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This study examines the critical success factors (CSFs) that contribute to the effectiveness of ideation programs for small and medium-sized enterprises (SMEs), with a focus on the Durban University of Technology’s Centre for Social Entrepreneurship Rapid Incubator. Employing a quantitative methodology, data were collected from 52 participants through a structured questionnaire and analysed using SPSS. The demographic profile indicated that most participants were young entrepreneurs operating in early-stage, resource-limited businesses. Reliability testing confirmed the robustness of the measurement scale (Cronbach’s alpha = 0.831; KMO = 0.709). Results from mean score analysis and one-sample t-tests demonstrated that clearly defined program objectives, customized content, mentoring, collaboration, practical learning experiences, a supportive environment, and access to market insights were consistently highly valued. Additionally, mechanisms such as feedback processes, follow-up support, and partnerships with industry stakeholders were identified as essential for maintaining the long-term impact of ideation initiatives. The study concludes that the success of ideation programs depends on a systems-based approach that effectively integrates resources, processes, the environment, and ongoing feedback, thereby promoting innovation, competitiveness, and resilience among SMEs.

This study investigates the use of artificial intelligence (AI) to improve operational performance in Zimbabwean road haulage enterprises, with a focus on driver training as a moderator. As the logistics industry faces new difficulties, AI technologies have great promise for increasing efficiency and decision making. However, the usefulness of these technologies is determined by the skill levels of the drivers using them. This study demonstrated how extensive driver training improves the capacity to comprehend AI-generated insights, resulting in better route management, lower operating costs, and increased safety. This study examines how AI affects key performance variables such as cost savings, productivity, customer happiness, and environmental sustainability, using real data from road haulage companies. Key findings demonstrate how AI is transforming decision-making, improving operational effectiveness, and optimizing routes. The research highlights several noteworthy obstacles in addition to their obvious advantages, such as budgetary limitations, difficulty in obtaining pertinent data, and the need for more regionalized AI solutions. The findings, which are based on case studies and performance data from diverse enterprises, indicate that (i) organizations that invest in both AI and driver training benefit from a synergistic impact, resulting in higher operational outcomes, (ii) there is need to combine technical improvements with human experience to achieve maximum performance in Zimbabwe's competitive road-haulage market and finally (iii) this study offers helpful recommendations for successfully integrating artificial intelligence (AI) into haulage processes, along with insights into best practices and alternative approaches to overcome current obstacles. This study emphasizes the importance of context-specific solutions in emerging regions, enhancing the expanding corpus of knowledge on AI applications, particularly in logistics.