This paper systematically studies the application of artificial intelligence technology in corporate strategic innovation management and its impact mechanism. The research shows that artificial intelligence significantly improves corporate innovation performance by reshaping the entire process of strategic analysis, selection, and implementation. In the strategic analysis phase, based on big data analysis and intelligent monitoring technology, enterprises can accurately identify market trends and consumer demand; in the strategic selection phase, predictive models and decision support systems enhance the scientific nature of strategic decisions; in the strategic implementation phase, process automation and adaptive adjustment mechanisms ensure the efficient execution of strategies. The study also found that artificial intelligence promotes the improvement of innovation performance through two paths: enhancing corporate self-innovation capabilities and promoting the upgrading of R&D skills, with the effects being particularly significant in technology-intensive and low-pollution industries. At the same time, this study identified five implementation paths for artificial intelligence to enable innovation performance, including differentiated modes such as technology synergy and service ecosystem. Despite challenges such as data security and algorithm transparency, enterprises can fully exploit the strategic value of artificial intelligence by establishing a sound data governance system and ethical norms. This study provides theoretical basis and practical guidance for enterprises to promote digital transformation.

Zhibin Li. Research on AI-Empowered Enterprise Strategic Innovation Management. Academic Journal of Business & Management (2025), Vol. 7, Issue 10: 23-29. https://doi.org/10.25236/AJBM.2025.071004.

[1] Wang Yu, Tang Yaojia. How does the application of artificial intelligence affect the breadth of corporate innovation? [J]. Journal of Financial and Economic Issues, 2024, 483(02): 40-52. 

[2] Minghui Zhang, Ian Thormpson. AI Origins: From Turing to ChatGPT [J]. World Science, 2024, (01):56-58.

[3] Zhigao Liu. Research on Innovation of Enterprise Management Models in Big Data Environment [J]. Macroeconomic Management, 2017, (S1): 134-135. 

[4] Xu Wenwei, Xiao Lizhi, Liu He. The current status and challenges of artificial intelligence applications in Chinese enterprises [J]. China Engineering Science, 2022, 24(06): 181-191.

[5] Chen Guoqing, Zeng Dajun, Wei Qiang, et al. Paradigm shifts and enabling innovations in decision-making under the big data environment [J]. Management World, 2020, 36(02): 95-105+220.

[6] Lin Zijun, Wu Qionglin, Cai Fengyan. A Review of Artificial Intelligence Research in the Field of Marketing [J]. Foreign Economy and Management, 2021, 43(03):89-106. 

[7] He Da'an. Digital Economic Models and Corporate Investment Management [J]. Social Science Journal, 2020, (06):167-176.

[8] Nie Shun, Wu Duansheng, Luo Tianxiong, et al. Research on Real-time Monitoring System for the Whole Process of Intelligent Manufacturing in the Industrial Internet Environment [J]. Software, 2023, 44(06):175-177+180.

[9] Liu Yi, Deng Qing, Peng Yusu. Security Challenges Facing Data Sovereignty and Privacy Protection in the Big Data Era [J]. Journal of Management Modernization, 2019, 39(01):104-107. 

[10] Chen Xiongshen. Ethical Risks and Governance of Artificial Intelligence: A Path Based on Algorithm Audit System [J]. Research on Natural Dialectics, 2023, 39(10): 138-141. 

[11] Mingzhen Zhang. Discussion and Research on the Role of Daily Chemical Industry Culture in High-Quality Development under the Background of Industrial Digitization [J]. Daily Chemical Industry (Chinese and English), 2023, 53(03):367-368.