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 advanced business analytics are reshaping how organizations allocate tasks, make decisions, and define productivity. Yet much of the AI–work debate still treats analytics as a primarily automation-oriented technology (replacement, monitoring, and headcount reduction). Building on a qualitative meta-synthesis of interdisciplinary research and policy evidence published between 2012 and 2025, this article develops the Automation-to-Augmentation Business Analytics (A2A-BA) framework as a socio-technical *process model* that explains when and how analytics moves from automation to augmentation and with what productivity consequences. The synthesis identifies four interdependent mechanisms (1) task re-bundling and workflow redesign, (2) decision support and organizational sense-making, (3) skills/AI literacy and hybrid roles, and (4) productivity metrics and governance and specifies their causal logic: skills and governance enable effective task and decision reconfiguration, while metrics and governance also institutionalize (or undermine) augmentation over time through feedback loops. The framework contributes by integrating task-based technological change, work design, sociotechnical systems, and dynamic capabilities into an empirically anchored explanation that yields testable propositions and explicit boundary conditions (e.g., low-data environments, small firms, and informal labor markets). Managerial and policy implications highlight augmentation-first strategy, inclusive reskilling, and accountable analytics governance for sustainable productivity gains.
References
Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3–30. https://doi.org/10.1257/jep.33.2.3
Bastida, M., Vaquero García, A., Vázquez Taín, M. Á., & Del Río Araujo, M. (2025). From automation to augmentation: Human resource’s journey with artificial intelligence. Journal of Industrial Information Integration, 46, 100872. https://doi.org/10.1016/j.jii.2025.100872
Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597. https://doi.org/10.1080/2159676X.2019.1628806
Czarnitzki, D., Fernández, G. P., & Rammer, C. (2023). Artificial intelligence and firm-level productivity. Journal of Economic Behavior & Organization, 211, 188–205. https://doi.org/10.1016/j.jebo.2023.05.008
Damioli, G., Van Roy, V., & Vertesy, D. (2021). The impact of artificial intelligence on labour productivity. Eurasian Business Review, 11(1), 1–25. https://doi.org/10.1007/s40821-020-00172-8
Davenport, T. H., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48, 24–42. https://doi.org/10.1007/s11747-019-00696-0
Dube, R., Agarwal, K., Rahman, A., Latif, W. B., & Balaji, V. (2025, February). The impact of AI on economic forecasting: A case study of currency exchange rate prediction. In 2025 International Conference on Technology Enabled Economic Changes (InTech) (pp. 755–760). IEEE. https://doi.org/10.1109/InTech64186.2025.11198571
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., … Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Filippucci, F., Gal, P., Jona-Lasinio, C., Leandro, A., & Nicoletti, G. (2024). The impact of artificial intelligence on productivity, distribution and growth: Key mechanisms, initial evidence and policy challenges (OECD Artificial Intelligence Papers, No. 15). Organisation for Economic Co-operation and Development. https://doi.org/10.1787/8d900037-en
Islam, M. N., Latif, M. S. W. B., Yasin, I. M., & Ali, M. J. (2025). Trending research: Transforming applied medical sciences with EdTech innovations. The Research Seer, 4(1), 1–5. Retrieved from https://www.researchseer.com/volumes-issues/volume-4-issue-1/trending-research
Latif, W. (2022). Determinants of hotel brand image: A unified model of customer-based brand equity. International Journal of Customer Relationship Marketing and Management, 13(1), 1–20. https://doi.org/10.4018/IJCRMM.300834
Latif, W. B., & Yasin, I. M. (2025). From disparities to sustainability: Embedding artificial intelligence and digital health literacy into learning health systems—A critical review article. Pacific Journal of Medical Research, 2(4), 167–173. https://doi.org/10.70818/pjmr.v02i04.0122
Latif, W. B., & Yasin, I. M. (2025). The evolution of business models: How innovation is redefining strategic success. Pacific Journal of Business Innovation and Strategy, 2(2), 1–4. https://doi.org/10.70818/pjbis.2025.v02i02.039
Latif, W. B., Islam, M. A., Rahman, A., Mohamad, M., & Kongsompong, K. (2016). Conceptual framework of brand image for tourism industry: Tourism management and advertisement as moderators. Journal of Tourism Management Research, 3(1), 1–9. https://doi.org/10.18488/journal.31/2016.3.1/31.1.1.9
Latif, W. B., Yasin, I. M., Ali, M. J., Islam, M. N., & Forid, M. S. (2024). Transforming applied medical sciences: The impact of AI, VR, and AR on research, education technology, and clinical practices. Integrative Biomedical Research, 8(9), 1–8. https://doi.org/10.25163/angiotherapy.899862
Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics and firm performance: Findings from a mixed-method approach. Journal of Business Research, 98, 261–276. https://doi.org/10.1016/j.jbusres.2019.01.044
Ozgul, P., Fregin, M.-C., Stops, M., Janssen, S., & Levels, M. (2024). High-skilled human workers in non-routine jobs are susceptible to AI automation but wage benefits differ between occupations. Labour Economics. Advance online publication. https://doi.org/10.48550/arXiv.2404.06472
Parker, S. K., & Grote, G. (2020). Automation, algorithms, and beyond: Why work design matters more than ever in a digital world. Applied Psychology, 71(4), 1171–1204. https://doi.org/10.1111/apps.12241
Sharma, R., Mithas, S., & Kankanhalli, A. (2014). Transforming decision-making processes: A research agenda for understanding the impact of business analytics on organisations. European Journal of Information Systems, 23(4), 433–441. https://doi.org/10.1057/ejis.2014.17
Sindhura, K., Rajagopal, N. K., Latif, W. B., Kumar, J. R., Kayande, R. A., & Sakthivel, S. (2025, February). Leveraging big data and machine learning for real-time economic monitoring and forecasting. In 2025 International Conference on Technology Enabled Economic Changes (InTech) (pp. 789–794). IEEE. https://doi.org/10.1109/InTech64186.2025.11198446
Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., … Fuso-Nerini, F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11, 233. https://doi.org/10.1038/s41467-019-14108-y
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J.-F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365. https://doi.org/10.1016/j.jbusres.2016.08.009
Yasin, I. M., & Latif, W. B. (2025). The strategic emergence of BRICS in business, policy and economics reimagining global trade and development. Pacific Journal of Business Innovation and Strategy, 2(3), 1–4. https://doi.org/10.70818/pjbis.2025.v02i03.074
Zirar, A., Imran, S., & Islam, N. (2023). Worker and workplace AI coexistence: Emerging themes and research agenda. Technovation, 124, 102747. https://doi.org/10.1016/j.technovation.2023.102747
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Md. Mahedi Hasan, Md. Yeasir Arafat Bhuiyan , Abdullah Al Maruf, Sujit Kumer Deb Nath, Muhammad Belal Hossain Khan
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
