AI, Organizing, and Management

Permanent URI for this collectionhttps://hdl.handle.net/10125/112532

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Now showing 1 - 13 of 13

Just, Adaptive and Meaningful (JAM): Energy Use Predictions for the Built Environment through Synthetic Data
(2026-01-06) Eidenskog, Maria; Glad, Wiktoria; Hajisharif, Saghi; Johari, Fatemeh; Vrotsou, Katerina
This paper aims to develop a framework for generating synthetic data for the Swedish built environment to improve energy predictions and peak load management in heating systems. Heating and cooling are central aspects of reducing energy use in the residential sector. However, predicting the energy performance of buildings is currently a difficult task, partly due to energy use data from end-users being outdated and sometimes missing. In the end, we will contribute to future energy systems in a broader sense by developing a socio-technical and ethical methodological framework for working with synthetic energy data. We will map available data together with stakeholders' needs. Data will be collected and prepared as training data to develop and evaluate a model for synthetic data. The approach is interdisciplinary which will ensure the integration of socio-technical, ethical and gendered aspects of energy use and synthetic data.
Towards a Sociomaterial Theory of Interfacing AI Data Infrastructures
(2026-01-06) Gupta, Anushri; Pujadas, Roser; Venters , Will
The implementation of advanced data analytics and AI in organisations to support decision-making has led to the development of rich data infrastructures. In this context, real-time operational data is increasingly perceived as a valuable source of data. However, leveraging this data demands two distinct technologies and respective groups to interact in new ways – Information Technology (IT) and Operational Technology (OT). We study how interfacing IT-OT helps further our understanding of the process of building data infrastructures. Drawing on Pickering’s (1995) Mangle of Practice, and his recent work on cybernetics, we conduct a qualitative study of interfacing IT-OT within a property management company and develop a sociomaterial conceptualization of interfacing which describes the ‘dance of agency’ (human, non-human) in both IT and OT domains to transition from material agency defined by knowledge from human heuristics to material agency from AI.
Beyond Accuracy: Rethinking the Value of AI in Decision-Making Through Baseball’s Automated Ball-Strike (ABS) System
(2026-01-06) Kamino, Waki; Wang, Andrea; Sabanovic, Selma; Jung, Malte F.
This paper examines how organisations integrate AI systems to support decision making by studying Major League Baseball’s multi-year experimentation with the Automated Ball-Strike (ABS) system – commonly known as ”robot umpires”. While automating the strike zone may appear to be a straightforward technical task, our study reveals that it entails complex practices of negotiation and meaning making over time. Rather than the simple adoption of a decision aid, the implementation of ABS became a site of organisational sensemaking. We argue for a need to move beyond user-centric models toward stakeholder-centric, contextually embedded frameworks for understanding AI in organisational decision-making.
Reducing Analytical Opacity in Decision Support: An AI-Enabled Framework for Traceable and Explainable Reporting
(2026-01-06) Yu, Mingqin; Tan, Felix; Rabhi, Fethi
Organizations increasingly rely on analytical metrics—computed indicators derived from data and models—to support ESG decision-making and disclosure. Yet these metrics are often produced through opaque workflows, making it difficult to trace their origin, recompute their values, or explain their meaning. This lack of transparency undermines auditability, adaptability, and stakeholder trust. To address this, we propose an analytical capability framework structured around three core functions: traceability, computability, and explainability. The framework integrates semantic technologies such as knowledge graphs with AI components like large language models to enhance transparency across the ESG metric lifecycle. We evaluate this framework through a positivist case study of WP, a leading financial institution in the Asia-Pacific region, using empirical disclosures to assess explanatory adequacy. Our findings show how capability gaps can be systematically diagnosed and addressed through system redesign. The proposed model supports more interpretable and accountable ESG reporting—meeting critical needs in both current practice and emerging regulatory expectations.
