Synergizing AI and Business Analytics for End-to-End Process Digitization: Frameworks for Sustainable Transformation

References

[1] Baier, M.S., Lockl, J., Röglinger, M. and Weidlich, R., 2022. Success factors of process digitalization projects–insights from an exploratory study. Business Process Management Journal, 28(2), pp.325-347.

[2] Barney, J., 1991. Firm resources and sustained competitive advantage. Journal of management, 17(1), pp.99-120.

[3] Bharadwaj, A., El Sawy, O.A., Pavlou, P.A. and Venkatraman, N.V., 2013. Digital business strategy: toward a next generation of insights. MIS quarterly, pp.471-482.

[4] Bukowski, M., Farkas, R., Beyan, O., Moll, L., Hahn, H., Kiessling, F. and Schmitz-Rode, T., 2020. Implementation of eHealth and AI integrated diagnostics with multidisciplinary digitized data: are we ready from an international perspective?. European radiology, 30, pp.5510-5524.

[5] Davenport, T.H. and Harris, J., 2017. Competing on analytics: Updated, with a new introduction. Harvard Business Review Press.

[6] Elkington, J. and Rowlands, I.H., 1999. Cannibals with forks: The triple bottom line of 21st century business. Alternatives Journal, 25(4), p.42.

[7] Fischer, G. and Herrmann, T., 2011. Socio-technical systems: a meta-design perspective. International Journal of Sociotechnology and Knowledge Development (IJSKD), 3(1), pp.1-33.

[8] Fitzgerald, M., Kruschwitz, N., Bonnet, D. and Welch, M., 2014. Embracing digital technology: A new strategic imperative. MIT sloan management review, 55(2), p.1.

[9] Goodfellow, I., 2016. Deep learning.

[10] Hu, Y., 2018. Marketing and business analysis in the era of big data. American Journal of Industrial and Business Management, 8(07), p.1747.

[11] Javaid, M., Haleem, A., Singh, R.P. and Suman, R., 2022. Artificial intelligence applications for industry 4.0: A literature-based study. Journal of Industrial Integration and Management, 7(01), pp.83-111.

[12] Kalyazina, S., Iliashenko, V., Kozhukhov, Y. and Zotova, E., 2020, December. Key end-to-end digital technologies in the ecosystem of the state’s digital economy. In IOP Conference Series: Materials Science and Engineering (Vol. 1001, No. 1, p. 012140). IOP Publishing.

[13] Konietzko, J., Bocken, N. and Hultink, E.J., 2020. A tool to analyze, ideate and develop circular innovation ecosystems. Sustainability, 12(1), p.417.

[14] Machireddy, J.R., Rachakatla, S.K. and Ravichandran, P., 2021. AI-Driven business analytics for financial forecasting: Integrating data warehousing with predictive models. Journal of Machine Learning in Pharmaceutical Research, 1(2), pp.1-24.

[15] Mavlutova, I., Spilbergs, A., Verdenhofs, A., Natrins, A., Arefjevs, I. and Volkova, T., 2022. Digital transformation as a driver of the financial sector sustainable development: An impact on financial inclusion and operational efficiency. Sustainability, 15(1), p.207.

[16] Moher, D., Liberati, A., Tetzlaff, J. and Altman, D.G., 2009. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Bmj, 339.

[17] Nadkarni, S. and Prügl, R., 2021. Digital transformation: a review, synthesis and opportunities for future research. Management review quarterly, 71, pp.233-341.

[18] Okoli, C., 2015. A guide to conducting a standalone systematic literature review. Communications of the Association for Information Systems, 37.

[19] Olayinka, O.H., 2022. Ethical implications and governance of AI models in business analytics and data science applications. International Journal of Engineering Technology Research & Management.

[20] Park, Y. and Mithas, S., 2020. Organized complexity of digital business strategy: A configurational perspective. MIS quarterly, 44(1).

