中文摘要
尽管人工智能(AI)具有推动重大商业创新的潜力,但许多企业难以实现其收益。我们研究为什么一些企业能够成功地将AI用于创新,而另一些企业则失败,重点关注在新颖创新和渐进创新中利用AI所必需的组织支持。具体而言,我们考察精益创业方法(LSM)如何影响AI对初创企业产品创新的作用。通过分析2011年至2020年间1,800家中国初创企业的数据,并结合中国政府鼓励AI采用的政策变化,我们发现AI能力较强的企业会生产出更多创新产品。此外,我们的研究表明,AI投资与LSM在创新中相互补充,其有效性因创新类型和AI能力而异。我们区分了两类AI:探索导向型AI(discovery-oriented AI),它降低新颖创新领域的不确定性;以及优化导向型AI(optimization-oriented AI),它改进和优化现有流程。在LSM的框架内,我们进一步区分原型设计(prototyping)——专注于开发最小可行产品——和受控实验(controlled experimentation)——专注于严格的测试,如A/B测试。我们发现,LSM与探索导向型AI形成互补:利用AI扩大市场机会的搜索范围,并运用原型设计来验证这些机会,从而降低不确定性,促进产品首个版本的推出。相反,LSM与优化导向型AI形成互补:使用A/B测试对输入特征的整个空间进行实验,并利用AI简化迭代改进流程,从而加速产品迭代版本的改进。因此,当企业在产品开发中同时使用AI和LSM时,能够在更短的时间内开发出更多高质量产品。这些发现同时适用于软件和硬件开发,强调了将AI视为异质性构念的重要性,因为不同的AI能力需要不同的组织流程才能实现最优结果。本文由D. J. Wu接受,发表于"人—算法连接"专刊。资助:感谢Mack创新管理研究所提供的资金支持。补充材料:数据文件可在https://doi.org/10.1287/mnsc.2022.03905获取。
Abstract
Although artificial intelligence (AI) has the potential to drive significant business innovation, many firms struggle to realize its benefits. We investigate why some firms succeed in using AI for innovation, whereas others fail, focusing on the organizational support necessary for leveraging AI in both novel and incremental innovation. Specifically, we examine how the lean startup method (LSM) influences the impact of AI on product innovation in startups. Analyzing data from 1,800 Chinese startups between 2011 and 2020, alongside policy shifts by the Chinese government in encouraging AI adoption, we find that companies with strong AI capabilities produce more innovative products. Moreover, our study reveals that AI investments complement LSM in innovation, with effectiveness varying by the type of innovation and AI capability. We differentiate between discovery-oriented AI, which reduces uncertainty in novel areas of innovation, and optimization-oriented AI, which refines and optimizes existing processes. Within the framework of LSM, we further distinguish between prototyping—focused on developing minimum viable products—and controlled experimentation—focused on rigorous testing such as A/B testing. We find that LSM complements discovery-oriented AI by utilizing AI to expand the search for market opportunities and employing prototyping to validate these opportunities, thereby reducing uncertainties and facilitating the development of the first release of products. Conversely, LSM complements optimization-oriented AI by using A/B testing to experiment with the universe of input features and using AI to streamline iterative refinement processes, thereby accelerating the improvement of iterative releases of products. As a result, when firms use AI and LSM for product development, they are able to generate more high-quality products in less time. These findings, applicable to both software and hardware development, underscore the importance of treating AI as a heterogeneous construct because different AI capabilities require distinct organizational processes to achieve optimal outcomes. This paper was accepted by D. J. Wu, Special Issue on the Human-Algorithm Connection. Funding: Financial support from the Mack Institute for Innovation Management is gratefully acknowledged. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.03905 .