How Does AI Empower the Development of Cities and Enterprises? A Literature Review and Pathway Analysis

Yulin Yang; Huake Liu

doi:10.62177/apemr.v2i3.345

Authors

Yulin Yang School of Public Administration，China University of Geosciences
Huake Liu Wuhan University

DOI:

https://doi.org/10.62177/apemr.v2i3.345

Keywords:

Artificial Intelligence, Urban Development, Corporate Governance, Pathway Mechanisms

Abstract

As a key driving force behind the new round of technological revolution, artificial intelligence (AI) is reshaping urban systems and the operational logic of enterprises at an unprecedented pace. In the urban context, it has revolutionized public governance and administrative models, driving the upgrading of smart infrastructure and the transformation of fiscal systems. In the corporate domain, AI has exerted far-reaching impacts on operational performance, corporate governance, and financing activities. However, issues such as the "algorithmic divide" and new financial risks have also become prominent. Through a comprehensive review and analysis of relevant literature, this study reveals the complex mechanism by which AI influences urban and corporate development. From the perspective of enabling pathways, the technological mechanism has altered cost structures and production models, the principal-agent mechanism has reconstructed organizational relationships, and the market allocation mechanism has reshaped market order—yet each of these mechanisms faces unique challenges. In the future, establishing a balance between AI capabilities and institutional frameworks, as well as strengthening regulatory and ethical norms, will be crucial to achieving efficient transmission across the "urban-corporate" dimension and promoting digital transformation.

Downloads

Download data is not yet available.

References

Schintler, L. A., & McNeely, C. L. (2022). Artificial intelligence, institutions, and resilience: Prospects and provocations for cities. Journal of Urban Management, 11(2), 256–268. DOI: https://doi.org/10.1016/j.jum.2022.05.004

Wamba-Taguimdje, S. L., Fosso Wamba, S., Kala Kamdjoug, J. R., et al. (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. DOI: https://doi.org/10.1108/BPMJ-10-2019-0411

Yigitcanlar, T., Desouza, K. C., Butler, L., et al. (2020). Contributions and risks of artificial intelligence (AI) in building smarter cities: Insights from a systematic review of the literature. Energies, 13(6), 1473. DOI: https://doi.org/10.3390/en13061473

Liu, H., Li, X., Nie, H., et al. (2024). The AI era: Urban digital and intelligent transformation and enterprise innovation. China Soft Science, (2), 38–54.

Das, D. K. (2025). Digital technology and AI for smart sustainable cities in the global south: A critical review of literature and case studies. Urban Science, 9(3), 72. DOI: https://doi.org/10.3390/urbansci9030072

Deng, T., Zhang, K., & Shen, Z. J. M. (2021). A systematic review of a digital twin city: A new pattern of urban governance toward smart cities. Journal of Management Science and Engineering, 6(2), 125–134. DOI: https://doi.org/10.1016/j.jmse.2021.03.003

Korada, L. (2021). Unlocking urban futures: The role of big data analytics and AI in urban planning–A systematic literature review and bibliometric insight. Migration Letters, 18(6), 775–795.

Valle-Cruz, D., Fernandez-Cortez, V., & Gil-Garcia, J. R. (2022). From E-budgeting to smart budgeting: Exploring the potential of artificial intelligence in government decision-making for resource allocation. Government Information Quarterly, 39(2), 101644. DOI: https://doi.org/10.1016/j.giq.2021.101644

Brynjolfsson, E., Rock, D., & Syverson, C. (2021). The productivity J-curve: How intangibles complement general purpose technologies. American Economic Journal: Macroeconomics, 13(1), 333–372. DOI: https://doi.org/10.1257/mac.20180386

Wang, K. L., Sun, T. T., & Xu, R. Y. (2023). The impact of artificial intelligence on total factor productivity: Empirical evidence from China’s manufacturing enterprises. Economic Change and Restructuring, 56(2), 1113–1146. DOI: https://doi.org/10.1007/s10644-022-09467-4

