Pacific Asia Journal of the Association for Information Systems


Background: Artificial Intelligence (AI) has become a ubiquitous phenomenon in recent times, with most organizations today attempting to maneuver their way around developing AI systems with the aim of improving the products and services they provide. However, what complicates developing AI systems is the paucity in frameworks to support organizations with AI System Development (SD). As a result, many organizations are using existing approaches which have been previously applied in earlier emerging Information Technology endeavors. This study explored how a framework can promote effective organizational AI SD. To achieve this a holistic framework for AI SD was conceptualized and examined from an organizational perspective.

Method: This study has examined the conceptualized framework People-Process-Data-Technology (2PDT) in AI, through a case study research design. The empirical data was analyzed based on 12 case studies within Australia including 39 interviews with AI experts. We have applied thematic analysis to investigate requirements of organizational AI SD.

Results: The results demonstrate that organizations are challenged by key factors, which inhibits their ability to effectively develop AI systems. For example, organizations are not achieving successful delivery of AI systems due to a lack of required skills. Additionally, a plethora of AI technology which is constantly evolving, poor data quality, and the paucity of AI SD frameworks are all contributing to unpredictable delivery outcomes.

Conclusion: This paper investigated requirements for effective organizational AI SD by examining the 2PDT framework. The results contribute to AI phenomenon by developing the requirements for AI SD, in terms of people, process, data and technology. It contributes to theory by evaluating and developing AI requirements for effective AI systems. The examination of the framework and case study approach added valuable knowledge to the AI domain. In addition, we contributed to practice by identifying requirements that organizations should consider in achieving better AI SD outcomes.