Artificial Intelligence and Machine Learning as Business Tools: A Framework for Diagnosing Value Destruction Potential

Authors

  • Shilpa Pathak Thakur, Sridevi R, Ashulekha Gupta, Gunjan Sharma, A. Deepak, Arun Pratap Srivastava, Akhilesh Kumar Khan, Anurag Shrivastava

Keywords:

Artificial Intelligence, Machine Learning, Value Creation, Value Destruction, Business Innovation

Abstract

Machine learning (ML) and artificial intelligence (AI) have the ability to save expenses and increase the efficacy of corporate operations. On the other hand, they also have the capacity to devalue a company's assets, which may sometimes have extremely catastrophic effects. It's possible that some managers won't accept new technologies because they can't fully understand and effectively manage the risks associated with doing so. This will prevent them from realising their maximum potential. The findings of this study provide a fresh paradigm for detecting and limiting the value-reducing potential of artificial intelligence and machine learning for businesses. In addition to outlining the components of an AI solution, this research also recommends this paradigm. The paradigm might be used to map the components of an artificial intelligence system. The concepts of value-generation process and content are then used to illustrate how the aforementioned dangers have the potential to obstruct the creation of value or even result in the loss of that value. In the interest of shedding some light on the topic of the commercial activation of artificial intelligence, this study does an in-depth and careful examination of the existing body of literature on the topic. In addition to that, a clear and succinct explanation of what constitutes artificial intelligence at the present time will be provided. The Implications, Applications, and Methods model (also known as the IAM model) has uncovered a total of six topics that are associated with these three primary topics of discussion. It is possible that academics and practitioners will find our study beneficial in that it provides an overview of the body of knowledge and research agenda. This will allow for artificial intelligence to be used as a strong facilitator in the process of producing business value.

Downloads

Download data is not yet available.

References

Airoldi, E. M., Blei, D. M., Fienberg, S. E., & Xing, E. P. (2008). Mixed membership stochastic blockmodels. Journal of machine learning research, 9, 1981-2014.

Buhalis, D., Harwood, T., Bogicevic, V., Viglia, G., Beldona, S., & Hofacker, C. (2019). Technological disruptions in services: lessons from tourism and hospitality. Journal of Service Management, 30 (4), 484-506.

Davenport, T. H. (2018). From analytics to artificial intelligence. Journal of Business Analytics, 1(2), 73-80.

Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24-42.

De Mauro, A., Greco, M., & Grimaldi, M. (2019). Understanding Big Data through a systematic literature review: The ITMI model. International Journal of Information Technology & Decision Making, 18(04), 1433-1461.

Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data consumer analytics and the transformation of marketing. Journal of business research, 69(2), 897-904.

Gatouillat, A., Badr, Y., Massot, B., Sejdic, E. (2018). Internet of medical things: a review of recent contributions dealing with cyber–physical systems in medicine. IEEE Internet of Things Journal, 5(5), 3810–3822.

Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72-80.

Johnson, K. W., Soto, J. T., Glicksberg, B. S., Shameer, K., Miotto, R., Ali, M., & Dudley, J. T. (2018). Artificial intelligence in cardiology. Journal of the American College of Cardiology, 71(23), 2668-2679.

Kumar, K., & Thakur, G. S. M. (2012). Advanced applications of neural networks and artificial intelligence: A review. International journal of information technology and computer science, 4(6), 57.

Neha Sharma, P. William, Kushagra Kulshreshtha, Gunjan Sharma, Bhadrappa Haralayya, Yogesh Chauhan, Anurag Shrivastava, “Human Resource Management Model with ICT Architecture: Solution of Management & Understanding of Psychology of Human Resources and Corporate Social Responsibility”, JRTDD, vol. 6, no. 9s(2), pp. 219–230, Aug. 2023.

William, P., Shrivastava, A., Chauhan, P.S., Raja, M., Ojha, S.B., Kumar, K. (2023). Natural Language Processing Implementation for Sentiment Analysis on Tweets. In: Marriwala, N., Tripathi, C., Jain, S., Kumar, D. (eds) Mobile Radio Communications and 5G Networks. Lecture Notes in Networks and Systems, vol 588. Springer, Singapore. https://doi.org/10.1007/978-981-19-7982-8_26

K. Maheswari, P. William, Gunjan Sharma, Firas Tayseer Mohammad Ayasrah, Ahmad Y. A. Bani Ahmad, Gowtham Ramkumar, Anurag Shrivastava, “Enterprise Human Resource Management Model by Artificial Intelligence to Get Befitted in Psychology of Consumers Towards Digital Technology”, JRTDD, vol. 6, no. 10s(2), pp. 209–220, Sep. 2023.

Anurag Shrivastava, S. J. Suji Prasad, Ajay Reddy Yeruva, P. Mani, Pooja Nagpal & Abhay Chaturvedi (2023): IoT Based RFID Attendance Monitoring System of Students using Arduino ESP8266 & Adafruit.io on Defined Area, Cybernetics and Systems.

William, G. R. Lanke, D. Bordoloi, A. Shrivastava, A. P. Srivastavaa and S. V. Deshmukh, "Assessment of Human Activity Recognition based on Impact of Feature Extraction Prediction Accuracy," 2023 4th International Conference on Intelligent Engineering and Management (ICIEM), London, United Kingdom, 2023, pp. 1-6, doi: 10.1109/ICIEM59379.2023.10166247

Mariani, M., & Fosso Wamba, S. (2020). Exploring how consumer goods companies innovate in the digital age: The role of big data analytics companies. Journal of Business Research, 121, 338–352. Mayring, P. (2008). Qualitative Inhalts analyse (p. 6). Beltz Deutscher Studien Verlag.

Nguyen, B., & Simkin, L. (2017). The Internet of Things (IoT) and marketing: The state of play, future trends and the implications for marketing. Journal of Marketing Management, 33(1–2), 1–6.

Shrivastava, A., Chakkaravarthy, M., Shah, M.A., A new machine learning method for predicting systolic and diastolic blood pressure using clinical characteristics. In Healthcare Analytics, 2023, 4, 10021

Shrivastava, A., Chakkaravarthy, M., Shah, M.A.,Health Monitoring based Cognitive IoT using Fast Machine Learning Technique. In International Journal of Intelligent Systems and Applications in Engineering, 2023, 11(6s), pp. 720–72

Shrivastava, A., Rajput, N., Rajesh, P., Swarnalatha, S.R., IoT-Based Label Distribution Learning Mechanism for Autism Spectrum Disorder for Healthcare Application. In Practical Artificial Intelligence for Internet of Medical Things: Emerging Trends, Issues, and Challenges, 2023, pp. 305–321

Downloads

Published

26.03.2024

How to Cite

Shilpa Pathak Thakur, Sridevi R, Ashulekha Gupta, Gunjan Sharma, A. Deepak, Arun Pratap Srivastava, Akhilesh Kumar Khan, Anurag Shrivastava. (2024). Artificial Intelligence and Machine Learning as Business Tools: A Framework for Diagnosing Value Destruction Potential. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 562–574. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5452

Issue

Section

Research Article