Ethical and Organizational Dimensions of AI in Strategic Innovation
DOI:
https://doi.org/10.61978/novatio.v2i1.999Keywords:
Artificial Intelligence, Strategic Innovation Management, Predictive Analytics, Dynamic Capabilities, Business Model Innovation, Sustainable Competitive AdvantageAbstract
Artificial Intelligence (AI) has emerged as a transformative force in strategic innovation management, reshaping decision-making, competitive advantage, and long-term strategies. This narrative review aims to synthesize evidence on AI’s role in fostering innovation while addressing the challenges of its adoption. Literature was systematically retrieved from major academic databases including Scopus, PubMed, and Google Scholar, using targeted keywords such as Artificial Intelligence, Strategic Innovation Management, Predictive Analytics, and Dynamic Capabilities. Inclusion criteria prioritized peer-reviewed studies published between 2014 and 2025 in English, with exclusions applied to non-empirical and non-accredited sources. The findings reveal that AI enhances organizational competitiveness through predictive analytics, optimizes innovation processes in the Fuzzy Front End, and supports long-term strategies when integrated with blockchain and the Internet of Things. Comparative perspectives demonstrate that developed countries leverage robust infrastructure for rapid adoption, while developing nations increasingly use AI-driven mobile solutions to overcome traditional barriers. However, persistent challenges including privacy concerns, data security risks, and algorithmic bias threaten equitable adoption, with organizational dynamic capabilities emerging as crucial determinants of success. Policy implications emphasize the need for regulatory frameworks, investment in digital infrastructure, and workforce reskilling. Future research should address underexplored contexts such as small and medium-sized enterprises and conduct longitudinal studies to assess AI’s enduring impact on organizational resilience. Overall, AI’s transformative potential can only be realized through responsible, context-sensitive, and ethically informed strategies that balance innovation with sustainability.
References
Al-Ayed, S. (2025). Role of artificial intelligence in human resource to achieve sustainable organizational performance. International Journal of Innovative Research and Scientific Studies, 8(1), 1613-1621. https://doi.org/10.53894/ijirss.v8i1.4709 DOI: https://doi.org/10.53894/ijirss.v8i1.4709
Albannai, N., Raziq, M., Malik, M., & Abrar, A. (2025). Future trends in digital leadership: cultivating innovation, leveraging ai and achieving competitive advantage: a qualitative study. Journal of Information & Knowledge Management. https://doi.org/10.1142/s0219649225500443 DOI: https://doi.org/10.1142/S0219649225500443
Alhyasat, W., Alhyasat, E., & Khattab, S. (2025). Technology trends in strategic management in the ai era: systematic literature review. Human Systems Management. https://doi.org/10.1177/01672533251322396 DOI: https://doi.org/10.1177/01672533251322396
Bhimavarapu, U. (2025). Ai-driven leadership transformation., 305-328. https://doi.org/10.4018/979-8-3693-7698-0.ch011 DOI: https://doi.org/10.4018/979-8-3693-7698-0.ch011
Coleman, S., Göb, R., Manco, G., Pievatolo, A., Tort‐Martorell, X., & Reis, M. (2016). How can smes benefit from big data? challenges and a path forward. Quality and Reliability Engineering International, 32(6), 2151-2164. https://doi.org/10.1002/qre.2008 DOI: https://doi.org/10.1002/qre.2008
Damian, A., Piciu, L., Turlea, S., & Țăpuș, N. (2019). Advanced customer activity prediction based on deep hierarchic encoder-decoders., 403-409. https://doi.org/10.1109/cscs.2019.00074 DOI: https://doi.org/10.1109/CSCS.2019.00074
Evangeline, S. (2025). Ai for business management., 411-426. https://doi.org/10.4018/979-8-3373-0608-7.ch018 DOI: https://doi.org/10.4018/979-8-3373-0608-7.ch018
