Generative Artificial Intelligence (GenAI) is quickly evolving as disruptive technology able to generate text, images, code, summaries, recommendations and other kinds of content. While classical artificial intelligence technologies were mainly used for predictive purposes, GenAI technology allows employees to interact with artificial intelligence using natural language and embed AI into knowledge-intensive tasks. Such technology implies certain consequences regarding productivity, employee learning, creativity, human resources management and organizational performance. But access to GenAI does not mean automatic creation of value by organizations. Results achieved from GenAI use depend on capabilities of employees, nature of tasks, organizational preparedness, control of human over processes and responsible governance. In this conceptual paper, a Human–Generative AI Collaboration Capability (H-GAICC) framework is developed to explore ways of translating GenAI capabilities into employee and organizational outcomes. Using Resource-Based View, Socio-technical Systems Theory, Automation-Augmentation Approach, Job Demands–Resources Theory and Dynamic Capabilities Theory, the paper argues that GenAI capability, AI literacy of employees and organizational preparedness determine quality of human-AI collaboration. Collaboration effectiveness has an impact on productivity, creativity, learning, and employee experience and therefore can have a bearing on organizational performance. Trust, responsible AI governance, task complexity, and human supervision are put forward as moderating factors. Twelve propositions are formulated for the purpose of guiding further empirical work. This paper makes a contribution to the growing GenAI literature by directing the focus away from mere AI adoption to its organizational configuration with humans.
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Prof. Sapna Sugandha
326-337
10.5281/zenodo.23267278
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