Agentic AI for Workforce Foresight: Enabling Intersectoral Competence Transfer in Engineering Consulting

Abstract

Engineering consulting firms increasingly rely on artificial intelligence (AI) to identify, mobilize and develop talent across heterogeneous sectors such as aerospace, energy and mobility. While prior research has examined the effects of AI on knowledge work and decision support, its role in shaping intersectoral agility, that is the ability of consultants to transfer and recombine competencies across industries, and in reconfiguring staffing and workforce planning logics remains underexplored. This paper investigates how AI reshapes intersectoral agility and redefines staffing and workforce planning logics in engineering consulting firms. The study adopts abductive qualitative design conducted within a large international engineering consulting group. The empirical corpus comprises 24 semi-structured interviews and two focus groups involving 30 participants drawn from technical, managerial and support functions, complemented by documentary material and by projective scenario techniques used as elicitation devices. Data was analyzed through an iterative process of open, axial and selective coding combining AI-assisted identification of meaning units with systematic human validation, which generated 472 open codes. The findings, reported in structured form and compared with theoretical expectations, identify three core dimensions. First, transferability across domains is real but conditional upon a pre-existing knowledge base and human mediation. Second, hybrid competencies emerge as a situated capability articulating domain expertise, AI use, reflexivity and verification practices. Third, staffing practices are reconfigured by algorithmic support that participants accept only as a decision aid. Five cross-cutting conditions, namely governance and explicability, dignity and confidentiality, prudence, client trust and acculturation, delimit the acceptability of these transformations. The paper contributes by identifying the socio-organizational mechanisms through which AI reshapes competence transferability and staffing practices. It introduces conditional augmentation as a socio-organizational regime explaining why AI-enabled workforce foresight remains dependent upon human validation, organizational trust and contextual embedding. The findings further reveal four persistent tensions shaping augmented work: decision, transparency, agency and learning paradoxes. From a managerial perspective, transparency, human oversight and equity emerge as essential principles for reconciling efficiency with legitimacy and trust. The study calls for longitudinal and comparative research on the organizational consequences of AI-mediated workforce allocation systems.