Nigerian aviation workers rate their AI skills 2.1 out of 5, a survey of 250 finds

The gap was widest in customer service and administrative roles, where AI proficiency averaged below 2.0 (1.7 and 1.8). These are the jobs the authors say are increasingly handled with chatbots and automated scheduling. Technical and maintenance staff rated themselves 2.5 on AI tools, and managers 3.2.

In a regression analysis, the quality of reskilling training was the strongest predictor of how well employees adapted (β=0.38), ahead of perceived usefulness (0.28) and organizational support (0.25). Together with ease of use, the factors explained 62% of the variance in an adaptation score. Fifteen interviews pointed to fear of job loss, training that was too theoretical and top-down rollouts.

The researchers surveyed staff by stratified random sampling across administrative, technical and customer service roles and interviewed 15 key informants, including senior HR directors and union representatives. The questionnaire drew on Technology Acceptance Model scales and the World Economic Forum skills framework, and was piloted with 30 aviation professionals (Cronbach's alpha 0.82). Interviews ran in May and June 2026.

Skill ratings used a five-point scale. Cybersecurity averaged 3.2 and basic digital communication 4.1, so staff were comfortable with everyday tools but not with AI systems. Managers rated their data analytics at 3.5, and the authors say this may lead them to underestimate frontline training needs.

Interviewees described anxiety about their future. One HR manager said, "We hear about automation, but no one tells us if we will still have jobs in five years." A customer service supervisor said staff were never consulted on the system's design. Staff who saw AI as a way to avoid data entry and focus on complex cases showed higher engagement.

The authors recommend replacing generic digital literacy courses with simulations tailored to job roles, adding AI milestones to promotion criteria, forming co-design teams with frontline staff and unions, and tracking psychological safety before and after AI rollouts. They also propose a sector-wide fund to subsidize training for mid-career staff in emerging economies.