Synergizing artificial intelligence and data science: challenges and opportunities

Abstract

Purpose: to identify challenges and opportunities in integrating AI and data science and to develop a comprehensive framework for their effective and sustainable synergy.

Method: this study employs a qualitative literature review to examine the integration of artificial intelligence (AI) and data science. Data were collected from reputable academic sources and analyzed using thematic analysis to identify key themes, patterns, and research gaps. The study also applied source triangulation and critical evaluation to ensure the validity and reliability of the findings.

Findings: integrating AI and data science improves decision-making and efficiency, but is hindered by data quality issues, skill gaps, ethical concerns, and infrastructure limitations, highlighting the need for a holistic framework.

Implications: the study provides a conceptual framework for effectively integrating AI and data science, offering practical insights to improve data-driven strategies, enhance organizational readiness, and guide ethical and sustainable digital transformation.

Originality: lies in developing a holistic, multidimensional framework that integrates technical, organizational, and ethical aspects to address challenges and opportunities arising from the synergy between AI and data science.