Sectoral Differences in AI Adoption and Balanced Scorecard Application: Evidence from Enterprises in the Czech Republic
TL;DR
It is suggested that AI integration is increasingly a cross-sectoral strategic phenomenon, while adoption barriers remain context-dependent, and that AI support initiatives should combine broad cross-sectoral measures with targeted interventions addressing sector-specific obstacles.
Abstract
This study examines the extent to which enterprises differ across economic sectors in their adoption of artificial intelligence (AI) for strategic management, with a particular focus on AI integration, perceived strategic benefits, and adoption barriers. The aim is to assess whether the sectoral context represents a decisive factor in shaping AI-driven strategic practices or whether common patterns prevail across industries. The research is based on a quantitative design using primary survey data collected from enterprises operating in multiple sectors of the Czech economy, which were analyzed by using descriptive and inferential statistical methods. The findings indicate that enterprises across sectors exhibit largely comparable levels of AI integration and similar expectations regarding the strategic benefits of AI, including an improved decision-making quality, efficiency, and competitive positioning. However, statistically significant sectoral differences emerge in the perceived barriers to AI adoption, with IT- and service-sector firms reporting greater challenges related to data quality, system integration, and implementation complexity. These results contribute to the literature on AI adoption and strategic management by suggesting that AI integration is increasingly a cross-sectoral strategic phenomenon, while adoption barriers remain context-dependent. From a managerial and policy perspective, the findings imply that AI support initiatives should combine broad cross-sectoral measures with targeted interventions addressing sector-specific obstacles.