[21] Rautenbach, W.J., de Kock, I. and Jooste, J.L., 2019, June. The development of a conceptual model for enabling a value-adding digital transformation: A conceptual model that aids organisations in the digital transformation process. In 2019 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) (pp. 1-10). IEEE.

[22] Ravichandran, P., Machireddy, J.R. and Rachakatla, S.K., 2022. AI-Enhanced data analytics for real-time business intelligence: Applications and challenges. Journal of AI in Healthcare and Medicine, 2(2), pp.168-195.

[23] Teece, D.J., 2007. Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance. Strategic management journal, 28(13), pp.1319-1350.

[24] Theofanidis, D. and Fountouki, A., 2018. Limitations and delimitations in the research process. Perioperative Nursing-Quarterly scientific, online official journal of GORNA, 7(3 September-December 2018), pp.155-163.

[25] Thomas, A., 2020. Convergence and digital fusion lead to competitive differentiation. Business Process Management Journal, 26(3), pp.707-720.

[26] Tranfield, D., Denyer, D. and Smart, P., 2003. Towards a methodology for developing evidence‐informed management knowledge by means of systematic review. British journal of management, 14(3), pp.207-222.

[27] Trist, E.L., 1981. The evolution of socio-technical systems (Vol. 2, p. 1981). Toronto: Ontario Quality of Working Life Centre.

[28] Trist, B., Murray, H. and Trist, E., 2016. Characteristics of socio-technical systems. In The Social Engagement of Social Science, a Tavistock Anthology, Volume 2: The Socio-Technical Perspective (pp. 157-186). University of Pennsylvania Press.

[29] Turing, A.M., 2009. Computing machinery and intelligence (pp. 23-65). Springer Netherlands.

[30] Velter, M.G.E., Bitzer, V. and Bocken, N.M.P., 2022. A boundary tool for multi-stakeholder sustainable business model innovation. Circular Economy and Sustainability, 2(2), pp.401-431.

[31] Wamba, S.F., Akter, S., Edwards, A., Chopin, G. and Gnanzou, D., 2015. How ‘big data’can make big impact: Findings from a systematic review and a longitudinal case study. International journal of production economics, 165, pp.234-246.

[32] Yigitcanlar, T., Desouza, K.C., Butler, L. and Roozkhosh, F., 2020. Contributions and risks of artificial intelligence (AI) in building smarter cities: Insights from a systematic review of the literature. Energies, 13(6), p.1473.

@article{1708892,
      author = {Enuma Ezeife, Telma Erebor, Alexandra Buchanan, Raphael Iyitor},
      title = {Synergizing AI and Business Analytics for End-to-End Process Digitization: Frameworks for Sustainable Transformation},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {6},
      number = {5},
      pages = {238-252},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1708892.pdf},
      abstract = {This paper critically reviews the convergence of artificial intelligence (AI) and business analytics as key drivers of end-to-end process digitization, with an emphasis on sustainability imperatives. The study employs a systematic literature review methodology to explore three research questions: the nature of existing frameworks integrating AI and analytics; the critical factors that determine the success of digitization initiatives; and the metrics used to evaluate impacts on sustainable transformation. Drawing from both theoretical and empirical literature, the findings reveal that while numerous frameworks depict staged integration of AI and analytics, few incorporate explicit sustainability targets into their design. Organizational factors such as leadership commitment, data governance, and a culture of continuous learning emerge as pivotal enablers, aligning with social and environmental considerations when guided by clear strategic priorities. At the same time, barriers including data fragmentation, regulatory uncertainties, and skills shortages underscore the complexity of implementing AI?analytics solutions that uphold ethical and ecological standards. In terms of impact evaluation, research increasingly emphasizes holistic metrics that measure economic, social, and ecological performance in unison. This points to a growing need for standardized indicators and adaptive feedback loops that allow organizations to respond promptly to sustainability challenges. Overall, the review underscores the potential of AI-powered analytics to drive robust and responsible process digitization, while also highlighting gaps in current frameworks and measures that must be addressed for truly sustainable outcomes.},
      month = {November},
  }