Autonomy at the Crossroads: Knowledge Workers Teamed with Intelligent Machines: A Qualitative Systematic Review

Abstract: This study aims to identify risks in adopting artificial intelligence (AI) for organizational decision-making by examining empirical studies. AI, is increasingly applied to automate tasks and decisions which were traditionally made by humans, posing challenges to sense of autonomy. Design/methodology – A total of 28 empirical studies were selected using predefined inclusion and exclusion criteria. To this end, this research systematically explored the processes of inquiry, identification, selection, critical appraisal, and the synthesis of empirical studies. This study is undertaken to address the following primary inquiries: (1) What is the direction of the observed effect? (2) What is the magnitude of the effect within the inclusion criteria? (3) Does the effect exhibit a consistent pattern across the spectrum of studies encompassed in the analysis? (4) What is the level of evidentiary robustness underlying the discovered effect? Findings – This content analysis interpretated within task-technology fit (TTF) model revealed that AI adoption represents a promising outlook for the future of human-AI teams. Anchoring on reliable data, this qualitative systematic review informs knowledge workers and leaders on adoption of AI systems and how it positively influences their working processes. Contributions/value – This research conducted a structured analysis to reveal the gap between collective perception of AI adoption, and what leaders and knowledge workers have experienced in relying on AI systems. AI tools are becoming more autonomous therefore a true representation of human-AI team interaction must be displayed. By uncovering the diverse approaches of leaders and the reactions of knowledge workers to AI integration, this paper contributes to a deeper understanding of the evolving landscape of working in the age of AI. The provided insights can assist organizations in harnessing the potential of AI while maintaining a healthy balance of autonomy within their domain.KEYWORDS Leadership, AI-Driven Decisions, Problem-Solving, Digital Autonomy, Hybrid intelligence, Human-Machine Teams, Qualitative Systematic Literature Review.

Keywords: AI-Driven Decisions, Hybrid intelligence, Digital Autonomy, Human-Machine Teams, Leadership, Qualitative Systematic Literature Review.

DOI: 10.54941/ahfe1005458

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