AI-First Research Ecosystems: Redefining Multidisciplinary Innovation in the Digital Era

Abstract

Research organisations have so far adopted artificial intelligence (AI) in a piecemeal way: a literature tool here, a prediction model there, each attached to workflows that were designed for a pre-AI world. This paper argues that the next phase of multidisciplinary innovation depends on a different posture, which we call AI-first: research ecosystems in which data, models, workflows, people and rules are designed from the outset on the assumption that AI participates in every stage of inquiry. Writing from the combined perspectives of electronics and communication engineering, computer science, pharmacology and mathematics, we synthesise recent literature on AI-enabled discovery and on research and innovation ecosystems. We map the functions of AI across the research lifecycle, and propose the AI-First Research Ecosystem (AFRE) architecture comprising five interdependent layers: data commons, model and compute, agentic workflow, human expertise and collaboration, and governance and incentives, connected by explicit learning loops. Disciplinary vignettes show how each field both contributes to and draws from the shared layers. A five-level maturity model enables institutions to locate themselves between ad hoc tool use and a fully AI-first configuration, and five propositions are advanced for empirical testing. We also examine the guardrails—verification, provenance, authorship norms, equitable access to compute and protection against methodological monoculture—without which AI-first research would be faster but not better. The paper concludes with a staged roadmap for universities and research institutes, with specific reference to the Indian higher-education context.