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Cross-Border Workforce Partnerships for Artificial Intelligence Projects: A Review of Talent Supply Strategies and Enterprise Outcomes

Miracle Wikiri, Rasheed Akhigbe, Chioma Ann Udeh, Corresponding author

Abstract

Background: The difficulty of translating geographically distributed technical talent into reliable project capacity limits responsible access to specialized expertise and improved project delivery. Purpose: This conceptual review integrates research and institutional guidance published to date to explain the problem and develop an actionable framework for enterprises staffing artificial intelligence and machine-learning projects across borders. Method: The paper uses an integrative review organized around demand definition, evidence, interaction, decisions, transition, and feedback. Results: The synthesis identifies six recurring requirements: clear demand signals, job-relevant evidence, accessible communication, multidimensional matching, transition support, and governed learning. It proposes a five-stage model comprising Capability- demand mapping, Partner ecosystem design, Verification and compliance, Distributed-team integration, Outcome governance. The model pairs each stage with controls and measures, including demand stability, evidence quality, conversion, time to contribution, sustained performance, and fairness indicators. Conclusion: Cross-border AI workforce partnerships should be managed as a connected socio-technical system. Organizations should pilot the model in a bounded role family, compare outcomes with a baseline, examine unequal effects, and revise the process before scaling.

Keywords

Cross-border AI workforce partnershipsworkforce systemstalent pipelinesskills assessmentorganizational performanceresponsible technology

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