AI-based Delphi Methodology: A Novel Approach to Triangulation in Managerial Research

Objective
The aim of this study is to design and validate an innovative hybrid methodology that integrates the classical Delphi technique with the advanced capabilities of artificial intelligence. This hybrid approach seeks to answer the central question of how the synergy between AI capabilities and human judgment can be harnessed to address emerging research and development needs. By combining machine intelligence with expert insight, the study attempts to reduce ambiguity, accelerate consensus-building, and expand the methodological toolkit available to management researchers. As the first research in Iran to focus specifically on an AI-based Delphi approach, this study introduces a pioneering framework grounded in language model technologies and explores its methodological and practical implications.
Methods
This study adopts a mixed-method design with an applied orientation. To evaluate the validity of the proposed approach, its application was examined in the domain of personal development coaching, thereby situating the study within applied research. The statistical population included both human experts and AI language models. In the classical Delphi phase, 15 experts in human resources, coaching, and personal development were selected using the snowball sampling method. In the AI-based phase, three leading language models—ChatGPT, Microsoft Copilot, and Gemini—were selected through comparative expert evaluation. Data collection was conducted via questionnaires, in accordance with Delphi principles. To validate the data, a triangulation strategy was employed, encompassing algorithm triangulation, researcher triangulation, data triangulation, and theory triangulation. To ensure theoretical saturation in interactions with language models, measures such as initial prompt training, multi-stage response review, and iterative model updates were adopted. Finally, paired-sample mean comparison was used to assess the degree of alignment and divergence between the perspectives of human experts and AI-generated outputs.
Results
The integration of classical Delphi with AI-based Delphi yielded a novel methodological framework capable of enhancing both rigor and efficiency in management research. In the qualitative stage, three rounds of the classical Delphi process produced strong expert consensus, as evidenced by an increasing Kendall’s coefficient of concordance. In the quantitative stage, comparison of human expert judgments with AI model outputs through paired-sample mean tests revealed no statistically significant differences between the two sources, indicating high reliability and stability of the data. Furthermore, the findings highlight that targeted prompt engineering and rotational analysis in AI-based Delphi can substantially enrich research processes. These features facilitated multilayer analysis of qualitative data, improved accuracy and comprehensiveness, expanded the diversity of viewpoints, reduced expert fatigue, and accelerated the path to consensus. Collectively, the results show that AI can serve not only as a supplement but also as a methodological partner in collaborative decision-making and research design.
Conclusion
The study concludes that AI-based Delphi methodology holds significant potential for advancing research quality and decision-making in managerial and related fields. The integration of advanced language model capabilities with human expertise enables more precise, multidimensional, and replicable analyses. Although variations in model performance occasionally created challenges in achieving full convergence, the hybrid design offered a practical solution to strengthen the validity of findings. The validation of this methodology in the field of personal development coaching further demonstrates its adaptability to diverse areas of managerial research. Overall, this study provides an innovative perspective on how artificial intelligence can be systematically incorporated into the Delphi method. By addressing both its opportunities and limitations, the study opens a new window for methodological innovation in management studies and contributes to the evolving discourse on the role of AI in research methodologies.