随着第四次工业革命的来临，AI已逐步从科幻走入现实。AI技术的发展源自核心算法的突破、计算能力的迅速提高、以及海量互联网数据的支撑。社会经济的各个领域正努力朝着“人、机、物”三元融合的目标前行，不断推动新型创新管理方式的实现。然而，人机交互的复杂性以及利益相关者的感知和动机仍面临着巨大挑战，亟待学者深入剖析AI赋能创新的内在机理。基于AI的创新管理可以定义为应用AI技术来扩展、补充甚至替代人类能力，以高效、系统地开发和促进组织中的创新，范围从识别有前途的机会到成功的市场启动。

本期，编者选取近期刊登在《Technological Forecasting & Social Change》及《Management Information Systems Quarterly》上与AI赋能创新相关的三篇文章。具体而言，Haefner等 (2021)针对创新的两种约束（信息处理约束和局部搜索例程约束），揭示了AI帮助企业产生想法和发展想法的内在机制，并进一步概述AI逐步替代人类的各阶段的程度。Füller等 (2022) 在此基础上，拓展了AI影响创新管理的阶段。除了机会识别和想法产生，延伸了想法评估和筛选、概念和策略发展及商业化实施阶段。同时利用聚类分析从AI技术采纳者角度探讨不同集群（AI领航者，AI实践者，AI偶发创新者和非AI创新者）在组织设置、感知和组织情境等方面的差异，有助于理解AI未来实践应用的影响因素。Lou和Wu (2021)聚焦生物医药行业，通过实证研究探讨AI创新能力对药物研发的影响。研究表明，对于作用机制已知的新药和新颖性中等的药物，该影响正向显著，同时在拥有AI技能和药物发现领域专长的组合员工时，AI创新能力尤为凸显。

人工智能（AI）重塑企业以及创新管理的组织方式。随着技术的快速发展和人类组织的取代，人工智能可能确实会迫使管理层重新思考企业的整个创新过程。为此，我们回顾和探讨了未来创新管理的影响。利用卡内基学院的想法和企业的行为理论，我们回顾了人工智能技术和基于机器学习的人工智能系统的创新管理的影响。我们概述了一个框架，展示了人工智能可以在多大程度上取代人类，并解释了在向创新的数字化组织进行转型时需要考虑的重要因素。最后，我们对未来的研究方向进行了探讨。

Abstract: Artificial Intelligence (AI) reshapes companies and how innovation management is organized. Consistent with rapid technological development and the replacement of human organization, AI may indeed compel management to rethink a company's entire innovation process. In response, we review and explore the implications for future innovation management. Using ideas from the Carnegie School and the behavioral theory of the firm, we review the implications for innovation management of AI technologies and machine learning-based AI systems. We outline a framework showing the extent to which AI can replace humans and explain what is important to consider in making the transformation to the digital organization of innovation. We conclude our study by exploring directions for future research.

参考文献：

Haefner N., Wincent J., Parida V., et al. Artificial intelligence and innovation management: A review, framework, and research agenda[J]. Technological Forecasting and Social Change, 2021, 162.

人工智能的应用有望为创新管理提供新的机遇，重塑组织创新实践。我们对150名精通人工智能的创新经理进行了探索性研究，揭示了组织在创新管理中如何使用和实施人工智能的四种不同集群，包括（1）人工智能领航者，（2）人工智能实践者，（3）人工智能偶发创新者和（4）非人工智能创新者。不同的群体不仅在他们的战略、组织结构和技能建设上有所不同，而且在他们感知的潜力、对所需变化的理解、遇到的挑战和组织环境方面也有所不同。我们的研究有助于更好地理解基于人工智能的创新管理的当前状态，其对未来创新实践的影响，以及组织的人工智能雄心和选择的实施方法的差异。

Summary:  The application of AI is expected to enable new opportunities for innovation management and reshape innovation practice in organizations. Our exploratory study among 150 AI-savvy innovation managers reveals four different clusters in terms of how organizations may use and implement AI in their innovation management ranging from (1) AI-Frontrunners, (2) AI-Practitioners, and (3) AI-Occasional innovators to (4) Non-AI innovators. The different groups vary not only in their strategy, organizational structure, and skill-building but also in their perceived potential, understanding of the required changes, encountered challenges, and organizational contexts. Our study contributes to a better understanding of the current state of AI-based innovation management, its impact on future innovation practice, and differences in organizations’ AI ambitions and chosen implementation approaches.

参考文献：

Füller J., Hutter K., Wahl J., et al. How AI revolutionizes innovation management – Perceptions and implementation preferences of AI-based innovators[J]. Technological Forecasting and Social Change, 2022, 178.

Summary: Advances in artificial intelligence (AI) could potentially reduce the complexities and costs in drug discovery. Using a resource-based view, we conceptualize an AI innovation capability that gauges a firm's ability to develop, manage and utilize AI resources for innovation. Using patents and job postings to measure AI innovation capability, we find that it can affect a firm’s discovery of new drug-target pairs for preclinical studies. The effect is particularly pronounced for developing new drugs whose mechanism of impact on a disease is known and for drugs at the medium level of chemical novelty. However, AI is less helpful in developing drugs when there is no existing therapy. AI is also less helpful for drugs that are either entirely novel or those that are incremental “follow-on” drugs. Examining AI skills, a key component of AI innovation capability, we find that the main effect of AI innovation capability comes from employees possessing the combination of AI skills and domain expertise in drug discovery as opposed to employees possessing AI skills only. Having the combination is key because developing and improving AI tools is an iterative process requiring synthesizing inputs from both AI and domain experts. Taken together, our study sheds light on both the advantages and the limitations of using AI in drug discovery and how to effectively manage AI resources for drug development.

参考文献：

Lou B., Wu L. AI on drugs: Can artificial intelligence accelerate drug development? Evidence from a large-scale examination of bio-pharma firms[J]. MISQ Forthcoming, 2021.