The method relied on expert judgments expressed as triangular fuzzy numbers, a way of capturing the uncertainty inherent in subjective assessments. After normalization and CFCS defuzzification, five challenges emerged as the primary causal drivers: resistance to change, lack of standardization, limited trust in AI outputs, talent shortage, and lack of clinical validation. These upstream factors exert strong influence on downstream effects, including regulatory barriers, biological complexity, integration with legacy systems, and poor data quality.

The most striking implication is that several constraints commonly viewed as external to organizations, such as regulatory hurdles and data quality problems, appear in the causal map as manifestations of internal organizational and technical shortcomings. In other words, weaknesses in workforce capability, standardization, and confidence in AI tools may be propagating outward, shaping how companies experience regulation, legacy infrastructure, and data reliability. By providing a structured causal prioritization of barriers, the study offers actionable guidance for targeting high-leverage interventions in AI-enabled drug discovery.

The authors caution that the findings reflect expert judgment within a North Cyprus context and should be interpreted as indicative organizational signals rather than universal prescriptions. The study is an analysis of perceived interdependencies among challenges, not a measurement of AI performance in drug discovery itself, and its conclusions depend on the expert panel and setting from which the judgments were drawn. Even so, the causal structure it reveals suggests that efforts to speed AI adoption in pharmaceutical research may achieve more by addressing foundational issues, such as change management, standards, trust, skills, and clinical validation, than by tackling downstream symptoms in isolation.

Subject of Research: Causal mapping of barriers to artificial intelligence adoption in drug discovery

Article Title: Mapping interdependencies among challenges to artificial intelligence adoption in drug discovery

Article References: Alhajj, L., Isiaku, L., Muhammad, A. S., Alhajj, J., & Sheikh Muse, A. M. (2026). Mapping interdependencies among challenges to artificial intelligence adoption in drug discovery. Humanities and Social Sciences Communications. https://doi.org/10.1057/s41599-026-08833-1

Image Credits: AI Generated

DOI: 10.1057/s41599-026-08833-1

Keywords: artificial intelligence, drug discovery, Fuzzy DEMATEL, pharmaceutical industry, regulatory barriers, data quality, talent shortage, organizational change, clinical validation, expert judgment, Mapping, interdependencies