This paper aims to examine the conditions under which AI policy advice systems (AIPAs) are more likely to adopt a multidisciplinary composition and collaborative framework. Advanced democracies have developed AIPAs to guide the responsible development, deployment, and use of artificial intelligence technologies. These governance structures serve a range of essential functions, including establishing regulatory standards, monitoring technological advancements, addressing ethical concerns, ensuring equitable access to AI's benefits, and mitigating risks associated with AI misuse. However, AIPAs vary in their accessibility, openness, equity, and alignment with guiding principles and goals. Accessibility refers to how technical and practical knowledge is made available through public services, think tanks, NGOs, or private consultants. The openness of channels highlights the inclusivity of advisory systems, ensuring that a wide array of stakeholders can contribute their insights. Equity in access emphasizes creating equal opportunities for all actors to offer their perspectives and have their advice considered in policymaking. Alignment with principles, goals, and instruments underscores how AIPAs are shaped by the broader policy subsystem. Building on frameworks from Policy Advice Systems (PAS) and governance approaches, this paper explores how advice is structured, delivered, and utilized in AI decision-making processes. Using a mixed-methods approach, we compare 73 AIPAs through both quantitative and qualitative analyses. We explore their accessibility, the alignment of policy ideas, and other institutional characteristics. The paper reveals significant differences in AIPAs, with notable variations in the number and type of actors involved, the functions they perform, and the resources they have at their disposal. Accessibility within PAS not only facilitates technically sound advice but also fosters inclusiveness, usability, and alignment with the realities of governance. Furthermore, collaborative frameworks ensure that diverse perspectives shape decision-making, enabling governance systems to balance innovation with accountability and equity. This comparative analysis highlights the importance of designing AI policy advice systems that are both comprehensive and inclusive, ensuring effective and equitable policymaking in the rapidly evolving field of artificial intelligence.