TY - JOUR
T1 - The cold-start problem in nascent AI strategy
T2 - Kickstarting data network effects
AU - Vomberg, Arnd
AU - Schauerte, Nico
AU - Krakowski, Sebastian
AU - Ingram Bogusz, Claire
AU - Gijsenberg, Maarten J.
AU - Bleier, Alexander
N1 - Publisher Copyright:
© 2023 The Authors
PY - 2023/11
Y1 - 2023/11
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Cold-start problem
KW - Data strategy
KW - Network effects
KW - Value creation
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U2 - 10.1016/j.jbusres.2023.114236
DO - 10.1016/j.jbusres.2023.114236
M3 - Article
AN - SCOPUS:85172455901
SN - 0148-2963
VL - 168
SP - 1
EP - 10
JO - Journal of Business Research
JF - Journal of Business Research
M1 - 114236
ER -