Füller, J., Tekić, Ž., & Hutter, K. (2024). Rethinking innovation management—how ai is changing the way we innovate. The Journal of Applied Behavioral Science, 60(4), 603-612. https://doi.org/10.1177/00218863241287323 DOI: https://doi.org/10.1177/00218863241287323
Gudigantala, N., Madhavaram, S., & Bicen, P. (2023). An ai decision‐making framework for business value maximization. Ai Magazine, 44(1), 67-84. https://doi.org/10.1002/aaai.12076 DOI: https://doi.org/10.1002/aaai.12076
Gupta, H., & Kaur, J. (2025). Optimizing hr operations with generative ai., 195-218. https://doi.org/10.4018/979-8-3693-7026-1.ch008 DOI: https://doi.org/10.4018/979-8-3693-7026-1.ch008
Jin, K., Zhong, Z., & Zhao, E. (2024). Sustainable digital marketing under big data: an ai random forest model approach. Ieee Transactions on Engineering Management, 71, 3566-3579. https://doi.org/10.1109/tem.2023.3348991 DOI: https://doi.org/10.1109/TEM.2023.3348991
Kalkan, G. (2024). The impact of artificial intelligence on corporate governance. Journal of Corporate Finance Research / Корпоративные Финансы | Issn 2073-0438, 18(2), 17-25. https://doi.org/10.17323/j.jcfr.2073-0438.18.2.2024.17-25 DOI: https://doi.org/10.17323/j.jcfr.2073-0438.18.2.2024.17-25
Kyambade, M., & Namatovu, A. (2025). Health-care leaders’ perspectives on ai implementation in uganda: overcoming barriers, driving innovation and strategic considerations. Leadership in Health Services, 38(3), 442-463. https://doi.org/10.1108/lhs-02-2025-0025 DOI: https://doi.org/10.1108/LHS-02-2025-0025
Lauri, C., Shimpo, F., & Sokołowski, M. (2023). Artificial intelligence and robotics on the frontlines of the pandemic response: the regulatory models for technology adoption and the development of resilient organisations in smart cities. Journal of Ambient Intelligence and Humanized Computing, 14(11), 14753-14764. https://doi.org/10.1007/s12652-023-04556-2 DOI: https://doi.org/10.1007/s12652-023-04556-2
Lee, J., Azamfar, M., Singh, J., & Siahpour, S. (2020). Integration of digital twin and deep learning in cyber‐physical systems: towards smart manufacturing. Iet Collaborative Intelligent Manufacturing, 2(1), 34-36. https://doi.org/10.1049/iet-cim.2020.0009 DOI: https://doi.org/10.1049/iet-cim.2020.0009
Liu, J., Cheng, C., Cheng, T., Liu, C., Chen, J., & Hao, W. (2024). Unveiling the potential: remote monitoring and telemedicine in shaping the future of heart failure management. Life, 14(8), 936. https://doi.org/10.3390/life14080936 DOI: https://doi.org/10.3390/life14080936
Luqman, A., Zhang, Q., Talwar, S., Bhatia, M., & Dhir, A. (2024). Artificial intelligence and corporate carbon neutrality: a qualitative exploration. Business Strategy and the Environment, 33(5), 3986-4003. https://doi.org/10.1002/bse.3689 DOI: https://doi.org/10.1002/bse.3689
Maqousi, A., Aldweesh, A., & Al–Qerem, A. (2025). Generative ai in supply chain security., 465-486. https://doi.org/10.4018/979-8-3373-0832-6.ch020 DOI: https://doi.org/10.4018/979-8-3373-0832-6.ch020
Menzies, J., Sabert, B., Hassan, R., & Mensah, P. (2024). Artificial intelligence for international business: its use, challenges, and suggestions for future research and practice. Thunderbird International Business Review, 66(2), 185-200. https://doi.org/10.1002/tie.22370 DOI: https://doi.org/10.1002/tie.22370
Mili, K., Bengana, I., Rahma, Z., & Mekimah, S. (2025). From code to quality: how ai is transforming quality management in algerian startups. International Journal of Innovative Research and Scientific Studies, 8(1), 1770-1776. https://doi.org/10.53894/ijirss.v8i1.4802 DOI: https://doi.org/10.53894/ijirss.v8i1.4802
Petersson, L., Larsson, I., Nygren, J., Nilsén, P., Neher, M., Reed, J., … & Svedberg, P. (2022). Challenges to implementing artificial intelligence in healthcare: a qualitative interview study with healthcare leaders in sweden. BMC Health Services Research, 22(1). https://doi.org/10.1186/s12913-022-08215-8 DOI: https://doi.org/10.1186/s12913-022-08215-8
Proto, S., Corso, E., Apiletti, D., Cagliero, L., Cerquitelli, T., Malnati, G., … & Mazzucchi, D. (2020). Redtag: a predictive maintenance framework for parcel delivery services. Ieee Access, 8, 14953-14964. https://doi.org/10.1109/access.2020.2966568 DOI: https://doi.org/10.1109/ACCESS.2020.2966568
