In this session from DX Annual, Eleanor Millman, Senior Staff Product Manager, and Mina Tawadrous, Associate Director of Product Management at SiriusXM, share how their platform engineering organization developed a prioritization framework for platform engineering teams serving hundreds of developers across a complex cloud platform.

They explain how they define and weight platform-specific impact factors, use developer data to refine priorities, and score projects more consistently. They also explore why prioritization debates often stem from conflicting, invisible, or outdated assumptions, and how SiriusXM began treating assumptions like code by documenting, versioning, and reviewing them in source control.

Finally, they demonstrate how AI can surface assumptions, connect initiatives to existing knowledge, and support project scoring while keeping humans in the loop. Throughout the session, they offer a practical framework for making prioritization decisions more transparent, data-driven, and scalable.

In this episode, we cover:

(00:00) Intro

(02:58) Building a platform engineering prioritization framework

(04:59) The seven platform engineering impact factors

(09:38) Using impact factors to score projects

(13:11) Using developer data to refine priorities

(16:33) Three ways assumptions fail 

(17:40) Assumptions as code 

(21:00) New problems created by assumptions as code

(22:00) Using AI to surface assumptions

(23:44) Building an AI-powered feedback loop

(25:44) Inside the AI prioritization tool

(28:18) Three steps to build your own framework

(30:02) Q&A #1: Evaluating high-cost projects

(31:30) Q&A #2: The cadence of iteration 

(32:10) Q&A #3: When the framework conflicts with a stakeholder's priorities

(35:26) Q&A #4: Using the framework for non-developers

Referenced:

• AWS

• Databricks

• RICE: Simple prioritization for product managers

• Designing developer experience surveys

• GSB Preserve | View | The Curse of Knowledge