AI is increasingly being adopted across construction workflows. Yet its implications for work and employment remain poorly understood in the industry’s project-based, highly variable work environments. This study investigates how construction professionals in managerial, supervisory, and technology leadership roles interpret the impact of AI-enabled innovation on tasks, workflows, and workforce structures. The study adopted an interpretivist qualitative design, using semi-structured interviews with 31 professionals from 22 construction firms in the United States. Participants included executives, project managers, technology leaders, and field supervisors selected through purposive and snowball sampling. The data were analyzed using reflexive thematic analysis. Findings showed that AI adoption is concentrated in data-rich and semi-structured tasks such as document review, estimating, reporting, and workflow monitoring. Participants primarily viewed AI as enabling workflow rationalization, task redistribution, and socio-technical reconfiguration rather than widespread occupation-level replacement. From an organizational and managerial perspective, participants interpreted employment impacts along a displacement-net-zero-expansion continuum and generally perceived field trades and relationship-intensive roles as comparatively resilient because their tasks rely heavily on tacit knowledge, contextual judgment, and interpersonal coordination. The study extends task-based automation perspectives by positioning construction as a boundary case where substantial task-level automation does not necessarily translate into occupation-level displacement.
Asare et al. (2026) studied this question.