Generative AI for enhanced risk management in SMEs
Eamon Leonard et al.
What the paper says
Establishing and sustaining small and medium-sized enterprises (SMEs) presents considerable challenges. Entrepreneurs must navigate limited resources and significant knowledge gaps across a spectrum of information requirements–from health and safety to corporate governance, taxation, and personnel management. Despite the extensive deployment of generative artificial intelligence (GAI) across sectors such as medicine, education, and finance, its application and research in SME risk management remain notably sparse. This research aims to bridge this gap by showcasing the benefits of GAI within the risk management cycle, which is critical to enhancing the prospects of success among smaller business entities. With rapid technological advancements and the widespread integration of GAI in office and home settings, an opportunity arises to develop customised risk management tools specifically tailored for SMEs. The analysis outlines a conceptual case study in the food and beverage sector, where an entrepreneur launches a new restaurant. The study tracks GAI’s iterative application across all phases of the risk management cycle: identification, comprehension, assessment, treatment, and monitoring. Specifically, it integrates retrieval augmented generation (RAG), which accesses and incorporates information from the Irish Health and Safety Authority (HSA) into its processes. Additionally, GAI assistants are created and employed to execute tasks integral to the risk management cycle, such as calculating risk factors. A key outcome is the creation of a risk register for each potential hazard, demonstrating GAI’s practical application in risk assessment. These tools illustrate the potential of GAI to navigate the complex, information-dense, and regulatory-heavy landscape of contemporary business environments. This exploration underscores how GAI can improve decision-making and risk evaluation and promotes further discussion and research on developing user-friendly GAI technologies.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.