Main Article Content
Abstract
This article reviews the challenges and opportunities associated with integrating Artificial intelligence (AI) into business operations through the lens of Dynamic Capabilities Theory (DCT). Artificial intelligence is becoming a pivotal tool for enhancing organizational efficiency and driving innovation across industries. In this literature review, the author examines how businesses can effectively implement AI to improve decision-making, productivity, and customer experience while addressing data privacy, algorithmic bias, and ethical implications. The paper highlights the relevance of DCT, which emphasizes the importance of sensing, seizing, and transforming capabilities in navigating these complexities. While AI offers substantial benefits, its integration is fraught with challenges that require organizations to strategically adapt their structures, processes, and skills. The article concludes by underscoring the importance of developing ethical frameworks, investing in workforce reskilling, and enhancing dynamic capabilities to ensure the successful adoption of AI. These insights provide valuable guidance for business leaders seeking to leverage AI to achieve sustainable growth and competitive advantage.
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Copyright (c) 2025 Jeanette Owusu, Isaac Sardello Agbesi

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
References
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- Bharadiya, J. (2023). The impact of artificial intelligence on business processes. European Journal of Technology. https://doi.org/10.47672/ejt.1488
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- Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence: What it can — and cannot — do for your organization. Harvard Business Review. Retrieved from https://hbr.org/2017/07/the-business-of-artificial-intelligence
- Chui, M., Manyika, J., & Miremadi, M. (2018). What AI can and can’t do (yet) for your business. McKinsey Quarterly, 1(97-108), 1.
- Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116. https://hbr.org/2018/01/artificial-intelligence-for-the-real-world
- Dogru, A. K., & Keskin, B. (2020). AI in operations management: Applications, challenges, and opportunities. Journal of Data, Information and Management, 2(1), 67-74. https://doi.org/10.1007/s42488-020-00023-1
- Dwivedi, Y. K., Hughes, D. L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dvivedi, Rohita, Edwards, J., Eirug, A., Galanos, V., Vigneswara Ilavarasan, P., Janssen, M., Jones, P., Kumar Kar, A., Hatice Kizgin, H, Kronemann, B., Lal, B., Lucini, B., Medaglia, R., & 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
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- Govori, A., & Sejdija, Q. (2023). Future prospects and challenges of integrating artificial intelligence within the business practices of small and medium enterprises. Journal of Governance and Regulation. 10. https://doi.org/10.22495/jgrv12i2art16
- Gupta, D. V. (2023). Recent Advancements in Computer Science: A Comprehensive Review of Emerging Technologies and Innovations. International Journal for Research Publication and Seminar 14(1), 329-334.
- Han, R., Lam, H. K. S., Zhan, Y., Wang, Y., & Dwivedi, Y. K. (2021). Artificial intelligence in business-to-business marketing: A bibliometric analysis of current research status, development, and future directions. Industrial Management & Data Systems, 121(9), 2467-2497. https://doi.org/10.1108/imds-05-2021-0300
- Iyer, L. (2021). AI enabled applications towards intelligent transportation. Transport Engineering, 5, 100083. https://doi.org/10.1016/J.TRENG.2021.100083
- Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399. https://doi.org/10.1038/s42256-019-0088-2
- Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine, 17, 195. https://doi.org/10.1186/s12916-019-1426-2
- Lichtenthaler, U. (2020). Agile innovation: The role of dynamic capabilities in implementing digital technologies. Journal of Business Research, 123, 120-136. https://doi.org/10.1016/j.jbusres.2020.09.045
- Loureiro, S., Guerreiro, J., & Tussyadiah, I. P. (2020). Artificial intelligence in business: State of the art and future research agenda. Journal of Business Research, 117, 1-10. https://doi.org/10.1016/j.jbusres.2020.11.001
- Manyika, J., Chui, M., Miremadi, M., Bughin, J., George, K., Willmott, P., & Dewhurst, M. (2017). A future that works: Automation, employment, and productivity. McKinsey Global Institute. Retrieved from https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/a-future-that-works-automation-employment-and-productivity
- Modhoriye, P., Yadav, P., & Jadhav, S. (2023). AI transformation in business: Unveiling the dual effects of advancement and challenges. International Journal of Scientific Research in Engineering and Management, 6(9), 73-82. https://doi.org/10.55041/ijsrem27359
- Mohammad, S. M. (2020). Artificial Intelligence in Information Technology. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3625444
- Mondal, B. (2020). Artificial intelligence: state of the art. Recent trends and advances in artificial intelligence and internet of things, Springer . 389-425.
