2024

Aaltonen, Päivi; Ramaul, Laavanya; Kurvinen, Emil; Kutvonen, Antero; Nemeh, Andre

Digital twins (DTs) can be considered a practical example of artificial intelligence (AI)-based industrial applications. While individual technologies may exist to reach the full potential of such applications, organizations nonetheless face various internal and external pressures that impact the capabilities of building and scaling such techniques. Furthermore, industrial applications such as DTs are not free from path-dependency, thus building capabilities towards higher level technology integration differs from completely new firms entering the market. Organizational barriers and enablers for reaching DT maturity are not widely discussed, yet they share similarities with barriers and enablers for reaching AI maturity. These may extend from global challenges to managerial biases in decision-making to geographic location and strategic initiatives. This chapter introduces a perspective for understanding specifically such barriers and potential related to reaching high-level maturity in DT technology, and how does it differ from factors connected to low-level technologies.