Implications of artificial intelligence on organizational agility : a PLS-SEM and PLS-POS approach
Simona Cătălina Ștefan, Ana Alexandra Olariu and Ștefan Cătălin Popa
Artificial intelligence (AI) has radically changed companies' vision of business development, based on its widespread assimilation in key organisational processes. However, in organisational practice, the implementation of AI has generated major challenges, such as those related to the high need for investments in technologies, insufficient level of skill development, and resistance to change of personnel. At the same time, under the conditions in which markets become increasingly dynamic, an increasingly emphasised requirement for companies is to maintain increased organisational agility to quickly adapt to the challenges of the external environment. Therefore, this study aims to analyse the role of AI in capitalising on an organisation's digital capabilities as a means of amplifying organisational agility in order to improve internal and external processes. The research conclusions were based on the application of a questionnaire to employees from various Romanian sectors of activity and for data analysis, structural equation modelling (PLS-SEM) and prediction-oriented segmentation (PLS-POS) were used. The main results indicate that the more digital capabilities organisations have, the more agile they become in relation to internal processes and changes in the external environment. This relationship is facilitated by the use of AI tools. At the same time, prediction-oriented segmentation highlighted two distinct categories of organisations in terms of AI transformation: in those in which this process is more advanced, the mediation effect is stronger. The originality of the study emerges by extending the PLS-SEM results by respondents segmentation through finite mixture partial least squares (FIMIX-PLS) and PLS-POS, but also by considering AI implications on internal and external organisational agility, which is less addressed in the literature. The study outlines several possible managerial interventions, both on AI-related public policies and on the optimisation of business processes under AI implementation.
