Artificial Intelligence Adoption, Enterprise Capabilities and Performance

There are few publications dealing with the relationship between the adoption of AI, enterprise capabilities, and its performance. This study addresses the gap by investigating key factors that influence enterprise capabilities and performance in the context of AI adoption. We used an archival research method based on secondary data in the form of relevant publications from 2000–2025. In particular, we used the causation coding technique for qualitative data analysis. We present a conceptual framework that emerges from our secondary qualitative data analysis results, illustrating the relationships between higher-level constructs presented by A. Zebec and M. Indihar Stemberger and including enterprise capabilities measured by 13 independent variables grouped under 3 higher level constructs (cognitive business process automation, business process innovation, organisational learning), a moderating construct called AI adoption, measured by 3 variables as well as outcome construct called enterprise performance measured by 16 dependent variables grouped under 3 higher level constructs (business process performance, organisational performance, decision-making performance). We propose that, in the future, the conceptual framework could be operationalised in the form of a quantitative questionnaire and tested for validity and reliability based on a statistically significant sample of enterprises across various industries.