The cold-start problem in nascent AI strategy: Kickstarting data network effects
从网络效应视角分析新兴AI战略，指出成功依赖数据网络效应，但面临冷启动问题。通过文献和实践者访谈，提出研究议程以帮助克服该问题，启动良性循环。
While many artificial intelligence (AI) strategies are successful, countless others fail. Why do some strategies succeed while others fail? We adopt a network effects (NEs) perspective to conceptualize AI strategies, highlighting the AI context’s specifics. We argue that nascent AI strategies’ success depends on data NEs: companies establishing a functional “running system” to capitalize on these effects. However, this presents a challenge known as the cold-start problem (CSP), which involves initiating and accelerating a virtuous cycle: more data benefits the AI system, enhancing performance, which then attracts more data. In this paper, we examine the CSP in nascent AI strategy, exploring how it can be understood in terms of its technological and business dimensions and ultimately be overcome to kick-start a virtuous cycle of data NEs. By drawing insights from existing literature and practitioner interviews, we present a research agenda to encourage further investigation into overcoming the CSP.