AI and the Future of Work

Permanent URI for this collectionhttps://hdl.handle.net/10125/112397

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Now showing 1 - 12 of 12

Understanding Human-AI Task Delegation in User Support
(2026-01-06) Vuolasto, Jaakko; Koskinen, Kari
User support is of growing importance as companies are more dependent on externally provided digital solutions. Artificial intelligence (AI) is proposed as a tool for improving the efficiency and automation of user support. However, understanding the essence of user support is required before any of its activities can be delegated to AI agents. Based on 17 interviews of professionals involved in user support, our research reveals the granularity of support process, which has implications for the use of different AI tools. Particularly, aligning with the user is challenging for the AI tools. The research argues for the inclusion of the human dimension into the attributes of task delegation to information systems (IS) artefacts, while also further demonstrating that there is a long way to go before AI can be given full agency in user support.
Beyond Training: How Workers Discover Value in Enterprise AI
(2026-01-06) Sahni, Riya; Chilton, Lydia
While organizations continue to invest in enterprise AI, little is known about how individual employees find valuable use cases once these tools are deployed. We present an exploratory interview study of 10 experienced U.S. professionals using M365 Copilot and interpret accounts through Rogers’ Diffusion of Innovations to examine where value appears and how use cases are found and shared. Findings reveal a strong preference for informal learning methods over structured training. No participants (0/10) reported formal training as their primary way of learning; most relied on trial-and-error (8/10) and on exchanging tips with colleagues (6/10). Participants most often used M365 Copilot for note-taking/summarization, information retrieval/explanation, and writing. They also reported perceived gains in efficiency but low confidence in mastering more advanced features. The paper discusses social learning strategies and outlines implementable steps for organizations to support the discovery of high-value use cases with available enterprise AI tools.
Adapting or Avoiding AI: A Coping Theory Approach to Employee Responses in the Future of Work
(2026-01-06) Everson, Bob
This qualitative research study explores how employees in the healthcare technology industry perceive and cope with AI implementation in the workplace, using Lazarus and Folkman (1984) Coping Theory. Through ethnographic semi-structured interviews with twenty-three white-collared professionals, five core themes emerged: Mixed Attitudes Toward AI, Anticipated Benefits of AI Automation, Organizational Gaps in Communication and Support, Support for Continuous Learning, and Privacy and Security Concerns, While employees acknowledged the benefits of AI for efficiency and task reallocation, they also expressed concerns about job security, privacy, and a lack of organizational support. Participants employed both problem-focused coping (e.g., self-directed learning) and emotion-focused coping (e.g., skepticism, avoidance). The research highlights transparent communication, secure AI deployment, and sustained learning support. These findings contribute to the broader discourse on AI adoption and provide actionable strategies for supporting ethical, employee focused AI integration within the workplace.
Generative AI Competencies in Demand: A Job Market Analysis of Knowledge, Skills, and Abilities
(2026-01-06) Bluemelhuber, Benedikt; Langer, Benedict
The emergence of generative AI (GenAI) technologies and their rapid organizational adoption are transforming workforce requirements, yet little is known about the evolving competencies needed for GenAI-related roles. It is unclear which knowledge, skills, and abilities (KSAs) are needed to work in this emerging field. To address this knowledge gap, we processed 22,352 job postings from Stepstone in the field of GenAI. Through a text mining approach combining BERTopic modeling, clustering, and expert assessment, we identified 32 distinct KSA topics and classified them into business, technical, and analytical domains. Our findings show that GenAI roles require a diverse set of skills: new GenAI-specific competencies combined with established expertise in technical domains and business functions. We contribute to research by empirically demonstrating which competencies are demanded to work successfully in the GenAI field. Understanding these competencies can guide organizations in workforce planning and support professionals in targeted skill development.
The Developmental Bypass: How AI Assistance Undermines Human Growth
(2026-01-06) De Vreede, Triparna; De Vreede, Gj; Siemon, Dominik; Ebel, Philipp Alexander; Elshan, Edona
This study examines how AI writing assistance affects human development, revealing a fundamental paradox: while AI tools improve immediate output quality, they undermine the psychological processes through which writers grow. Drawing on self- determination theory, we conducted an experiment (N=206) comparing self-editing to varying levels of AI assistance. Results demonstrate that self-editing produced significantly greater gains in writer identity (M=0.88) compared to low (M=0.54) and high (M=0.43) AI contribution conditions. Self-efficacy gains showed even starker differences, with self-editing yielding meaningful growth (M=0.41) while AI conditions produced negligible gains (<0.10). Most surprisingly, domain expertise transformed from an asset in self-editing to a liability under AI assistance, as experts defensively disengaged to protect their threatened identity. These findings suggest AI creates a "developmental bypass"—eliminating the productive struggle necessary for growth. We discuss implications for designing AI systems that enhance rather than diminish human potential, arguing for preserving developmental experiences in human-AI collaboration.
The Changing Face of Leadership: How AI is Transforming Management
(2026-01-06) Hartling, Bryan; Mandviwalla, Munir
