Benchmarking and designing AI-native entrepreneurship ecosystems. Switzerland and Jordan as a case study

Abstract

AI is currently shaping how people are being entrepreneurial by allowing the establishment of AI-native ventures. The creation of these new types of businesses builds off an infrastructure of data, computing power, and research that typically accompany advanced economies. In contrast, most developing economies are still experiencing institutional and structural barriers that inhibit the formation of new ventures and their subsequent growth. Despite the existence of research that explores some of the ways in which successful ecosystems from developed economies could be applied to developing ecosystems, there is little guidance on how to systematically adapt these successful practices in resource-constrained environments. This research uses a comparative, document-based study design to assess how to benchmark and configure AI-native entrepreneurship ecosystems across heterogeneous institutional environments. Using Switzerland and Jordan as two contrasting analytical cases, we define twelve dimensions of an ecosystem and then create comparative ecosystem profiles using a standardized coding and scoring framework. We combine dimension-level data on talent development, applied research, infrastructure, financing, governance and market access with baseline socio-economic indicators. Our results demonstrate a high level of structural asymmetry between the two ecosystems. Switzerland has a balanced and highly coordinated configuration, whereas there is a strong university anchor and demand for talent in Jordan, but there are also significant weaknesses in terms of infrastructure, financing, and industry linkages. Building from these results, we present the parameter re-weighting and the context-sensitive design model to encourage the emergence of AI-native entrepreneurship in Jordan through coordinated architecture, collaborative experimentation resources and internationalization at an early stage. This article advances both the fields of entrepreneurial ecosystems and digital entrepreneurship by framing AI-native entrepreneurship as a new form of knowledge-intensive venture creation and providing a context-sensitive approach for adapting entrepreneurial ecosystems. The results provide a document-informed basis for policymakers, academic institutions and other ecosystem actors seeking to develop AI-based innovation in resource-constrained economies.