- Nigmatov, A., & Pradeep, A. (2023). The impact of AI on business: Opportunities, risks, and challenges. 2023 13th International Conference on Advanced Computer Information Technologies (ACIT), 618-622. https://doi.org/10.1109/ACIT58437.2023.10275510
- Norori, N., Hu, Q., Aellen, F., Faraci, F., & Tzovara, A. (2021). Addressing bias in big data and AI for health care: A call for open science. Patterns, 2(6), 100347. https://doi.org/10.1016/j.patter.2021.100347
- Ntoutsi, E., Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., Vidal, M. E., Salvatore Ruggieri, Franco Turini, Symeon Papadopoulos, Emmanouil Krasanakis, Ioannis Kompatsiaris, Katharina Kinder-Kurlanda, Claudia Wagner, Fariba Karimi, Miriam Fernandez, Harith Alani, Bettina Berendt, Tina Kruegel, Christian Heinze, Klaus Broelemann, Gjergji Kasneci, Thanassis Tiropanis, & Staab, S. (2020). Bias in data‐driven artificial intelligence systems—An introductory survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1356. https://doi.org/10.1002/widm.1356
- Patel, P., & Thakkar, A. (2020). The upsurge of deep learning for computer vision applications. International Journal of Electrical and Computer Engineering, 10(1), 538-548. https://doi.org/10.11591/IJECE.V10I1.PP538-548
- Paul, S., Daga, V., Gupta, T., & Aishwarya, S. (2023). A study on the impact of artificial intelligence in small and medium enterprises. International Journal for Multidisciplinary Research. https://doi.org/10.36948/ijfmr.2023.v05i06.11145
- Perifanis, N. A., & Kitsios, F. (2023). Investigating the influence of artificial intelligence on business value in the digital era of strategy: A literature review. Information, 14(2), 85.
- Rai, A. (2020). Explainable AI: From black box to glass box. Journal of the Academy of Marketing Science, 48(1), 137-141. https://doi.org/10.1007/s11747-019-00710-5
- Ransbotham, S., Fichman, R. G., Gopal, R., & Gupta, A. (2018). Research commentary—Ubiquitous IT, invisible IT, and the future of IT research. Information Systems Research, 29(1), 1-7. https://doi.org/10.1287/isre.2018.0771
- Rubab, S. A. (2023). Impact of AI on business growth. The Business and Management Review, 14(2). https://doi.org/10.24052/bmr/v14nu02/art-24
- Stoykova, S., & Shakev, N. (2023). Artificial intelligence for management information systems: Opportunities, challenges, and future directions. Algorithms, 16(8), 357. https://doi.org/10.3390/a16080357
- Tambe, P., Cappelli, P., & Yakubovich, V. (2020). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15-42. https://doi.org/10.1177/0008125619867910
- Teece, D. J., Peteraf, M., & Leih, S. (2019). Dynamic capabilities and organizational agility: Risk, uncertainty, and strategy in the innovation economy. California Management Review, 61(2), 134-156. https://doi.org/10.1177/0008125618790246
- Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889-901. https://doi.org/10.1016/j.jbusres.2019.09.022
- Wang, Y. (2022). Using Machine Learning and Natural Language Processing to Analyze Library Chat Reference Transcripts. Information Technology and Libraries, 41(3). https://doi.org/10.6017/ital.v41i3.14967
- Warner, K. S. R., & Wäger, M. (2019). Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Planning, 52(3), 326-349. https://doi.org/10.1016/j.lrp.2018.12.001
- Xu, Z. (2023). Research on the application of artificial intelligence in the library sector. Proceedings of SPIE - The International Society for Optical Engineering, 126105U-10. https://doi.org/10.1117/12.2671477
References
Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., & Song, Y. (2021). Artificial intelligence in sustainable energy industry: Status quo, challenges and opportunities. Journal of Cleaner Production, 289, 125834. https://doi.org/10.1016/j.jclepro.2021.125834
Alet, J. (2023). Effective integration of artificial intelligence: Key axes for business strategy. Journal of Business Strategy. https://doi.org/10.1108/jbs-01-2023-0005
Bharadiya, J. (2023). The impact of artificial intelligence on business processes. European Journal of Technology. https://doi.org/10.47672/ejt.1488
Bharati, S. (2020). How Artificial Intelligence Impacts Businesses in the Period of Pandemics. Journal of the International Academy of Case Studies, 26(1), 1-2. https://consensus.app/papers/artificial-intelligence-impacts-businesses-period-bharati/ea7864c1bc3a5dfe82b49e3b0fecc246 /
