The adoption of AI technologies and data to support workplace learning in the corporate sector: A scoping review

Abstract AI and data‐driven technologies are increasingly shaping work and skill demands, with growing evidence that AI can support workplace learning and professional development. However, research on how AI and data are integrated into workplace learning remains dispersed across disciplines, technologies and organisational contexts. This scoping review aims to review empirical studies about the integration of AI and data in workplace learning to map current concepts and practices while identifying opportunities and gaps that can guide future research and adoption. Journal articles or full conference papers indexed in the EBSCOHost, ERIC, Proquest One Business, Proquest One Academic, Scopus and Web of Science and Google Scholar were included if they discussed technology‐enhanced learning in the corporate/business, mixed‐sector or sector‐unspecified workplace contexts. Fifty‐nine studies published in English between 2018 and 2025 were included for data extraction and analysed using inductive thematic analysis. The findings show that workplace learning is increasingly organised within digital learning ecosystems, in which AI‐enabled functions are embedded across learning management systems, communication tools, distribution platforms and immersive and simulation‐based technologies. Within these ecosystems, AI‐enabled functions support profiling and skill gap analysis, personalised training, automated and real‐time feedback and intelligent recommendation. Collectively, these tools demonstrate potential to enhance learner engagement and retention, support continuous and work‐embedded learning, and enable more responsive upskilling and reskilling in workplaces. The review further reveals that at the same time, organisations are increasingly collecting meaningful data such as demographics, job profiles, performance evaluations, skills assessments, training histories and digital traces to identify skill gaps, personalise learning and support feedback. On the other hand, the review found some challenges, including fragmented ecosystems, limited interoperability and unresolved ethical concerns regarding data use, privacy and learner autonomy. Theoretically, this review proposes a revised conceptualisation of workplace learning by showing that it increasingly occurs within human‐AI‐data ecosystems. Practically, the findings highlight the importance of AI‐enabled skills intelligence for strategic workforce development, as well as the critical need for transparent and learner‐centred governance of workplace learning data. The review concludes by identifying key ethical, methodological and theoretical research gaps and suggests implications for future research. Context and implications Rationale for this study: AI and data‐driven technologies are increasingly shaping the landscape of professional learning and development, yet research on their integration into workplace learning remains fragmented. This scoping review maps empirical research on AI and data use in workplace learning to identify current concepts, practices, gaps and directions for future research and adoption in evolving hybrid work contexts. Why the new findings matter: The findings show that AI‐enabled features are embedded across workplace learning ecosystems, supporting learning beyond formal training and into daily work practices while highlighting persistent fragmentation across systems. They also reveal an emerging shift towards human–AI collaboration and flag key concerns for future research related to data governance and responsible AI use. Implications for researchers and practitioners: For researchers, this review reveals substantial ethical research gaps and calls for further research on ethics governance guidelines or policy to address ethical risks such as data privacy, surveillance, misinformation and overreliance on generative AI. The review also proposes a revised framework of workplace learning shaped by both technical–organisational and socio‐cultural learning environments, by conceptualising learning as situated within human–AI–data ecosystems, and calls for further empirical evidence of this model. For human resource development (HRD) and practitioners, the review highlights the importance of skills intelligence, interoperable systems and ethical governance to support targeted upskilling while maintaining trust, transparency and learner‐centred design in AI‐enabled workplace learning ecosystems.