Kuang, Z., & Zhou, X. (2025). A study on the impact of digital transformation on the ESG performance of enterprises. Journal of Shanghai University of International Business and Economics, 32(1), 34–50. DOI: https://doi.org/10.56028/aemr.13.1.695.2025

Sokołowska, E., & Zargartalebi, M. (2024). Capital structure and firm performance: Global financing decisions among listed companies. Taylor & Francis. DOI: https://doi.org/10.4324/9781003545194

Agrawal, A., Gans, J., & Goldfarb, A. (2022). Power and prediction: The disruptive economics of artificial intelligence. Harvard Business Press.

Shao, J., Lou, Z., Wang, C., et al. (2022). The impact of artificial intelligence (AI) finance on financing constraints of non-SOE firms in emerging markets. International Journal of Emerging Markets, 17(4), 930–944. DOI: https://doi.org/10.1108/IJOEM-02-2021-0299

Mahmood, H. S., Abdulqader, D. M., Abdullah, R. M., et al. (2024). Conducting in-depth analysis of AI, IoT, web technology, cloud computing, and enterprise systems integration for enhancing data security and governance to promote sustainable business practices. Journal of Information Technology and Informatics, 3(2).

Yu, L., & Li, Y. (2022). Artificial intelligence decision-making transparency and employees’ trust: The parallel multiple mediating effect of effectiveness and discomfort. Behavioral Sciences, 12(5), 127. DOI: https://doi.org/10.3390/bs12050127

Alao, O. (2025). Data driven financial decision-making for minority enterprises: Capital access, investment strategies, and creditworthiness optimization. International Research Journal of Modernization in Engineering Technology and Science, 7, 2582–5208.

Xu, R., & Zhao, D. (2023). Digital transformation of private equity in China. Springer. DOI: https://doi.org/10.1007/978-981-99-8482-4

Chang, Q., Kong, C., & Jin, S. (2024). Exploring the impact of digital transformation on corporate violations in China’s A-share market. Systems, 12(9), 322. DOI: https://doi.org/10.3390/systems12090322

Chang, Q., Xie, D., & Zhang, L. (2025). Generative AI meets data quality: Innovation or risk? Available at SSRN. https://doi.org/10.2139/ssrn.5223738 DOI: https://doi.org/10.2139/ssrn.5223738

Nayak, B. S., & Walton, N. (2024). Political economy of artificial intelligence. Springer Books. DOI: https://doi.org/10.1007/978-3-031-62308-0

Liang, H., Fan, J., & Wang, Y. (2025). Artificial intelligence, technological innovation, and employment transformation for sustainable development: Evidence from China. Sustainability, 17(9), 3842. DOI: https://doi.org/10.3390/su17093842

Christou, E., & Piller, F. (2024). Organizational transformation: A management research perspective. In Transformation towards sustainability: A novel interdisciplinary framework from RWTH Aachen University (pp. 303–330). DOI: https://doi.org/10.1007/978-3-031-54700-3_11

Narechania, T. N. (2021). Machine learning as natural monopoly. Iowa Law Review, 107, 1543. DOI: https://doi.org/10.2139/ssrn.3810366

Möhlmann, M., Zalmanson, L., Henfridsson, O., et al. (2021). Algorithmic management of work on online labor platforms: When matching meets control. MIS Quarterly, 45(4), 1999–2022. DOI: https://doi.org/10.25300/MISQ/2021/15333

Delacroix, S., & Wagner, B. (2021). Constructing a mutually supportive interface between ethics and regulation. Computer Law & Security Review, 40, 105520. DOI: https://doi.org/10.1016/j.clsr.2020.105520

Neumann, K., Wiewiorra, L., Baischew, D., et al. (2022). Competitive conditions on transit and peering markets: Implications for European digital sovereignty. https://www.sipotra.it/wp-content/uploads/2022/09/Competitive-conditions-on-transit-and-peering-markets.-Implications-for-European-digital-sovereignty.pdf