Dual impact of AI on business processes: fuzzy TOPSIS prioritization and theoretical framework for strategic adoption

Purpose This study examines the dual impact of artificial intelligence (AI) on business processes by systematically identifying and prioritizing both its value-creating benefits (bright-side) and risk-inducing consequences (dark-side). The study further aims to develop an integrated theoretical framework that explains how organizations can balance these opposing effects to achieve sustainable AI-driven business transformation. Design/methodology/approach A content-driven review of recent peer-reviewed literature was conducted to identify key positive and negative impacts of AI across five business process dimensions. Expert evaluations from 42 professionals were analyzed using the Fuzzy Technique for Order Preference by Similarity to Ideal Solution method to prioritize these impacts under uncertainty. Sensitivity analysis was performed to validate the robustness of the results, and an integrated theoretical framework was developed based on the empirical findings. Findings The results reveal that AI delivers its strongest benefits through enhanced financial risk forecasting, internal control, sustainability reporting and customer-facing automation, significantly improving operational efficiency and decision quality. However, the findings also highlight critical risks associated with inadequate governance, privacy invasion, ethical lapses and excessive reliance on automated systems. Governance-related risks emerged as the most severe challenges, indicating that the absence of robust ethical and regulatory frameworks can undermine the long-term value of AI adoption. Practical implications The study provides managers and policymakers with a structured decision-support approach to prioritize AI applications while proactively managing ethical, governance and data-related risks. Originality/value This research offers a novel integration of fuzzy multi-criteria decision analysis with theory development to present a balanced, data-driven understanding of AI's dual impact on business processes and organizational sustainability.