Effect of artificial intelligence on corporate world
Vedika Taware
1) Li , et al. (2023) examines the impact of artificial intelligence (AI) on on-the-job learning through a systematic analysis. It proposes six hypotheses based on literature review and theoretical analysis, which are tested empirically using CGSS data. The findings indicate that AI usage in the workplace significantly hampers workers’ on-the-job learning, confirmed through various robustness and endogeneity checks. Additionally, AI’s adverse effects on learning are mediated by factors such as future expectations, economic income, and working hours. Furthermore, regions with more human-AI competition and labor-management conflicts demonstrate a more pronounced negative effect on on-the-job learning.
2) LAZO,et al. (2023) emphasize the increasing interest in AI adoption within the banking industry is evident, with applications ranging from credit decisions to fraud detection. However, existing studies primarily focus on the perspectives of customers and banks, neglecting other stakeholders like regulators and service providers. Regulators’ involvement is crucial due to regulatory concerns, and service providers can accelerate AI adoption. Trustworthiness of AI solutions is a concern, necessitating collaboration among stakeholders for responsible AI use. Future research should explore regulators’ and service providers’ influence to inform policy reforms and service contracts, facilitating seamless technology adoption in banking.
3) Zhang, et al. (2024) stated that AI has reshaped the understanding of intelligence, extending its application beyond scientific research to various industries and services. However, the question of AI’s legal personality arises only when it demonstrates subjective assessment and awareness of causality. Currently, AI responsibility remains outside the legal realm, with owners/operators accountable for its actions. Key issues surrounding AI damage and liability include culpability, perception of causality, intentionality, and interpretability of decision-making processes. While AI’s impact on human life is still evolving, there’s a need for a robust legal framework to balance individual interests and mitigate threats to livelihood and safety.
4) Shoufu, et al. (2023) examines the relationship between the agglomeration of the AI industry and economic complexity in 194 Chinese cities from 2000 to 2016. Using a novel dataset of over 2.5 million AI enterprises and employing the DO index to measure industry agglomeration, the study finds a positive impact of AI industry agglomeration on economic complexity. factors such as manufacturing and financial development, as well as infrastructure, are found to be positively correlated with economic complexity. The study also proposes a mediating model to explore the underlying mechanisms of this relationship based on Marshall industry agglomeration theory.
5) Zou,et al.(2023) investigates the impact of artificial intelligence (AI) on industrial upgrading using panel data from 285 cities in China spanning from 2000 to 2019. The findings indicate that AI not only facilitates industrial advancement but also effectively prevents industrial structure deviation from equilibrium, promoting industrial rationalization. Robust analysis techniques, including central city samples, winsorize treatment, and instrumental variables method, confirm the study’s results. AI has a more pronounced effect on cities with a higher level of industrial upgrading, where it acts through the intermediary mechanism of technological innovation.
6) Zhang & Deng (2023) examines the impact of artificial intelligence (AI) and industrial robots on firms’ exports in China, using industry-level data from 2002 to 2013. The findings reveal a dominant negative substitution effect of industrial robots on exports, despite their positive productivity effect. However, the impact varies across industries, with high-tech sectors benefiting from increased exports due to higher productivity. The study identifies temporal variability, with industrial robots .the paper suggests policy recommendations, including promoting industrial transformation towards reducing the proportion of low-skilled labor, and improving the capital-to-labor ratio to enhance China’s competitiveness in international markets.
7) Goi, et al. (2023) highlights the evolving strategic priorities for businesses emphasizing the integrating digital technologies. Key trends is priorities have shifted towards digital transformation, operational efficiency, customer experience optimization, and remote work support. Businesses recognize the importance of enhancing cybersecurity, digital employee experience, productivity, and profitability. Key challenges and opportunities include the centrality of data for competitiveness, IoT facilitating market entry and global reach, digitization across sectors, virtualization of IT infrastructure, and AI for strategic decision-making. Integrating digital solutions into strategy drives technological advancement.
