From Automation to Augmentation: AI-Driven Business Analytics and the Future of Workforce Productivity
DOI:
https://doi.org/10.18533/9jpbdf75Keywords:
Business analytics, Artificial intelligence, Automation and augmentation, Workforce productivity, Digital transformationAbstract
Artificial intelligence (AI) and sophisticated business analytics are quickly transforming how companies generate value out of data, although most organizations have approached AI implementation as a form of pure automation that is replacing labor. In this paper, we will argue that business analytics have a future of transitioning beyond automation to an augmentation where AI systems do not replace the workforce productivity but rearrange it. The study builds on a qualitative meta-synthesis of peer-reviewed research, policy reports, and cases of industry applications published since 2012 to develop an Automation-to-Augmentation Business Analytics (A2A-BA) framework. The discussion outlines four types of augmentation, namely re-bundling of tasks and workflow redesign; decision support and sense-making; skills and AI-literacy upgrading; and new productivity measures and governance. These processes describe how the human capabilities and AI analytics have complementarities that determine the productivity outcomes and the distributional effects. The paper wraps up with a policy and managerial implication on how to design augmentation-first strategies, reskilling systems and ethical governance arrangements to support inclusive and sustainable productivity gains. Theoretically, the framework theorizes socio-technical insights into business analytics and provides falsifiable predictions to the future of empirical studies.
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Copyright (c) 2025 Md. Mahedi Hasan, Md. Yeasir Arafat Bhuiyan , Abdullah Al Maruf, Sujit Kumer Deb Nath, Muhammad Belal Hossain Khan
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