Bhima, B., Zahra, A. R. A., & Nurtino, T. (2023). Enhancing organizational efficiency through the integration of artificial intelligence in management information systems. APTISI Transactions on Management (ATM). https://doi.org/10.33050/atm.v7i3.2146
Borges, A. F. S., Laurindo, F., Spínola, M., Gonçalves, R. F., & Mattos, C. (2020). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International Journal of Information Management, 57, 102225. https://doi.org/10.1016/J.IJINFOMGT.2020.102225
Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence: What it can — and cannot — do for your organization. Harvard Business Review. Retrieved from https://hbr.org/2017/07/the-business-of-artificial-intelligence
Chui, M., Manyika, J., & Miremadi, M. (2018). What AI can and can’t do (yet) for your business. McKinsey Quarterly, 1(97-108), 1.
Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116. https://hbr.org/2018/01/artificial-intelligence-for-the-real-world
Dogru, A. K., & Keskin, B. (2020). AI in operations management: Applications, challenges, and opportunities. Journal of Data, Information and Management, 2(1), 67-74. https://doi.org/10.1007/s42488-020-00023-1
Dwivedi, Y. K., Hughes, D. L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dvivedi, Rohita, Edwards, J., Eirug, A., Galanos, V., Vigneswara Ilavarasan, P., Janssen, M., Jones, P., Kumar Kar, A., Hatice Kizgin, H, Kronemann, B., Lal, B., Lucini, B., Medaglia, R., & 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
Farayola, O. A., Abdul, A. A., Irabor, B. O., & Okeleke, E. C. (2023). Innovative business models driven by AI technologies: A review. Computer Science & IT Research Journal. https://doi.org/10.51594/csitrj.v4i2.608
Govori, A., & Sejdija, Q. (2023). Future prospects and challenges of integrating artificial intelligence within the business practices of small and medium enterprises. Journal of Governance and Regulation. 10. https://doi.org/10.22495/jgrv12i2art16
Gupta, D. V. (2023). Recent Advancements in Computer Science: A Comprehensive Review of Emerging Technologies and Innovations. International Journal for Research Publication and Seminar 14(1), 329-334.
Han, R., Lam, H. K. S., Zhan, Y., Wang, Y., & Dwivedi, Y. K. (2021). Artificial intelligence in business-to-business marketing: A bibliometric analysis of current research status, development, and future directions. Industrial Management & Data Systems, 121(9), 2467-2497. https://doi.org/10.1108/imds-05-2021-0300
Iyer, L. (2021). AI enabled applications towards intelligent transportation. Transport Engineering, 5, 100083. https://doi.org/10.1016/J.TRENG.2021.100083
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399. https://doi.org/10.1038/s42256-019-0088-2
Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine, 17, 195. https://doi.org/10.1186/s12916-019-1426-2
Lichtenthaler, U. (2020). Agile innovation: The role of dynamic capabilities in implementing digital technologies. Journal of Business Research, 123, 120-136. https://doi.org/10.1016/j.jbusres.2020.09.045
Loureiro, S., Guerreiro, J., & Tussyadiah, I. P. (2020). Artificial intelligence in business: State of the art and future research agenda. Journal of Business Research, 117, 1-10. https://doi.org/10.1016/j.jbusres.2020.11.001
Manyika, J., Chui, M., Miremadi, M., Bughin, J., George, K., Willmott, P., & Dewhurst, M. (2017). A future that works: Automation, employment, and productivity. McKinsey Global Institute. Retrieved from https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/a-future-that-works-automation-employment-and-productivity
Modhoriye, P., Yadav, P., & Jadhav, S. (2023). AI transformation in business: Unveiling the dual effects of advancement and challenges. International Journal of Scientific Research in Engineering and Management, 6(9), 73-82. https://doi.org/10.55041/ijsrem27359
Mohammad, S. M. (2020). Artificial Intelligence in Information Technology. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3625444
Mondal, B. (2020). Artificial intelligence: state of the art. Recent trends and advances in artificial intelligence and internet of things, Springer . 389-425.
