{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T13:41:05Z","timestamp":1774446065967,"version":"3.50.1"},"reference-count":25,"publisher":"Emerald","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"\n Purpose<\/jats:title>\n This study explores how employees experience digital reskilling initiatives in the context of AI-driven workplace transformation, foregrounding reskilling as a form of identity work rather than solely a response to skill gaps or automation risks. It examines how workers reinterpret professional identities, navigate changes in perceived competence and value and construct career narratives amid technological disruption. The research aims to understand the interplay between identity work, learning processes and organizational capability development in sustaining employability and adaptability in AI-mediated environments. By focusing on how employees actively re-author their professional selves, the study moves beyond accounts centered on automation anxiety, job insecurity or employability perceptions alone.<\/jats:p>\n <\/jats:sec>\n \n Design\/methodology\/approach<\/jats:title>\n Drawing on adult learning theory, career construction theory and dynamic capabilities theory, the study adopts a qualitative narrative inquiry design. Data were collected from 30 employees across finance, retail and public service sectors participating in digital reskilling programs. Analysis of life-history interviews, reflective journals and HR documentation was conducted to trace how individuals engage in \u201creskilling identity work\u201d as they negotiate the transition between legacy expertise and emerging digital roles.<\/jats:p>\n <\/jats:sec>\n \n Findings<\/jats:title>\n The study identifies \u201creskilling identity work\u201d as a key process through which employees reconcile tensions between existing skill repertoires and new digital expectations. Three patterned responses to AI-driven reskilling emerged \u2013 preservation, bridging and transformation \u2013 each reflecting distinct ways employees protect, translate or reconfigure professional selves, going beyond accounts focused solely on fear of redundancy or perceived job insecurity. The results suggest that career adaptability and a supportive, power-sensitive learning culture are critical in determining whether reskilling initiatives foster identity renewal or defensive resistance, thereby influencing organizational effectiveness and capability evolution.<\/jats:p>\n <\/jats:sec>\n \n Practical implications<\/jats:title>\n Findings highlight the need for organizations to design reskilling initiatives that go beyond technical skill training to support identity transitions, career reflection and psychological empowerment. Building a culture of continuous learning and adaptability, which recognizes legacy expertise while creating space for hybrid AI-ready identities, can help align employee identity reconstruction with broader organizational capability goals in the face of AI transformation.<\/jats:p>\n <\/jats:sec>\n \n Originality\/value<\/jats:title>\n This study contributes by integrating identity theory, adult learning and dynamic capabilities perspectives to conceptualize \u201creskilling identity work\u201d as a dynamic, interpretive process within workforce transformation. It advances debates on automation anxiety, job insecurity and identity threats by specifying a meso-level construct that links individual identity work in formal reskilling programs to organizational capability-building and HRM practices in AI-mediated workplaces. In doing so, it offers a nuanced understanding of how employees remake their professional selves during digital transitions, providing both theoretical insight and actionable guidance for sustaining human capability in AI-mediated workplaces.<\/jats:p>\n <\/jats:sec>","DOI":"10.1108\/joepp-12-2025-1109","type":"journal-article","created":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T11:24:45Z","timestamp":1774437885000},"page":"1-19","source":"Crossref","is-referenced-by-count":0,"title":["Digital reskilling and workforce transformation: exploring identity, capability and adaptation in\u00a0AI-mediated workplaces"],"prefix":"10.1108","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1245-4555","authenticated-orcid":true,"given":"Isabel","family":"Barbosa","sequence":"first","affiliation":[{"name":"Portucalense University, Research on Economics, Management and Information Technologies, REMIT , ,","place":["Porto, Portugal"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4153-491X","authenticated-orcid":true,"given":"Elizabeth","family":"Real de Oliveira","sequence":"additional","affiliation":[{"name":"Portucalense University, Research on Economics, Management and Information Technologies, REMIT , ,","place":["Porto, Portugal"]}]}],"member":"140","published-online":{"date-parts":[[2026,3,26]]},"reference":[{"issue":"1","key":"2026032507243803100_ref001","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1177\/1350508407084426","article-title":"Identity matters: reflections on the construction of identity scholarship in organization studies","volume":"15","author":"Alvesson","year":"2008","journal-title":"Organization"},{"issue":"2","key":"2026032507243803100_ref002","doi-asserted-by":"publisher","first-page":"111","DOI":"10.5465\/amd.2017.0034","article-title":"The changing nature of work: careers, identities, and work lives in the 21st century","volume":"3","author":"Barley","year":"2017","journal-title":"Academy of Management Discoveries"},{"issue":"2","key":"2026032507243803100_ref003","doi-asserted-by":"publisher","first-page":"27","DOI":"10.3316\/QRJ0902027","article-title":"Document analysis as a qualitative research method","volume":"9","author":"Bowen","year":"2009","journal-title":"Qualitative Research Journal"},{"issue":"6370","key":"2026032507243803100_ref004","doi-asserted-by":"publisher","first-page":"1530","DOI":"10.1126\/science.aap8062","article-title":"What can machine learning do? 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