AI Driven Multidomain Research: New Frontiers in Science, Business and Technology
Abstract
Artificial intelligence (AI) is no longer confined to computer science. The same families of learning algorithms now predict protein structures, price financial assets, design semiconductor layouts and personalise customer journeys. This diffusion has created a new kind of scholarship in which methods, data and problem formulations move rapidly between science, business and technology. Yet the literature that documents this shift remains organised by discipline, and there is little integrative guidance on how AI-enabled knowledge actually travels across domains or on the institutional conditions under which such travel is productive. This paper addresses that gap through an integrative review and conceptual synthesis. We first consolidate representative evidence on how AI is reshaping inquiry in the natural sciences, in business and management, and in engineering and technology. We then propose the Multidomain AI Research Integration (MAIRI) framework, which explains cross-domain research value through three interacting layers: a foundation layer of shared data, compute and models; a translation layer comprising four transfer mechanisms (method, data and representation, problem-structure, and talent and tool transfer); and a governance layer that secures reproducibility, explainability, ethics and equitable access. Five propositions are derived to guide empirical testing, and a forward research agenda is set out. The paper contributes a common vocabulary for multidomain AI research and offers practical direction for universities, funding agencies and industry partners, particularly in emerging research systems such as India.
