Developing ERAF-AI: An Early-stage Research Assessment Framework for Artificial Intelligence.

Today, most research evaluation frameworks are designed to assess mature projects with well-defined data and clearly articulated outcomes. Yet, few, if any, are equipped to evaluate the promise of early-stage research, which is inherently characterized by limited evidence, high uncertainty, and evolving objectives. These early-stage projects require nuanced assessments that can adapt to incomplete information, project maturity, and shifting research questions. Compounding these challenges is the difficulty of systematically scaling evaluations with the increasing volume of research projects. As a step toward addressing this gap, we introduce the Early-Stage Research Assessment Framework for Artificial Intelligence (ERAF-AI), a systematic approach to evaluate research at Technology Readiness Levels (TRLs) 1 to 3 - maturity levels where ideas are more conceptual and only preliminary evidence exists to indicate potential viability. By leveraging AI-driven methodologies and platforms such as Lateral's Coordination.Network, ERAF-AI ensures transparent, scalable, and context-sensitive evaluations that integrate research maturity classification, adaptive scoring, and strategic decision-making. Importantly, ERAF-AI aligns criteria with the unique demands of early-stage research, guiding evaluation through the 4P framework (Promote, Pause, Pivot, Perish) to inform next steps. As an initial demonstration of its potential, we apply ERAF-AI to a high-impact early-stage project, providing actionable insights and measurable improvement over conventional practices. Although ERAF-AI shows significant promise in improving the prioritization of early-stage research, further refinement and validation across a wider range of disciplines and datasets is required to refine its scalability and adaptability. Overall, we expect this framework to serve as a valuable tool for empowering researchers to make informed decisions and to prioritize high-potential initiatives in the face of uncertainty and limited data.