Review of AI-Driven Solutions in Business Value and Operational Efficiency
Abstract
Introduction. Artificial intelligence (AI) refers to a wide spectrum of breakthroughs that offer multiple advantages to companies in terms of increased sales. Considering the abundance of data and the significant increase in computational resources, organisations have rapidly turned to artificial intelligence (AI) to create financial benefits. Nevertheless, businesses continue to discover it is challenging to implement and employ AI in their everyday activities. Therefore, a comprehensive understanding is required due to the absence of an integrated comprehension of how artificial intelligence creates business value and what kind of corporate worth is anticipated.
Aim and tasks. The study aims to review value-generating methods and explain how enterprises might use AI technology in their business activities. To accomplish its main aims, this study offers a thorough literature review. The working hypothesis claims that the use of AI can increase business value.
Results. This study examines the research capabilities of AI, its use in the corporate environment, and its initial and secondary impacts. The impact of AI includes process efficiency, generating insights hidden in huge amounts of data, and transforming business processes in terms of procedural actions, operational efficiency, financial efficiency, market efficiency, and sustainability in terms of company profile. In addition to the favourable impacts, several recent cases have shown that unwanted and undesired consequences may develop in the absence of effective management procedures. These effects hurt the reputation of the businesses concerned and, in certain cases, resulted in huge fines and financial losses. Such findings increase the responsibility of AI enterprises to incorporate solutions that reduce the bias in data and algorithms at every stage of implementation.
Conclusions. The role of artificial intelligence in the corporate environment in value creation and operational efficiency is extending. AI technologies can be used by companies to increase automation of corporate processes without direct interaction with customers, including applications that mean the use of AI in customer-facing services and products. Learning about the means by which AI might be employed will assist businesses in generating rational choices regarding the strength of implementing technologies in the supply chain. Assessing the possible implications of AI acceptance of artificial intelligence may enable firms to plan more successfully on a technology’s launch.
References
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Balasundaram, S., & Venkatagiri, S. (2020). A structured approach to implementing Robotic Process Automation in HR. Journal of Physics: Conference Series, 1427(1), 012008.
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. An International Journal on Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
Batin, M., & Turchin, A. (2017). Artificial Intelligence in Life Extension: From Deep Learning to Superintelligence. Informatica, 41, 401–417.
Bauer, W., & Vocke, C. (2020). Work in the age of artificial intelligence – challenges and potentials for the design of new forms of human-machine interaction. In Advances in Intelligent Systems and Computing (pp. 493–501). Springer International Publishing.
Bednar, P. M., & Welch, C. (2020). Socio-Technical Perspectives on Smart Working: Creating Meaningful and Sustainable Systems. Information Systems Frontiers, 22(2), 281–298. https://doi.org/10.1007/s10796-019-09921-1
Borges, A. F., Laurindo, F. J., Spínola, M. M., Gonçalves, R. F., & Mattos, C. A. (2020). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International Journal of Information Management, 102225.
Bytniewski, A., Matouk, K., Chojnacka-Komorowska, A., Hernes, M., Zawadzki, A., & Kozina, A. (2020). The functionalities of cognitive technology in management control system. Asian Conference on Intelligent Information and Database Systems
Castillo, D., Canhoto, A. I., & Said, E. (2020). The dark side of AI powered service interactions: exploring the process of codestruction from the customer perspective. The Service Industries Journal, 1–26.
Cheng, X., Bao, Y., & Zarifis, A. (2020a). Investigating the impact of IT-mediated information interruption on emotional exhaustion in the workplace. Information Processing & Management, 57(6), 1–16. https://doi.org/10.1016/j.ipm.2020.102281
Cheng, X., Sun, J., & Zarifis, A. (2020b). Artificial intelligence and deep learning in educational technology research and practice. British Journal of Educational Technology, 51(5), 1653–1656. https://doi.org/10.1111/bjet.13018
Chkhaidze, I., Makharadze, N., & Devadze, L. (2023). Management problems of innovative business projects and ways of its improvement. Economics Ecology Socium, 7(1), 71–83. https://doi.org/10.31520/2616-7107/2023.7.1-7
Choudhary, S. S. (2013). Understanding Captcha: Text and Audio Based Captcha. International Journal of Advanced Research in Computer Science and Software Engineering, 106-115.
Coombs, C., Hislop, D., Taneva, S. K., & Barnard, S. (2020). The strategic impacts of Intelligent Automation for knowledge and service work: An interdisciplinary review. The Journal of Strategic Information Systems, 29(4), 101600.
