SiriusXM Platform Engineering Prioritization Framework
SiriusXM leverages a weighted prioritization framework and an 'Assumptions as Code' repository to resolve cross-team conflicts. This strategy uses AI agents to validate product hypotheses against historical data, enabling scalable decision-making for platform engineering teams supporting diverse builder personas.
Strategic Shift in Platform Prioritization
SiriusXM’s platform engineering organization has evolved beyond traditional product management metrics to address the unique challenges of internal tooling. Unlike consumer products, platform engineering is twice removed from revenue, making standard RICE (Reach, Impact, Confidence, Effort) models insufficient for scaling across multiple teams. The organization developed a weighted prioritization framework that incorporates dynamic impact factors, including developer speed, runtime reliability, cost reduction, and security compliance. By adjusting these weights based on quarterly executive OKRs, leadership can directly influence roadmap priorities, ensuring that platform investments align with broader business goals such as major launches or cost optimization.
Assumptions as Code Framework
A critical innovation is the "Assumptions as Code" framework, which treats product hypotheses and user research data as version-controlled code. This approach addresses three common failure states: conflicting assumptions between teams, invisible assumptions that other teams are unaware of, and stale assumptions that no longer reflect current reality. By storing these assumptions in a central repository, SiriusXM creates a single source of truth that reduces the cognitive bias known as the "curse of knowledge." This system ensures that when a new initiative is proposed, the team has immediate access to historical data and prior research, preventing redundant work and clarifying dependencies.
AI-Driven Validation and Scalability
The framework is augmented by AI agents that validate new epics and product briefs against the assumption repository. When a developer or product manager inputs a new project description, the AI checks for existing assumptions, flags potential conflicts, and suggests relevant historical data. This automation significantly reduces the time cost of prioritization, allowing the team to evaluate smaller backlog items that were previously too labor-intensive to score. The human-in-the-loop design ensures that AI assists with recall and validation, while humans retain judgment on final decisions.
Implications for Citizen Developers
As the definition of a "builder" expands to include non-technical employees, platform engineering must adapt to support this diverse user base. SiriusXM is focusing on documenting security and best practices in accessible formats that AI assistants can utilize. This enables citizen developers to build compliant applications without deep technical expertise. However, this shift introduces new challenges in code review and maintenance, requiring the organization to invest in automated review tools and clear ownership models to manage the increased volume of contributions. The strategic focus for 2026 is on discovering existing tools to prevent duplication and streamlining the contribution process to maintain quality at scale.
Key insights
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Traditional RICE models fail in platform engineering because they lack urgency and dynamic weighting. SiriusXM’s framework uses adjustable weights for impact factors to align with shifting executive priorities.
Impact: Enables platform teams to demonstrate direct value to leadership by linking technical investments to specific business outcomes like cost reduction or launch readiness.
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The "Assumptions as Code" framework treats user research and data points as version-controlled assets. This resolves conflicts, visibility gaps, and staleness in cross-team knowledge.
Impact: Reduces redundant research efforts and accelerates decision-making by providing a single source of truth for product hypotheses across the organization.
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AI agents are used to validate new product epics against the assumption repository. This automates the recall of historical data and flags conflicts before resource commitment.
Impact: Scales prioritization to smaller backlog items by reducing the manual labor required to gather and analyze user data for each initiative.
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High quality scores in one domain, such as runtime reliability, can justify reducing weights in other areas. This data-driven approach allows for strategic resource reallocation.
Impact: Provides objective justification for shifting focus from maintenance to innovation or cost optimization, reducing political friction in roadmap planning.
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The rise of citizen developers requires platform engineering to document security best practices in AI-accessible formats. This enables non-technical users to build compliant applications.
Impact: Expands the platform’s user base while mitigating security risks, but necessitates new strategies for code review and maintenance of diverse contributions.
Action items
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Implement a weighted prioritization framework with dynamic impact factors. Adjust weights quarterly based on executive OKRs to align platform work with business goals.
Impact: Creates a transparent and responsive roadmap that directly supports organizational objectives, improving stakeholder trust and alignment.
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Establish a central repository for product assumptions and user research data. Treat this data as code, with version control and review processes.
Impact: Reduces knowledge silos and duplicate research, enabling faster and more informed decision-making across distributed teams.
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Integrate AI agents into the product definition workflow to validate new epics against the assumption repository. Use AI to flag conflicts and suggest relevant historical data.
Impact: Automates the recall of historical insights, reducing the time cost of prioritization and enabling evaluation of smaller, lower-level backlog items.
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Document security and best practices in accessible, AI-readable formats. Provide clear onboarding paths for non-technical builders to ensure compliant development.
Impact: Enables citizen developers to build safely and efficiently, expanding the platform’s utility while maintaining security and compliance standards.
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Invest in automated code review tools and streamlined contribution paths. Address the bottleneck created by increased PR throughput from diverse contributors.
Impact: Maintains code quality and velocity as the user base expands, preventing review fatigue and ensuring sustainable platform growth.
Quotes
“I realized that you break assumptions down into three faulty states, I would say. There's assumptions that can be in conflict, right? So that's when two product teams or two engineers have assumptions that are different from each other.”
“The reason why I think this works really well with AI as well is data is expensive, right? Finding data, doing surveys is expensive. And historically, what we've done is we've done really good research for our biggest bets.”
“I think what we are seeing is this democratization of building. And this has been covered kind of everywhere. And I think it would be kind of a shame to have this growth happen outside of organizations, not replicate that same sort of like safe space within organizations.”