Rajagopal, N., Qureshi, N., Durga, S., Ramirez-Asís, E., Huerta-Soto, R., Gupta, S., … & Deepak, S. (2022). Future of business culture: an artificial intelligence‐driven digital framework for organization decision‐making process. Complexity, 2022(1). https://doi.org/10.1155/2022/7796507 DOI: https://doi.org/10.1155/2022/7796507
Rekunenko, І., Кобушко, Я., Dzydzyguri, O., Balahurovska, I., Yurynets, O., & Жук, О. (2025). The use of artificial intelligence in public administration: bibliometric analysis. Problems and Perspectives in Management, 23(1), 209-224. https://doi.org/10.21511/ppm.23(1).2025.16 DOI: https://doi.org/10.21511/ppm.23(1).2025.16
Roy, S., Dey, B., Brown, D., Abid, A., Apostolidis, C., Christofi, M., … & Tarba, S. (2025). Business model innovation through ai adaptation: the role of strategic human resources management. British Journal of Management, 36(2), 546-559. https://doi.org/10.1111/1467-8551.12894 DOI: https://doi.org/10.1111/1467-8551.12894
Santamato, V., Tricase, C., Faccilongo, N., Iacoviello, M., & Marengo, A. (2024). Exploring the impact of artificial intelligence on healthcare management: a combined systematic review and machine-learning approach. Applied Sciences, 14(22), 10144. https://doi.org/10.3390/app142210144 DOI: https://doi.org/10.3390/app142210144
Saura, J., & Bužinskienė, R. (2025). Behavioral economics, artificial intelligence and entrepreneurship: an updated framework for management. International Entrepreneurship and Management Journal, 21(1). https://doi.org/10.1007/s11365-025-01076-7 DOI: https://doi.org/10.1007/s11365-025-01076-7
Schöggl, J., Rusch, M., Stumpf, L., & Baumgartner, R. (2023). Implementation of digital technologies for a circular economy and sustainability management in the manufacturing sector. Sustainable Production and Consumption, 35, 401-420. https://doi.org/10.1016/j.spc.2022.11.012 DOI: https://doi.org/10.1016/j.spc.2022.11.012
Sharifi, S., Namvar, M., Intezari, A., & Akhlaghpour, S. (2024). Healthcare llms go to market: a realist review of product launch news.. https://doi.org/10.3233/shti240513 DOI: https://doi.org/10.3233/SHTI240513
Sia, W., Ahmad, Z., Muhamad, S., Ali, A., & Hamdan, H. (2024). Effective risk management through data-driven hse assurance program for safe execution project delivery.. https://doi.org/10.2118/221994-ms DOI: https://doi.org/10.2118/221994-MS
Sriharan, A., Kuhlmann, E., Correia, T., Tahzib, F., Czabanowska, K., Ungureanu, M., … & Kumar, B. (2025). Artificial intelligence in healthcare: balancing technological innovation with health and care workforce priorities. The International Journal of Health Planning and Management. https://doi.org/10.1002/hpm.3927 DOI: https://doi.org/10.1002/hpm.3927
Suryawanshi, V., Kanyal, D., Sabale, S., & Bhoyar, V. (2024). The role of ai in enhancing hospital operational efficiency and patient care. Multidisciplinary Reviews, 8(5), 2025153. https://doi.org/10.31893/multirev.2025153 DOI: https://doi.org/10.31893/multirev.2025153
Taifa, I., & Nzowa, J. (2025). Implementing supply chain management 4.0: potential driving forces and strategies from an empirical study of pharmaceutical industries. Engineering Reports, 7(6). https://doi.org/10.1002/eng2.70190 DOI: https://doi.org/10.1002/eng2.70190
Tariq, M., Poulin, M., & Abonamah, A. (2021). Achieving operational excellence through artificial intelligence: driving forces and barriers. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.686624 DOI: https://doi.org/10.3389/fpsyg.2021.686624
Wang, X., & Wu, W. (2024). Ceo ai orientation, human resources and green innovation: an attention-based view. Kybernetes. https://doi.org/10.1108/k-04-2024-0964 DOI: https://doi.org/10.1108/K-04-2024-0964
Whitman, M. (2021). Modeling ethics: approaches to data creep in higher education. Science and Engineering Ethics, 27(6). https://doi.org/10.1007/s11948-021-00346-1 DOI: https://doi.org/10.1007/s11948-021-00346-1