Dastin, J. (2018). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters.
Davenport, T. H. &Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., … 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), 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Engler, A. (2021). Auditing employment algorithms for discrimination. Retrieved 15/06 from https://www.brookings.edu/research/auditingemployment- algorithms-for-discrimination/
Eriksson, T., Bigi, A., & Bonera, M. (2020). Think with me, or think for me? On the future role of artificial intelligence in marketing strategy formulation. The TQM Journal, 32(4), 795–814.
Finch, G., Goehring, B., & Marshall, A. (2017). The enticing promise of cognitive computing: high-value functional efficiencies and innovative enterprise capabilities. Strategy & Leadership, 45(6), 26–33.
Ford, M. (2018). Architects of intelligence. Packt Publishing.
Fountaine, T., McCarthy, B., & Saleh, T. (2019). Building the AIpowered organization. Harvard Business Review, 97(4), 62–73.
Geldiev, E., Nenkov, N., Petrova, M. (2018). Exercise of Machine Learning Using Some Python Tools and Techniques. CBU International conference proceedings 2018: Innovations in Science and Education, pp.1 062-1070. https://doi.org/10.12955/cbup.v6.1295
George, B. &. (2015). Artificial intelligence simplified: Understanding basic concepts. Bettendorf: CSTrends LLP.
Jelonek, D., Mesjasz-Lech, A., Stępniak, C., Turek, T., & Ziora, L. (2020). The artificial intelligence application in the management of contemporary organization: Theoretical assumptions, current practices and research review. In Lecture Notes in Networks and Systems (pp. 319–327). Springer International Publishing.
Jeronimo, F. (2017). Mobile AI and the Future of Intelligent Devices. IDC. https://cdn.rios.hu/dl/cnt/2017-12/142233/idc_white_paper.pdf
Keding, C. (2020). Understanding the interplay of artificial intelligence and strategic management: four decades of research in review. Management Review Quarterly, 71(1), 91–134.
Khemali, C. D. (2019). Solving Rubik’s cube using graph theory: ICCI-2017. Computational intelligence: Theories, applications and future directions-volume 1, 301-317.
Kirchmer, M., & Franz, P. (2019). Value-driven robotic process automation (RPA): A process-led approach to fast results at minimal risk. In Lecture Notes in Business Information Processing (pp. 31–46). Springer International Publishing. https://doi.org/10.1007/978-3-030-24854-3_3
Laird, J. E. (2009). Claims and challenges in evaluating human-level intelligent systems. Second Conference on Artificial GeneralIntelligence, (pp. 91-96).
Lee, J., Suh, T., Roy, D., & Baucus, M. (2019). Emerging technology and business model innovation: the case of artificial intelligence. Journal of Open Innovation: Technology, Market, and Complexity, 5(3), 44. https://doi.org/10.3390/joitmc5030044
Legg, S., & Veness, J. (2013). An approximation of the universal intelligence measure. In Lecture Notes in Computer Science (pp. 236–249). Springer Berlin Heidelberg.
Lichtenthaler, U. (2019). An intelligence-based view of firm performance: profiting from artificial intelligence. Journal of Innovation Management, 7(1), 7–20. https://doi.org/10.24840/2183-0606_007.001_0002
Littman, M. L.-V. (2021). Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report. https://ai100.stanford.edu/sites/g/files/sbiybj18871/files/media/file/AI100Report_MT_10.pdf
Loyola-Gonzalez, O. (2019). Black-box vs. white-box: Understanding their advantages and weaknesses from a practical point of view. IEEE Access, 7, 154096–154113
Makarius, E. E., Mukherjee, D., Fox, J. D., & Fox, A. K. (2020). Rising with the machines: A sociotechnical framework for bringing artificial intelligence into the organization. Journal of Business Research, 120, 262–273. https://doi.org/10.1016/j.jbusres.2020.07.045
Marcondes, F. S., Almeida, J. J., & Novais, P. (2020). A short survey on chatbot technology: Failure in raising the state of the art. In F. Herrera, K. Matsui, & S. Rodríguez-González (Eds.), Advances in intelligent systems and computing (Vol. 1003, pp. 55–66). Springer. https://doi.org/10.1007/978-3-030-23887-2_4
Marinova, N. (2014). Artificial intelligence systems. Svishtov: Tsenov Publishing House.
