ROSAI: Role-Oriented Skills Assessment for AI — A Framework for Role-Relative AI Readiness

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

ROSAI — Role-Oriented Skills Assessment for AI — is a framework developed by Prasanjit Saha for assessing professional AI readiness relative to the mix of responsibilities expected from a role. ROSAI represents a role as a continuous composition across Product/Business, Software Engineering, Data/ML, Operations/Transformation, Design/UX and Leadership/Governance. That role composition dynamically determines the relative importance of seven AI capability dimensions: AI Fluency, Applied AI Judgement, Build & Execution, Evaluation & Reliability, Data & ML Capability, Responsible AI & Governance, and Business & Organizational Impact. The framework produces three separate interpretation signals: ROSAI Score, Evidence Strength, and Assessment Confidence. This Version 1.0 paper defines the conceptual model, high-level scoring architecture, role-relative weighting mechanism, validation roadmap, limitations, and the relationship between ROSAI and existing AI literacy and workforce competency frameworks. The first implementation of the framework is SCORE-AI — Skills, Capability, Outcomes, Role-fit & Evidence for AI, available at:https://score-ai.prasanjitsaha.com/ ROSAI v1.0 is a practitioner-designed framework and testable methodology. It has not yet undergone psychometric validation or peer review.