Author:
Langholf Valentin,Wilkens Uta
Abstract
AbstractThe use of artificial intelligence (AI) in work processes requires the anticipatory change of work roles because areas of activity are shifting within job profiles, resulting in new interaction patterns between humans and AI and between employees. In order to avoid role conflicts, rejection of the AI system and other undesirable side effects of AI integration, organizations must support human-AI role development with suitable measures. This article presents a methodologically sound approach to role development (clarifying AI Augmented individual roles—clAIr) using the example of service technicians in a mechanical engineering company before and during the introduction of AI-based services. It illustrates how role clarity can be achieved in the interaction with AI when job profiles shift and how role development also includes collaboration with other departments and goal-oriented external communication with customers. The method results in six basic roles that are rooted in role theory in terms of role identity, role innovation, and role clarity. clAIr allows the anticipatory examination of human-AI work roles as a process-based approach.Practical Relevance: Due to the rapidly advancing development of AI in work processes, there is a need in organizations for scientifically validated findings and examples of good practice for successful work with AI. A socio-technical approach with a focus on the changes in role identities of professionals is promising, as the anticipated development of tasks and professions resulting from AI use can only be countered with a comprehensive approach. Previous work refers to human-centered job designs but neglects the preceding process of role identification as a key challenge of implementation. This process support is made possible by the clAIr method for determining roles for working with AI. Its use requires an understanding of role theory and expertise in organizational development.
Publisher
Springer Science and Business Media LLC
Reference41 articles.
1. Akudjedu TN, Torre S, Khine R, Katsifarakis D, Newman D, Malamateniou C (2023) Knowledge, perceptions, and expectations of Artificial intelligence in radiography practice: a global radiography workforce survey. J Med Imaging Radiat Sci 54(1):104–116
2. Anglin AH, Kincaid PA, Short JC, Allen DG (2022) Role theory perspectives: past, present, and future applications of role theories in management research. J Manag 48(6):1469–1502
3. Ashforth BE (2001) Role transitions in organizational life: an identity-based perspective. Lawrence Erlbaum, Mahwah
4. Bauer TN, Bodner T, Erdogan B, Truxillo DM, Tucker JS (2007) Newcomer adjustment during organizational socialization: a meta-analytic review of antecedents, outcomes, and methods. J Appl Psychol 92(3):707–721
5. Berretta S, Tausch A, Ontrup G, Gilles B, Peifer C, Kluge A (2023a) Defining human-AI teaming the human-centered way: a scoping review and network analysis. Front Artif Intell 6: