AI-Augmented Decision-Making in Agile Teams: A Bibliometric Review from a Strategic Management and Organizational Capability Perspective

Abstract

This bibliometric review systematically maps the intellectual and thematic landscape of AI-augmented decision-making in agile IT project teams, drawing on 607 peer-reviewed articles retrieved from the Scopus database for the period 2020–2026. The study examines publication trends, influential authors and journals, keyword co-occurrence patterns, co-citation networks, and thematic evolution. Following the PRISMA 2020 framework and using the Bibliometrix R-package alongside VOSviewer, bibliometric network analyses identify three major research clusters, leading contributors including Kumar A and Govindan K, and evolving thematic configurations. Findings reveal a pronounced surge in research output from 2022 onward, with machine learning, artificial intelligence, deep learning, and blockchain emerging as dominant research themes, signalling a disciplinary shift from foundational AI methods toward explainable AI, predictive analytics, and smart circular economy applications. The study identifies research gaps and articulates future directions, with specific reference to the Indian IT engineering context.