The Study of Digital Transformation, Artificial Intelligence and Their Implications for Future Research

Digital transformation changes the conditions under which organizations produce and use knowledge. Artificial intelligence adds new ways to search, predict, generate, and evaluate information. Yet greater use of these tools does not establish that research becomes more reliable or more original. This paper develops a critical narrative review and descriptive secondary-data analysis to examine that distinction. Selected publications on organizational change, AI-assisted work, scientific discovery, and governance provide the conceptual foundation. Published statistics from Stanford's AI Index and Eurostat illustrate changes in adoption between 2023 and 2025. The two series are analysed separately because their populations and measurement procedures differ. The evidence indicates expanding use, but adoption statistics cannot demonstrate causal improvements in research quality. An original conceptual framework links digital foundations and AI capabilities with changes in research practice, conditioned by human expertise, institutional arrangements, and governance. Four propositions translate the framework into questions for future testing. The proposed agenda prioritizes reproducibility, task-specific evaluation, skill development, equitable participation, and preservation of intellectual diversity. The paper concludes that future research should evaluate the complete knowledge-production process, including verification and correction costs, rather than treating faster output as sufficient evidence of progress. No primary survey, experiment, or model validation is claimed. The contribution is an integrated account of how organizations can study AI-enabled transformation while maintaining the evidentiary standards needed for credible knowledge