Nadiya Ungo (current Ph.D. student) co-authored a research paper presented at the 7th Latin American Conference on Industrial Engineering and Operations Management (IEOM) in Panama City, Panama, on August 4–6, 2026. The paper, “Translating AI Exposure Indices into a Work-based Training Framework for Supply Chain and Maritime Industry,” was presented by co-author Dr. Ricardo Ungo.
The study addresses a growing challenge facing employers: while AI exposure indices can show which occupations may be affected by artificial intelligence, they often do not provide enough detail to guide practical workforce training decisions. In response, the paper argues that AI exposure indices should be used as a work activity-level diagnostic tool to identify the specific tasks and activities within jobs that are most susceptible to AI-driven displacement or augmentation.
Focusing on the supply chain and maritime industry, the researchers developed an AI Training Priority Index (AITPI) to translate AI exposure data into a targeted, role-specific training framework. The goal is to help organizations avoid broad, one-size-fits-all upskilling efforts and instead invest in training that addresses the most urgent capability gaps created by AI adoption.
The research applies this framework to 31 occupations in the supply chain and maritime sector, covering 151 intermediate work activities, 351 detailed work activities, and 689 tasks. Drawing on O*NET occupational classifications and large language model usage data, the study evaluates work activities according to three dimensions: criticality, AI exposure, and complementarity across occupations. These measures are then combined into the AITPI, which helps identify where training should be prioritized most urgently.
The paper also proposes four training categories to guide workforce development strategy: AI Collaboration Training, Transition Training, Core Competency Training, and Regular Training. Activities with both high exposure and high criticality are identified as strong candidates for AI collaboration training, where workers need support in learning how to use AI tools to enhance performance. Activities with high exposure but lower criticality are more likely to require transition training, including reskilling for reorganized or redesigned roles.
The study contributes to ongoing conversations about AI, workforce development, and college-to-work transitions by offering a practical framework that connects labor market analysis to actionable training strategy. It also provides a roadmap for firms seeking to capture the operational benefits of AI while preparing employees for changing workplace demands.
Nadiya Ungo’s research interests include college-to-work transition, professional networks, and workforce development in supply chain and maritime industries. The paper is expected to be published in Springer Nature’s Communications in Computer and Information Science series.