Look: AI at Work! - Analysing Key Aspects of AI-support at the Work Place

2025-09-02

AI Summary

This study addresses the dual technological and psychological challenges impeding AI adoption in workplace settings. Drawing on 12 real-world implementation cases, it adopts an integrated lens of technical deployment and AI literacy development to systematically analyze critical factors in AI system design: high-quality training data curation, embedding of domain-specific human expertise, and human–AI collaborative workflow architecture. It empirically investigates the psychological mechanisms—such as perceived usefulness, cognitive openness, and interpersonal trust—that shape employee acceptance and sustained engagement with AI tools. The study introduces a novel interdisciplinary framework that jointly optimizes technical feasibility and human-centered adaptability. It delivers actionable guidelines for managers, AI developers, and frontline workers, thereby enhancing AI literacy and cross-role collaboration across stakeholder groups. The findings provide both theoretical foundations and scalable implementation pathways for designing sustainable, human-aligned intelligent work systems. (149 words)

Technology Category

• Humans and AI: Intelligent User Interfaces
• Philosophy and Ethics of AI: AI & Jobs/Labor
• Intelligent Robots: Human-Robot Interaction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

In this paper we present an analysis of technological and psychological factors of applying artificial intelligence (AI) at the work place. We do so for a number of twelve application cases in the context of a project where AI is integrated at work places and in work systems of the future. From a technological point of view we mainly look at the areas of AI that the applications are concerned with. This allows to formulate recommendations in terms of what to look at in developing an AI application and what to pay attention to with regards to building AI literacy with different stakeholders using the system. This includes the importance of high-quality data for training learning-based systems as well as the integration of human expertise, especially with knowledge-based systems. In terms of the psychological factors we derive research questions to investigate in the development of AI supported work systems and to consider in future work, mainly concerned with topics such as acceptance, openness, and trust in an AI system.

Problem

• Analyzing technological factors of AI workplace integration
• Examining psychological aspects like trust and acceptance
• Providing recommendations for developing AI applications

Innovation

• AI integration in future work systems
• High-quality data for learning-based systems
• Combining human expertise with knowledge-based systems