Disruption or Dystopia? Assessing Automated Leadership for Traditional Work Contexts
(2026-01-06) Jünke, Annabel; Möller, Frederik; Robra-Bissantz, Susanne
In the wake of digitalization and automation, organizations are increasingly exploring automated leadership to enhance efficiency and standardization. However, while automated leadership systems promise operational improvements, they raise ethical, social, and technological concerns requiring careful consideration. This study presents a technology assessment based on 24 qualitative interviews with leaders to evaluate the potential consequences of automated leadership systems for traditional work contexts. The findings indicate significant economic benefits for organizations, such as efficiency gains or cost reduction. On the other hand, they indicate high risks for employees, such as psychological stress. Specifically, economic advantages were emphasized disproportionately more compared to other dimensions, such as social and technological risks. By providing insights into both benefits and risks, this research underscores the need for a balanced and human- centered approach to developing and implementing automated leadership systems.
Hybrid Teaming with Generative AI: Role Dynamics and Their Consequences for Task Performance
(2026-01-06) Saller, Rosanna; Reichl, Magdalena; Fleischmann, Carolin
This study investigates how the integration of a task-specific responsive GenAI agent influences team role distribution and task execution. Building on collaboration and role theory, we conducted a qualitative experiment with six student teams, comparing experimental groups with AI access and control groups working with search engines. The results show that the effectiveness of GenAI depends on how critically and collaboratively teams engage with it. Teams that over-relied on AI - either due to uncertainty or lack of initiative - produced weaker outcomes than control groups, whereas those who challenged and contextualized AI outputs achieved higher performance. Furthermore, the presence of GenAI altered team role dynamics, prompting reallocation and adaptation of human responsibilities. Our findings contribute to the emerging field of Human-AI collaboration by exploring early-stage interactions and behavior towards AI in team settings. The study underscores the importance of role clarity and critical engagement for maximizing AI’s potential, and aims to contribute to building a continuum of understanding of Human-AI teaming.
Hiring Tomorrow’s Talents: How Generative Artificial Intelligence Transforms Human Resources Recruitment
(2026-01-06) Banh, Leonardo; Rex, Alexander; Strobel, Gero; Urbach, Nils
The global talent shortage has become a universal challenge, prompting practitioners and researchers to explore digital innovations as potential solutions for acquiring the right talents. However, the role of emerging technologies like generative artificial intelligence (AI) in human resources (HR) remains largely uncharted territory. This article investigates generative AI’s transformative potential to augment recruiters’ daily operations. Through a qualitative interview study, we derive and illuminate the opportunities of generative AI within the recruitment domain, shedding light on its promising opportunities but also addressing inherent challenges. The findings of this study propose a theoretical model of generative AI in recruitment and how it empowers recruiters in their daily tasks to recruit tomorrow’s talents.
Do Investors Trust in AI Investments of European Companies?
(2026-01-06) Keil, Samuel; Martin, Pascal; Schiereck, Dirk
Announcements of emerging technologies often lead to notable stock market reactions, with Artificial Intelligence standing out due to its transformative potential and growing regulatory attention. Yet, most research on investor responses to AI disclosures focuses on U.S. firms, leaving the distinct European context unexplored. Using a short-term event study of 526 AI-related announcements by STOXX Europe 600 firms between 2015 and 2024, we report a significantly negative average stock return of -0.176% within a three-day window. However, announcements detailing specific AI technologies, involving collaborations with AI specialists, or made after the release of ChatGPT are associated with less negative reactions. In contrast, references to EU regulatory frameworks like the AI Act show no significant effect. Our findings confirm generally negative investor reactions to AI announcements but show that in Europe, strategic factors such as announcement specificity, collaborations, and timing also significantly mitigate these effects.
