The Application of Artificial Intelligence Technology in Assisting R&D Project Initiation

Zhenhuan Liu

doi:10.26689/jera.v9i5.12395

Zhenhuan Liu Golden Gate University, San Francisco, CA, 94105, USA

DOI: https://doi.org/10.26689/jera.v9i5.12395

Keywords: Artificial intelligence, Project initiation, Management

Abstract

This paper reviews the latest advancements in artificial intelligence-assisted R&D project initiation, aiming to provide intelligent solutions for R&D management. It thoroughly examines the value of artificial intelligence technologies in four core areas: intelligent requirement analysis, technical feasibility assessment, market prospect forecasting, and automated risk identification. Furthermore, it proposes three forward-looking trends—enhanced intelligence, the establishment of industry standards, and deeper human-machine collaboration. These insights are expected to improve project approval success rates and shorten initiation timelines, driving a paradigm shift in R&D management from experience-based to data-driven decision-making. The review highlights how artificial intelligence, through machine learning, natural language processing, and data mining, effectively addresses chronic challenges in traditional initiation processes such as inefficiency, delayed decisions, and resource misallocation. It also identifies critical hurdles, including data quality, model interpretability, and organizational transformation, offering a vital reference framework for the future of intelligent R&D development.

References

Hamamoto R, 2021, Application of Artificial Intelligence for Medical Research. Biomolecules, 11(1): 90.

Mooghal M, Anjum S, Khan W, et al., 2024, Artificial Intelligence-Powered Optimization of KI-67 Assessment in Breast Cancer: Enhancing Precision and Workflow Efficiency. A Literature Review. J Pak Med Assoc, 74(4 (Supple-4)): S109–S116.

Ding H, Tian J, Yu W, et al., 2023, The Application of Artificial Intelligence and Big Data in the Food Industry. Foods, 12(24): 4511.

Claudino JG, Capanema DO, de Souza TV, et al., 2019, Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: A Systematic Review. Sports Med Open, 5(1): 28.

Yu H, Liu S, Qin H, et al., 2024, Artificial Intelligence-Based Approaches for Traditional Fermented Alcoholic Beverages’ Development: Review and Prospect. Crit Rev Food Sci Nutr, 64(10): 2879–2889.

Mu W, Kleter GA, Bouzembrak Y, et al., 2024, Making Food Systems More Resilient to Food Safety Risks by Including Artificial Intelligence, Big Data, and Internet of Things into Food Safety Early Warning and Emerging Risk Identification Tools. Compr Rev Food Sci Food Saf, 23(1): e13296.