International Journal of
Advanced Multidisciplinary Application

Volume 3, Issue 7

• Author(s)Aditi Krishnamurti, Marcus J. Wolfe
• AffiliationCentre for Biomedical AI, All India Institute of Medical Sciences, New Delhi, India School of Environmental Policy, University of Cambridge, Cambridge, United Kingdom
• Page No.1-6
• Volume, Issue & YearVolume 3, Issue 7, July 2026
• Published On2026/07/01
• JournalInternational Journal of Advanced Multidisciplinary Application (IJAMA)
• ISSN No.3048-9350

Abstract

The proliferation of artificial intelligence (AI) technologies across industrial, social, and scientific domains constitutes one of the most consequential technological transitions of the twenty-first century. This multidisciplinary review synthesises evidence from peer-reviewed literature, industry datasets, and primary empirical analyses to examine AI adoption trajectories across six key sectors — healthcare, finance, manufacturing, education, retail, and agriculture — through the interlocking lenses of technological performance, economic productivity, workforce transformation, ethical governance, and environmental sustainability. A cross-sectoral AI adoption dataset (n = 214 organisations, 2020–2024) reveals mean adoption growth of 143% over the study period, with healthcare and finance as lead adopters. AI-assisted diagnostic systems demonstrate mean accuracy of 93.1% across five clinical domains, outperforming specialist clinicians by 6.2 percentage points. Econometric modelling (OLS with sector fixed effects) confirms a significant logarithmic relationship between AI investment intensity and productivity gain (β = 3.21, p < 0.001, R² = 0.74). Thematic analysis of 62 governance frameworks identifies bias and accountability as the highest-rated ethical concerns across all stakeholder groups. A lifecycle carbon assessment of AI-driven process optimisation across five industrial sectors estimates mean CO₂ reduction of 24.2%, with energy grid applications yielding the largest absolute gains. These findings support the conclusion that AI’s transformative potential is maximised when deployment is accompanied by parallel investment in workforce reskilling, ethical governance infrastructure, and sustainability-conscious systems design.

Keywords: artificial intelligence, multidisciplinary, healthcare AI, AI ethics, workforce transformation, environmental sustainability, technology adoption, productivity, carbon footprint, governance

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