Adapting to Algorithmic Workplaces: Human–Automation Collaboration and Job Redesign for Improved Employee Task Performance

Algorithmic management (AM) automated systems for monitoring, scheduling and decision-making have quickly altered banking workplaces using AI-driven platforms and data-driven performance tools. While productivity improvements have been well established, the human-centered consequences of AM remain unknown, specifically how employees adapt, interact with automation and maintain their performance. This study bridges the gap by looking into human–automation collaboration and job redesign in algorithmically managed banking environments. The study asks: How can algorithmic systems influence work structure, experience, outcomes and how can collaboration increase productivity while protecting employee well-being? The analysis is based on three sub-questions: How do employees interact with AM systems in their daily tasks? What digital, data and socio-emotional competencies promote autonomy and engagement? How can HR managers adapt work and training to strike a balance between efficiency and human values? The study uses a quantitative methodology in Nigerian banking sectors, where algorithmic decision tools are critical for credit evaluation, compliance and performance analytics. The sample will comprise relationship officers, credit analyzers and digital operations personnel. Data will be gathered using validated survey tools that measure AM functions (monitoring, scheduling, evaluation), job demands/resources (workload, autonomy, technostress) and outcome (engagement and performance). Analytical techniques will include confirmatory factor analysis and structural equation modeling to test relationships within the proposed framework. The paper makes a new contribution by providing a multidimensional sociotechnical framework for human–automation collaboration in banking. This approach combines job design principles, competency development methodologies and algorithmic transparency rules, providing HR practitioners and policymakers with actionable insights to improve AM systems while fostering autonomy, capability growth and organizational resilience.