Artificial Intelligence-Powered Anti-Poaching Strategies: Enhancing Conservation Management in Zimbabwe’s Wildlife Sector - Simelisizwe Sibanda; Kudakwashe Zvitambo (Goodwood Publishing, 2026)
Purpose: The study explores the role of Artificial Intelligence (AI) in improving anti-poaching strategies within Zimbabwe's wildlife tourism sector, evaluating its effectiveness, operational impact, and broader implications for sustainable conservation and tourism governance. Methodology: The study adopted an Interpretative Paradigm, which advocates a qualitative approach. The researcher interviewed 15 participants to evaluate the impact and scalability of AI-driven interventions in Zimbabwe. The study drew participants from Zimbabwe's game reserves. The study thematically analyzed the data to develop themes and codes. Results: The results indicate that AI enhances poacher detection and improves response time. In addition, the results showed that AI helps anticipate poaching attempts, allowing for proactive measures. Furthermore, the results show that AI tools, such as the Spatial Monitoring and Reporting Tool (SMART), empower local rangers and communities to take ownership of conservation. Conclusions: This study demonstrates that Artificial Intelligence (AI) enhances anti-poaching efforts in Zimbabwe by improving detection, response times, and proactive measures. AI tools like SMART empower local rangers, improving conservation management. The findings emphasize AI’s potential in wildlife protection and call for policy and infrastructure support. Limitations: The study adopted an interpretative paradigm using a qualitative approach, resulting in a small sample size that may affect the generalizability of the results. In addition, limited access to reliable data on poaching incidents and AI implementation affects the accuracy of the study. Contributions: This study provides insights into the conservation of wild animals by leveraging artificial intelligence.