Purpose
In this research paper we aim to answer the following research questions: How do mid-management professionals perceive the impact of data science disruption on their learning & development, employability and skill relevance? What organizational strategies can support continuous learning, reskilling and retention of mid-management talents amid digital disruption?

Design/methodology/approach
A qualitative approach is taken, with insights gathered through interviews and focus group discussions among mid-management professionals and organizational experts (HR professionals and team leads). Data were collected in two phases (34), in phase 1, experiences of mid-management professionals were captured on AI-ML technology and in phase 2, organizational responses and strategic interventions

Findings
The majority of managers felt that it is not feasible for everyone to dedicate cognitive cycles and personal time to ramping up their competency set from within. This requires a significant investment in learning, which can disrupt the work-life balance. There are calls for more sophisticated interventions than short workshops teaching managers how to navigate disruption effectively. Organizations must invest in comprehensive learning initiatives beyond short-term workshops through auxiliary tailored upskilling and career development paths.

Research limitations/implications
The online learning ecosystem and democratization of resources supporting rapid upskilling. Experienced practitioners often struggle to stay up-to-date, highlighting the need for structured interventions and ongoing education. Enterprises need to address the unique needs of managers, encourage lifelong learning, and cultivate environments where technical capabilities are complemented by social skills.

Practical implications
Organizational recommendations include increased investment in learning, structured upskilling programs, recognition of experienced staff, and the development that balance technical expertise with domain knowledge. Experienced practitioners often struggle to stay up-to-date, highlighting the need for structured interventions and ongoing education. Enterprises need to address the unique needs of managers, encourage lifelong learning, and cultivate environments where technical capabilities are complemented by social skills.

Originality/value
The evolving digital landscape, particularly the rapid advancement of data science (in Artificial Intelligence (AI) and Machine Learning (ML)), is reshaping organizational structures and talent management practices. The learning and development (L&D) ecosystem itself is undergoing transformation due to AI-ML-led digitalization. Unlearning and relearning approaches are explored under Talent Management strategies.