#project-management). The first surprise is that the famous methodology is mostly missing at the top. [How Big Tech Runs Tech Projects and the Curious Absence of Scrum](https://newsletter.pragmaticengineer.com/p/project-management-in-tech) is Gergely Orosz's survey of how tech projects run across the industry, why Scrum is absent from Big Tech, and what others should take from that. If the process is not Scrum, the roles carry the delivery. [Product manager vs project manager vs technical program manager](https://medium.com/swlh/product-manager-vs-project-manager-vs-technical-program-manager-e45d0cf32e01) is Julius Uy's comparison of the three, written against the backdrop of the Singapore tech scene growing in one decade from a regional sales and delivery center into a concentration of startups. On the engineering side, [Tech Lead vs Team Lead](https://www.rubick.com/engineering-manager-vs-tech-lead/) is Jade Rubick on the ways people break up leadership roles on engineering teams (engineering manager, tech lead, and single-threaded owner) and how some responsibilities are split between the engineering leader and other functions. The last of those roles gets its own post: [Single Threaded Owner](https://www.rubick.com/implementing-amazons-single-threaded-owner-model/), i.e., "a team focusing on a single thing at a time" - by Jade Rubick For an AI team, the delivery shape itself has to stretch. [Flexeegile](https://pub.towardsai.net/flexeegile-extending-agile-for-ai-data-teams-bdac2c422c45) (December 2024) extends program management for an AI team. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://www.mlcompendium.com/ai-product/project-and-program-management.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. 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.