This paper explores the hypothesis that transformational leadership strategy (TLS) is related to employee engagement in the branch-banking setting of a developing economy. Based on a quantitative survey of bank managers and employees (matched pairs; n = 61) and the available measures, reliability and validity have been measured, and the hypothesized TLS-engagement path has been tested through regression. Although there are recent studies and reviews that usually indicate positive links between TLS and engagement (e.g., meta-and narrative syntheses) (Bakker et al., 2023; Grah et al., 2024), our findings indicate a weak, statistically insignificant effect. The result indicates that leadership can be less motivated in banking due to contextual contingencies, including reward systems, legacy processes, or culture. We present hypotheses to apply to the job demands-resources (JD-R) theory, and in this case, leadership as a job resource might not be effective without other resources. We provide some steps that banks should take to balance leadership development with job redesign and incentives. We end with restrictions (convenience sampling, cross-sectional design) and future research (longitudinal and multi-level design and studies in other industries). Such insights provide a valuable boundary condition to other existing studies of TLS-engagement in other industries and different regions (Decuypere & Schaufeli, 2021; Bakker et al., 2023; Grah et al., 2024).

Higher education institutions face accelerating skills change, expanding digital delivery, and rising expectations for workforce relevance. This study examines how machine intelligence can support predictive–adaptive, data-driven curriculum design by integrating learning analytics, adaptive learning platforms, and institutional decision systems. Evidence from the analysis indicates that predictive models strengthen alignment between program outcomes and labor-market needs, while adaptive learning features improve student engagement and learning support. At the institutional level, analytics-enabled planning enhances resource allocation, training design, and operational efficiency. Qualitative insights further highlight perceived benefits (personalization, timely feedback, and transparency) alongside persistent barriers, including data quality constraints, implementation capacity, governance and ethics, and uneven access that may reinforce inequities if not addressed. The findings imply that effective adoption requires clear data governance, stakeholder readiness, faculty development, and equity-by-design safeguards. Overall, machine intelligence is positioned as an enabling infrastructure for continuous curriculum improvement when paired with responsible oversight and inclusive implementation.