Towards Closing the Affordances Gap of Artificial Intelligence in Financial Service Organizations

Bibtex

@Select Types{,
  
   
  
   
  title    = "Towards Closing the Affordances Gap of Artificial Intelligence in Financial Service Organizations", 
  author    = "Christian Engel, Benjamin van Giffen, Philipp Ebel", 
  doi    = "https://doi.org/10.30844/wi_2020_a9-engel", 
  abstract    = "Artificial Intelligence (AI) is considered being a disruptive force for existing companies and a promising avenue towards competitive advantage. A myriad of companies started investing in AI initiatives. However, a significant number of AI projects is not successfully deployed. Taking a closer look at financial service organizations, we aim at contributing to closing the gap between understanding the potential of AI and proactively leveraging the latter. We draw on affordance theory and socio-technical systems (STS) theory to identify the required socio-technical changes to actualize affordances of AI in financial service organizations. We present preliminary findings from a multiple case study approach with five financial service organizations based on rigorous interview coding that yields first insights into AI affordances. Building up on this, we will prioritize and structure future in-depth case studies to investigate how to orchestrate AI-induced changes in STS for actualizing AI affordances.

", 
  keywords    = "Artificial Intelligence, Affordance, Socio-Technical Systems, Organizational Development
", 
}

Abstract

Abstract

Artificial Intelligence (AI) is considered being a disruptive force for existing companies and a promising avenue towards competitive advantage. A myriad of companies started investing in AI initiatives. However, a significant number of AI projects is not successfully deployed. Taking a closer look at financial service organizations, we aim at contributing to closing the gap between understanding the potential of AI and proactively leveraging the latter. We draw on affordance theory and socio-technical systems (STS) theory to identify the required socio-technical changes to actualize affordances of AI in financial service organizations. We present preliminary findings from a multiple case study approach with five financial service organizations based on rigorous interview coding that yields first insights into AI affordances. Building up on this, we will prioritize and structure future in-depth case studies to investigate how to orchestrate AI-induced changes in STS for actualizing AI affordances.

Keywords

Schlüsselwörter

Artificial Intelligence, Affordance, Socio-Technical Systems, Organizational Development

References

Referenzen

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