Nigmatov, A., & Pradeep, A. (2023). The impact of AI on business: Opportunities, risks, and challenges. 2023 13th International Conference on Advanced Computer Information Technologies (ACIT), 618-622. https://doi.org/10.1109/ACIT58437.2023.10275510
Norori, N., Hu, Q., Aellen, F., Faraci, F., & Tzovara, A. (2021). Addressing bias in big data and AI for health care: A call for open science. Patterns, 2(6), 100347. https://doi.org/10.1016/j.patter.2021.100347
Ntoutsi, E., Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., Vidal, M. E., Salvatore Ruggieri, Franco Turini, Symeon Papadopoulos, Emmanouil Krasanakis, Ioannis Kompatsiaris, Katharina Kinder-Kurlanda, Claudia Wagner, Fariba Karimi, Miriam Fernandez, Harith Alani, Bettina Berendt, Tina Kruegel, Christian Heinze, Klaus Broelemann, Gjergji Kasneci, Thanassis Tiropanis, & Staab, S. (2020). Bias in data‐driven artificial intelligence systems—An introductory survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1356. https://doi.org/10.1002/widm.1356
Patel, P., & Thakkar, A. (2020). The upsurge of deep learning for computer vision applications. International Journal of Electrical and Computer Engineering, 10(1), 538-548. https://doi.org/10.11591/IJECE.V10I1.PP538-548
Paul, S., Daga, V., Gupta, T., & Aishwarya, S. (2023). A study on the impact of artificial intelligence in small and medium enterprises. International Journal for Multidisciplinary Research. https://doi.org/10.36948/ijfmr.2023.v05i06.11145
Perifanis, N. A., & Kitsios, F. (2023). Investigating the influence of artificial intelligence on business value in the digital era of strategy: A literature review. Information, 14(2), 85.
Rai, A. (2020). Explainable AI: From black box to glass box. Journal of the Academy of Marketing Science, 48(1), 137-141. https://doi.org/10.1007/s11747-019-00710-5
Ransbotham, S., Fichman, R. G., Gopal, R., & Gupta, A. (2018). Research commentary—Ubiquitous IT, invisible IT, and the future of IT research. Information Systems Research, 29(1), 1-7. https://doi.org/10.1287/isre.2018.0771
Rubab, S. A. (2023). Impact of AI on business growth. The Business and Management Review, 14(2). https://doi.org/10.24052/bmr/v14nu02/art-24
Stoykova, S., & Shakev, N. (2023). Artificial intelligence for management information systems: Opportunities, challenges, and future directions. Algorithms, 16(8), 357. https://doi.org/10.3390/a16080357
Tambe, P., Cappelli, P., & Yakubovich, V. (2020). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15-42. https://doi.org/10.1177/0008125619867910
Teece, D. J., Peteraf, M., & Leih, S. (2019). Dynamic capabilities and organizational agility: Risk, uncertainty, and strategy in the innovation economy. California Management Review, 61(2), 134-156. https://doi.org/10.1177/0008125618790246
Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889-901. https://doi.org/10.1016/j.jbusres.2019.09.022
Wang, Y. (2022). Using Machine Learning and Natural Language Processing to Analyze Library Chat Reference Transcripts. Information Technology and Libraries, 41(3). https://doi.org/10.6017/ital.v41i3.14967
Warner, K. S. R., & Wäger, M. (2019). Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Planning, 52(3), 326-349. https://doi.org/10.1016/j.lrp.2018.12.001
Xu, Z. (2023). Research on the application of artificial intelligence in the library sector. Proceedings of SPIE - The International Society for Optical Engineering, 126105U-10. https://doi.org/10.1117/12.2671477