Digital Tools for Risk Assessment and Decision-Making in Business Operations
Purpose: This paper analyses how digital tools such as artificial intelligence, predictive analytics, and digital twin technologies can enhance risk assessment and decision-making in business operations. Design/Methodology/Approach: The research studies the existing literature on digital transformation and risk management, drawing on 29 publications from 2020 to 2026 (27 journal articles published in the Scopus database and two book chapters). The publications are analysed using thematic synthesis based on three constructs: digital tools, risk assessment capability, and decision-making performance. Research Limitation: The study is based on existing literature, indicating the need for further research on the practical implementation of digital technologies in different business environments. Findings: The results suggest that introducing digital technologies into management systems speeds decision-making, improves risk management, and makes organisations more competitive. Digital technologies enable organisations to process large amounts of real-time data, detect potential risks, and support more informed, timely managerial decisions. Practical Implication: The findings show that digital tools can improve risk assessment procedures and support more effective managerial decision-making. Social Implication: More efficient decision-making and risk management contribute to greater organisational resilience in dynamic business environments. Originality/Value: The study addresses two gaps in the literature: insufficient analysis of technology combinations and the human factor. In its tool-to-capability framework, digital tools contribute to operational performance indirectly by affecting risk assessment and decision-making, an effect the synthesis links to governance and managerial competencies.
Authors
- Nataliia Lokhanova (ORCID: https://orcid.org/0000-0003-3888-1299)
- V. Khmurova
- V. Bezuhlyi
- O. Kholod
- O. Kiktenko
Institutions
- State University of Trade and Economics (UA)
- Classic Private University (UA)
- Odessa National Economics University (UA)
Publication Details
- Journal
- African Journal Of Applied Research
- Published
- 2026-10-30
- DOI
- https://doi.org/10.26437/sgy0nd10
- Primary Topic
- Impact of AI and Big Data on Business and Society
- Type
- article
- Field-Weighted Citation Impact
- 0.00