AI-based systems adoption in business operations: barriers and performance effects

The purpose of this study is to identify and describe the barriers to AI adoption in business operations, Technological barriersassess their relevance, and test their impact on the success of AI adoption and its subsequent effects on business performance. This study employs a quantitative survey method, gathering data from 193 AI specialists and managers. Hypotheses were tested using covariance-based structural equation modeling. To ensure reliability and validity, several robustness checks and alternative model specifications were applied. Among all known AI adoption barriers, strategic barriers appear to be the most significant as they negatively influence both AI adoption success directly and AI performance effects indirectly through AI adoption success. A lack of clear strategy, leadership commitment, and understanding of AI use cases significantly hinders adoption success. It was also found that successful AI adoption positively affects AI-driven business operations performance effects, and that a company’s age is predicted to inhibit successful AI adoption. The results provide a prioritization of AI adoption barriers and a novel measurement tool. While previous research has generally examined AI adoption barriers in relation to overall company performance, this study focuses specifically on their impact within business operations. Moreover, whereas earlier studies have primarily employed qualitative, exploratory methods, this research complements the literature by using a quantitative, explanatory approach based on primary data, discriminating between direct and indirect AI adoption barrier effects. The main limitation of the study is that most survey respondents have on average around two years of experience with AI implementation.