Generative AI for Organizational, Societal, and Emotional Relationships and Partnerships

Permanent URI for this collectionhttps://hdl.handle.net/10125/112507

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    Generative Agents at Work: Redesigning Administrative Processes at the German Federal Employment Agency
    (2026-01-06) Chircu, Alina; Czarnecki , Christian; Neumüller, Tim; Sebrak, Sebastian; Sultanow, Eldar; Winzer, Florian
    The integration of Generative Artificial Intelligence (GenAI) in public administration offers new ways to handle service and technology complexity while meeting high standards. This paper presents a case study from the German Federal Employment Agency, where a multi-agent system using local large language models (LLMs) automates the conversion of information technology (IT) change requests into structured IT development tasks (Jira tickets). Specialized agents interpret requirements, break them into tasks, and generate consistent entries. This improves organizational efficiency, reduces routine work, and outperforms traditional automation by enabling context-aware reasoning and dialogue, all within a secure, on-premise environment. While motivated by Germany’s demographic challenges, the findings have global relevance for public sector modernization and automation.
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    Integrating Generative AI into Business Operations: A Comprehensive Analysis of Use Cases as Lighthouse Projects
    (2026-01-06) Bluemelhuber, Benedikt; Thomandl, Leon; Langer, Benedict
    As Generative AI (GenAI) applications evolve, organizations face the challenge to decide which applications to adopt. This study supports organizations by mapping 63 GenAI use cases across a company's value chain. Through a systematic literature review complemented by a multiple case study with 33 semi-structured interviews with industry experts, it seeks to provide companies with actionable insights for integrating GenAI technologies, thereby enhancing efficiency and productivity. To provide a starting point for GenAI adoption, five applications were identified as particularly high-potential lighthouse projects: Enterprise GPT/Copilot Systems, which serve as company-internal assistants for knowledge management and content generation; Customer Service Chatbots utilizing uncritical, openly accessible data to enhance customer satisfaction; Coding Assistance Tools that automate routine coding tasks, increasing developer productivity; Input Management Systems for processing and classifying incoming information like customer complaints and emails; and Marketing Copy Generators for creating personalized marketing materials efficiently.
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    From AI Literacy to AI Use: Evidence from a Multi-Organization Upskilling Program
    (2026-01-06) Wimmer, Christian; Treffers, Theresa; Welpe, Isabell
    As artificial intelligence (AI) becomes integral to organizational processes, employee adoption emerges as a key determinant of a successful implementation. This study draws on the Unified Theory of Acceptance and Use of Technology (UTAUT) and extends it with AI-specific drivers, AI literacy and attitude toward AI, alongside established factors such as subjective norm and effort expectancy, while exploring the moderating roles of the demographic factors age and gender. We surveyed 180 employees from 39 organizations in Bavaria, Germany, before and after a five-month AI upskilling program. Results show that AI literacy and subjective norm were the strongest predictors of AI use. Age moderated the literacy-use relationship, with diminished effects for older employees. Pre-post training comparisons showed significant gains in AI literacy, AI use, and AI-supported task share. These findings refine UTAUT by incorporating AI literacy and offer practical insights for designing targeted, age-sensitive interventions to drive workplace AI usage.
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    Seven Axioms on the Nature of Generative AI: Laying the Foundation for a Genuine Understanding of Machine Intelligence
    (2026-01-06) Peter, Sandra; Riemer, Kai; West, Jevin
    We challenge tendencies to evaluate Generative AI (GenAI), and large langue models (LLMs) in particular, through the lens of human intelligence, arguing that such anthropomorphic renditions constitute a fundamental category error. We do not dispute LLMs' capabilities, but contend that analogies to human intelligence obscure its true nature. This leads to unrealistic expectations and risks overlooking unhuman-like capabilities. We propose seven axioms to characterize GenAI and LLMs: (1) no direct relationship to truth, (2) no connection to the physical world, (3) absence of subjectivity, (4) lack of temporality, (5) no intentionality, (6) purely relational information representation, and (7) complex pattern prediction. These axioms, informed by existential philosophy, linguistics, and cognitive science, reveal GenAI as an "alien intelligence" fundamentally different from human cognition. We argue for developing a genuine machine psychology that embraces GenAI's alien nature rather than constraining it within human frameworks, to unlock applications that leverage its unique capabilities.
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    Engineering Better Requirements: Understanding the Impact of GenAI on Task Performance and Quality in Requirements Engineering
    (2026-01-06) Bluemelhuber, Benedikt; Junker, Sebastian
    In order to deliver high-quality software systems, organizations utilize Requirements Engineering (RE) as a foundation for aligning development with stakeholder needs. With advances in Generative Artificial Intelligence (GenAI), potential emerges to augment RE processes to improve both task completion time and requirements quality. As GenAI-powered tools become more common in industry practice, our research examines under which circumstances GenAI supports RE tasks. Through our online experiment with 41 RE professionals from a manufacturing company, we demonstrate that GenAI assistance significantly reduces task completion time across different complexity levels and improves quality, particularly in simpler tasks. These results indicate that while GenAI effectively enhances RE efficiency in all contexts, its contribution to quality varies with task complexity. This suggests that organizations should strategically implement GenAI tools in RE workflows, recognizing both their productivity benefits and the continued importance of human expertise for more complex requirement scenarios.
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