The rapid advancement of artificial intelligence has significantly altered job roles, required skills, and the overall nature of work in the banking sector. These changes highlight the need for a structured model to guide workforce transformation. This study aims to design a workforce transformation model for the banking industry based on dynamic capabilities to explain how employees adapt to emerging digital technologies.
This applied research used a mixed-method approach. In the qualitative phase, a meta-synthesis of 37 international studies identified four major themes: new job opportunities, workforce readiness, transition from traditional roles, and redefinition of employment structures. In the quantitative phase, the components were evaluated and prioritized using a fuzzy Delphi technique with input from 18 experts in banking and digital technology across two rounds.
The findings revealed that “role change and new skills” was the most influential factor with a defuzzified value of 0.90. This was followed by “replacement of repetitive tasks with algorithms” (0.88) and “digital literacy and familiarity with artificial intelligence” (0.86). Overall, the results show that artificial intelligence in the Iranian banking sector contributes more to enhancing skills, restructuring roles, and strengthening human–machine collaboration than to eliminating jobs.
The proposed model suggests that effective workforce transformation requires alignment between the technological, organizational, and environmental dimensions of the TOE framework and dynamic capabilities related to learning, reskilling, and resource reconfiguration. The study recommends focusing on digital skill development, designing human–machine hybrid roles, and establishing continuous learning systems to support sustainable workforce transformation in the digital era.