8)Owczarczuk, (2023)discusses the growing significance of AI in the modern economy, emphasizing recent widespread adoption due to improved computer performance and data availability. Regulatory authorities globally prioritize AI in strategic programs to leverage its benefits while managing associated risks. The article advocates for balanced regulation promoting innovation while ensuring security and protecting citizens’ rights. However, regulating AI faces challenges such as diverse interpretations of AI, ethical concerns, and regulatory competition. the article suggests the importance of an anthropocentric approach, emphasizing AI’s role in serving people.
9) Wang, et al. (2023) examined the impact of artificial intelligence (AI) technology in the workplace on economic outcomes such as employment, income distribution, and productivity. While these studies have enriched our understanding of related fields, they often focus on economic performance and rely on industry-level data or theoretical models. The conclusions are primarily based on employee perceptions. Additionally, the widespread application of AI in firms has led to changes in management objectives, ideas, and methods, potentially diminishing employee responsibility and weakening the employer-employee relationship.
10) Ghimire, et al. (2024). delves into the potential applications and challenges of incorporating Generative Artificial Intelligence (GenAI) within the construction industry. the study highlights the limitations of traditional AI methods and explores recent GenAI use cases. Industry insights were gathered through sentiment analysis, revealing both the perceived opportunities and barriers to GenAI adoption in construction. The study proposes a conceptual framework to facilitate GenAI implementation in construction, mapping various GenAI model types to different construction tasks across project phases. However, significant implementation challenges such as domain knowledge, model accuracy, interpretability, and ethical considerations must be addressed.
Conclusion:
The diverse range of studies presented underscores the multifaceted impact of artificial intelligence (AI) across various industries and domains. From labor dynamics and economic complexity to organizational management and technological advancement, AI’s influence permeates through different sectors, prompting both opportunities and challenges.
In conclusion, the synthesis of these studies underscores the complex interplay between AI adoption, economic dynamics, regulatory landscapes, and societal implications. As AI continues to reshape industries and redefine work paradigms, it is essential to navigate these changes thoughtfully, prioritizing ethical considerations, inclusivity, and human-centric values in the pursuit of sustainable and equitable outcomes.
References
Ghimire, P., Kim, K., & Acharya, M. (2024). Opportunities and challenges of generative AI in construction industry: Focusing on adoption of text-based models. Buildings, 14(1), 220.
Goi, V., Ahieieva, I., Mamonov, K., Pavliuk, S., & Dligach, A. (2023). The impact of digital technologies on the companies’ strategic management. Economic Affairs, 68(2),
LAZO, M., & Ebardo, R. (2023). Artificial intelligence adoption in the banking industry: Current state and future prospect. Journal of Innovation Management, 11(3)
Li, C., Zhang, Y., Niu, X., Chen, F., & Zhou, H. (2023). Does artificial intelligence promote or inhibit on-the-job learning? human reactions to AI at work. Systems, 11(3), 114.
Owczarczuk, M. (2023). Ethical and regulatory challenges amid artificial intelligence development: An outline of the issue. Ekonomia i Prawo, 22(2),
Shoufu, Y., Ma, D., Zuiyi, S., Lin, W., & Li, D. (2023). The impact of artificial intelligence industry agglomeration on economic complexity: Znanstveno-strucni casopis. Ekonomska Istrazivanja, 36(1),
Wang, J., Xing, Z., & Zhang, R. (2023). AI technology application and employee responsibility. Humanities & Social Sciences Communications, 10(1),
Zhang, X., Antwi-Afari, M., Zhang, Y., & Xing, X. (2024). The impact of artificial intelligence on organizational justice and project performance: A systematic literature and science mapping review. Buildings, 14(1), 259.
Zhang, Z., & Deng, F. (2023). How can artificial intelligence boost firms’ exports? evidence from china. PLoS One, 18(8)
Zou, W., & Xiong, Y. (2023). Does artificial intelligence promote industrial upgrading? evidence from china: Znanstveno-strucni casopis. Ekonomska Istrazivanja, 36(1),
