Acta Informatica Pragensia X:X | DOI: 10.18267/j.aip.33234

Factors Influencing Digital Transformation Outcomes in AI-Enabled Business Information Systems: An Empirical Survey of Organizational Users

Boštjan Blažič ORCID...1, Jasmina Starc ORCID...2
1 Department of Computer and Information Science, Faculty of Economics and Informatics, University of Novo mesto, Novo mesto, Slovenia
2 Department of Business, Entrepreneurship and Organisation, Faculty of Business and Management Sciences, University of Novo mesto, Novo mesto, Slovenia

Background: Artificial intelligence is increasingly embedded in business information systems, where it supports analytics, automation, decision support, and organizational transformation. However, business informatics research has not yet fully integrated the organizational and measurement conditions under which AI-enabled business information systems are associated with digital transformation outcomes.

Objective: This study examined the associations of AI readiness, business information system quality, perceived AI opportunities, perceived AI challenges, AI governance, and digital competence with perceived digital transformation outcomes.

Methods: A cross-sectional online survey was administered through the 1KA platform to organizational users of business information systems in Slovenia. Data were collected between 1 May and 11 June 2026. Recruitment combined eligibility-based purposive sampling with snowball sampling initiated through 120 publicly available corporate email addresses. Of 607 submitted questionnaires, 43 incomplete submissions were excluded, leaving 564 complete cases. Analyses included reliability diagnostics, principal component analysis (PCA) with varimax rotation, convergent and discriminant validity assessment, including HTMT, Pearson correlations, multiple linear regression with HC3 robust standard errors, nonparametric bootstrap confidence intervals, and thematic analysis of open-ended responses.

Results: The measurement diagnostics indicated acceptable internal consistency and a theoretically interpretable seven-component solution. The regression model was statistically significant but explained a modest 8.1% of the variance in perceived digital transformation outcomes. AI readiness, AI governance, and business information system quality had statistically significant positive coefficients, whereas perceived AI opportunities, digital competence, and perceived AI challenges were not independently significant.

Conclusion: Perceived digital transformation outcomes were associated more strongly with organizational readiness, governance, and the quality of existing business information systems than with perceived technological opportunities. Because the model explained only 8.1% of the variance and the sample was non-probability-based, the findings should be interpreted as limited, directional associations rather than as evidence of necessary or sufficient conditions for digital transformation.

Keywords: AI readiness; Artificial intelligence; Business information systems; Digital transformation; AI governance; Business information system quality.

Received: July 3, 2026; Revised: August 31, 2026; Accepted: September 8, 2026; Prepublished online: November 12, 2026 

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