Marwala, T. &. (2017). Artificial intelligence and economic theory: Skynet in the market. Springer
Mikalef, P., & Gupta, M. (2021). Artificial Intelligence Capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(2), 103434. https://doi.org/10.1016/j.im.2021.103434
Mishra, A. N., & Pani, A. K. (2020). Business value appropriation roadmap for artificial intelligence. VINE Journal of Information and Knowledge Management Systems, 51(3), 353–368
Mitchell, M. (2019). Artificial intelligence: A guide for thinking humans. New York: Farrar, Straus and Giroux.
Stieglitz, S., & Dang-Xuan, L. (2020). The impact of artificial intelligence on event experiences: A scenario technique approach. Electronic Markets, 30(1), 55-69. https://doi.org/10.1007/s12525-020-00433-4
Petrova, M., Sushchenko, O., Trunina, I., Dekhtyar, N. (2018). Big Data Tools in Processing Information from Open Sources. IEEE First International Conference on System Analysis & Intelligent Computing (SAIC-2018), pp. 256–260. https://doi.org/10.1109/SAIC.2018.8516-800
Picard, R. W. (1995). Advancing human wellbeing by developing new ways to communicate, understand, and respond to emotion. https://affect.media.mit.edu/pdfs/95.picard.pdf
Poole, D. L. (2017). Artificial Intelligence: Foundations of computational agents. Cambridge: Cambridge University Printing House.
Rajput, S., & Singh, S. P. (2019). Connecting Circular Economy and Industry 4.0. International Journal of Information Management, 49, 98–113.
Ransbotham, S., Gerbert, P., Reeves, M., Kiron, D., & Spira, M. (2018). Artificial intelligence in business gets real. MIT Sloan Management Review.
Ransbotham, S., Kiron, D., Gerbert, P., & Reeves,M. (2017). Reshaping business with artificial intelligence: Closing the gap between ambition and action. MIT Sloan Management Review and The Boston Consulting Group.
Riikkinen, M., Saarijärvi, H., Sarlin, P., & Lähteenmäki, I. (2018). Using artificial intelligence to create value in insurance. International Journal of Bank Marketing, 36(6), 1145–1168. https://doi.org/10.1108/IJBM-01-2017-0015
Russell, S. (2019). Human compatible: Artificial intelligence and the problem of control. New York: Viking.
Russell, S., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach. Addison Wesley.
Samek,W., & Muller, K. R. (2019). Towards explainable artificial intelligence. In: Explainable AI: interpreting, explaining and visualizing deep learning (pp. 5–22). Springer
Schmidt, R., Zimmermann, A.,Moehring, M., & Keller, B. (2020). Value creation in connectionist artificial intelligence–A research agenda. AMCIS 2020 Proceedings, 14.
Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2019). Organizational Decision-Making Structures in the Age of Artificial Intelligence. California Management Review, 61(4), 66–83. https://doi.org/10.1177/0008125619862257
Siau, K., & Wang, W. (2018). Building Trust in Artificial Intelligence, Machine Learning, and Robotics. Cutter Business Technology Journal, 31(2), 47–53.
Slyusar, V. (2019). Artificial intelligence as the basis of future control networks. VII International Scientific and Practical Conference, pp. 76 - 77.
Templier, M. & Paré, G. (2015).Aframework for guiding and evaluating literature reviews. Communications of the Association for Information Systems, 37(1), 6
Toniolo, K., Masiero, E., Massaro, M.,& Bagnoli, C. (2020). Sustainable business models and artificial intelligence: Opportunities and challenges. In: In Knowledge, People, and Digital Transformation (pp. 103–117). Springer.
Vigdor, N. (2019). Apple card investigated after gender discrimination complaints. The New York Times. Retrieved 15/06 from https:// www.nytimes.com/2019/11/10/business/Apple-credit-cardinvestigation. html
Wamba-Taguimdje, S. L., Wamba, S. F., Kamdjoug, J. R. K., &Wanko, C. E. T. (2020). Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893–1924
Wang, H., Huang, J., & Zhang, Z. (2019). The impact of deep learning on organizational agility. In proceedings of the 40th International Conference on Information Systems (ICIS), Munich, Germany
Wang, W., & Siau, K. (2019). Artificial Intelligence, Machine Learning, Automation, Robotics, Future of Work and Future of Humanity: A Review and Research Agenda. Journal of Database Management, 30(1), 61–79. https://doi.org/10.4018/JDM.2019010104
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Published
2024-09-30
How to Cite
(1)
Tairov, I.; Stefanova, N.; Aleksandrova, A.; Aleksandrov, M. Review of AI-Driven Solutions in Business Value and Operational Efficiency. Economics Ecology Socium 2024, 8, 55-66.
Section
Articles
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