This study explores how AI is transforming leadership roles within organizations by extracting insights via a thematic analysis of interviews with white-collar professionals and leaders as well as CEOs of leading AI firms. Five strategic themes were identified that describe how leaders adapt to and utilize AI in the workplace today. The findings suggest that successful leaders develop new, collaborative interactions with AI which emphasize agility, critical evaluation, and human-centric communication. To guide these transformations, this paper proposes a Leader-AI Collaboration Framework and five Leader-AI Collaboration Design Principles. These practical contributions provide direction to organizations navigating the challenges experienced in leadership roles during rapid AI evolution and contribute to the theoretical conversation on human-AI collaboration. This study illustrates that in order to successfully integrate AI into leadership roles and capture synergies, systemic organizational shifts must coincide alongside technical and behavioral advancements that foster skill development, job-crafting, and Leader-AI collaboration systems.
Harder or Easier? Impact of the AI Exposure Index on China’s Online Labor Market: An Occupation-Based Perspective
(2026-01-06) Shi, Hao; Wang, Tianmei
This study explores the impact of artificial intelligence (AI) on the labor market from the perspective of occupations. We innovatively construct an AI exposure index based on local Chinese data by measuring the semantic similarity between AI capabilities and occupational task requirements using large language model and Sentence-BERT models. Using the retrieval-augmented generation method, we analyze online job postings in China from 2019 to 2024, examining the impact of AI exposure on employment opportunities, occupational wages, and job matching. Results show that overall, AI exposure has steadily increased across tasks and occupations in China. AI exposure shows significant substitution effect on traditional occupations but no significant creation effect on emerging ones. High AI exposure is associated with low wages, especially in occupations with high proportion of routine tasks, although task complexity mitigates this effect. In addition, AI exposure reduces job filling rates and prolongs recruitment time, indicating increased matching friction.
Reversing the Knobe Effect: AI Takes Less Blame for Harmful Decisions
(2026-01-06) Rosengren, Warren; Dennis, Alan; Kim, Antino
When a decision leads to harmful side effects, observers typically judge the action as more intentional than if it results in beneficial side effects, an asymmetry known as the “Knobe effect.” Using an online experiment, we examine whether this effect extends from human decision-makers to artificial intelligence (AI) systems. We also investigate the effects on the attribution of praise or blame to the decision-maker or the company. The results show that, with humans, harmful side effects led to greater perceived intentionality than beneficial side effects and resulted in more blame than praise. For AI systems, however, the effect reversed—beneficial outcomes led to greater perceived intentionality than harmful outcomes. We also find that the traditional Knobe effect holds true for company attribution regardless of who or what makes the decision. Our research contributes to the Knobe effect by showing that the effect reverses for AI and highlighting that organizations, not algorithms, are held publicly accountable for AI missteps.
Towards a Blueprint for Practitioners to Enhance Digital Awareness: Preliminary Observations on AI-Related Workplace Stressors
(2026-01-06) Illgen, Katharina-Maria; Hein, Laura; Kolchmeyer, Nina; Pham, Kim; Thomas, Oliver
The growing integration of Artificial Intelligence (AI) in the workplace introduces both positive and negative stressors that affect employee well-being and organizational efficiency, requiring effective management of these dynamics. Empirical evidence on the effectiveness of digital awareness training in mitigating AI-related stress remains scarce. This pilot study develops a digital awareness training and evaluates its impact on AI-related stress appraisal, including techno-distress, techno-eustress, and attitudes towards AI (fear vs. acceptance). Results from a heterogeneous sample of employees (n = 32) from small and medium-sized enterprises indicate a significant reduction in techno-overload, with tendencies towards improved work facilitation and information literacy. These results offer an initial, evidence-based blueprint for digital awareness interventions, offering actionable insights for reducing techno-distress and promoting digital literacy. Further longitudinal and repeated-intervention studies are needed to validate and sustain these effects over time.
Sweeping Up Digital Dirt: Navigating Immediate Working Experiences to Alleviate the Precarity of Online Crowd Work Conditions
(2026-01-06) Schafheitle, Simon; Van Zoonen, Ward; Wagner, Brian
The increasing use of artificial intelligence (AI) systems across sectors relies on online crowd work to generate, label, and curate the large datasets required for AI functionality. However, this form of labor is frequently conducted under precarious and ethically contentious conditions. While these systemic issues have received scholarly attention, little empirical research explores their psychological impact on workers. This study applies the Psychology of Working Theory (PWT) to examine how the satisfaction of core psychological needs, i.e., autonomy and belongingness, shapes work enjoyment and frustration among 1,291 European crowd workers engaged in such AI-related tasks. Findings indicate that autonomy and belongingness play a critical role in fostering positive crowd work experiences and enabling psychological resilience in the face of adverse conditions. This research contributes to understanding how AI is reshaping labor and offers theoretical and practical guidance for the ethical and human-centered design of AI-supported work systems
Digital Coordination of Work: How Data, Intelligence, and Transparency Reshape Organizational Practice
(2026-01-06) Jabbari, Araz; Lasfer, Assia; Kazantsev, Nikolai
Digital advancements have transformed how people work, interact, and experience their environments, demanding a revised understanding of work coordination. While current coordination theories remain influential, they struggle to explain emerging digital work practices. This paper critically examines the literature that applies Malone and Crowston’s coordination theory, uncovering their underlying assumptions. Building on concepts like ontological reversal and organizational digitization, we identify key needs for an updated theory. Our goal is to spark renewed interest in long-standing coordination theories and encourage scholars to explore how coordination theories can evolve to address the realities of today’s digital work environments.
Introduction to the Minitrack on AI and the Future of Work
(2026-01-06) De Vreede, Triparna; Siemon, Dominik; Cheng, Xusen; Singh, Vivek Kumar

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