Agility Under Fire: Barriers to Off-the-Shelf AI Adaptation
(2026-01-06) Wendt, Maurice
This study examines how off-the-shelf AI affects organizational agility in Germany’s Cooperative Financial Network. Drawing on 22 interviews across banks, service providers, and consultants and using a Gioia approach, we surface a dual lock-in: hyperscalers anchor a standardized core while a centralized integrator enforces uniform roadmaps and update cycles. Together with technological rigidity, layered bureaucracy, and data-governance frictions, this limits local sensing, rapid experimentation, and reconfiguration. Our contribution is conceptual: based on qualitative analysis, we integrate these empirically derived concepts into a framework that explains mechanisms and boundary conditions under which standardization constrains agility. We also outline design principles for a hybrid architecture, standardized backbone plus modular extensions, paired with dual governance and data contracts. The paper refines the agility-standardization debate and offers actionable guidance for financial networks seeking scale efficiency and organizational agility.
Artificial Intelligence, Upper Echelons, and Financial Performance: An Empirical Study of European Software Companies
(2026-01-06) Ibrahimli, Ulvi; Wirsing, Benedikt; Winkelmann, Axel
Artificial Intelligence has become a prevailing corporate paradigm, particularly for software firms. Despite the intense race to have the upper hand in the rapid integration, compatibility with the information systems in place and the economic pay-off remained peripheral in IS research and practice. Moreover, the role of upper-echelon characteristics in shaping the financial outcomes of AI adoption remains uncertain. Using longitudinal data, this study empirically explores the bottom line—the economic performance of European software firms that integrate AI into their current enterprise systems. Findings reveal a negative relationship between AI integration and firm performance; however, this effect is significantly moderated by the upper echelon's characteristics. The study’s findings contribute to the literature by establishing AI systems as a key co-determinant of financial performance. It also has practical implications, such that it highlights lightweight integration and value alignment problems that lead to adverse performance pay-offs.
How to Achieve Concrete Results in AI Deployment? A Framework and Learnings from Industry-Leading Companies
(2026-01-06) Ruotsalainen, Seppo; Hokkanen, Päivi; Porras, Jari; Kuivalainen, Olli
Interest in AI has grown dramatically in the last few years, but peer-reviewed research on AI deployment in companies is still scarce. The extant few academic articles indicate that AI deployment is still at the early stages in firms. In this research, we build on semistructured interviews of chief information officers and study the AI deployment of international stock-listed companies. We draw from the abductive analysis and the Dynamic Capabilities view to develop and explain the findings. The results demonstrate that companies are at very different stages of deployment. We identify and define eight capabilities explaining the differences between companies. We propose a novel comprehensive AI Deployment Capability Framework as a theoretical basis and guide to building AI capabilities in companies. The results help companies holistically develop and deploy their capabilities, focus on critical areas, and address obstacles to utilizing data and AI to improve their productivity and competitiveness.
Aligning AI with Ethical and Strategic Management: A Multi-Case Exploration of Drucker’s Human-Centered Principles
(2026-01-06) Yi, Zhaoxia (Grace); Fu, Yubo
The strategic adoption of Artificial Intelligence (AI) within organizational management significantly enhances operational efficiency and innovation but simultaneously introduces complex ethical considerations. This study applies Peter Drucker's established human-centered management principles—ethical responsibility, decentralized decision-making, knowledge empowerment, customer-centric innovation, and continuous organizational learning—to analyze and compare the AI governance strategies of JPMorgan Chase, Amazon, and Waymo. Utilizing a robust qualitative multi-case methodology informed by Yin (2018) and Eisenhardt and Graebner (2007), the research highlights distinctive strategic implementations: Amazon's robust decentralization fostering agile, customer-focused innovation; JPMorgan Chase's stringent ethical oversight and regulatory compliance frameworks; and Waymo's comprehensive safety protocols combined with intensive employee empowerment initiatives. Findings extend Drucker’s theoretical insights to contemporary digital contexts, proposing practical frameworks that support ethically responsible, strategically innovative, and human-centered management of AI technologies in diverse organizational environments.
Introduction to the Minitrack on AI, Organizing, and Management
(2026-01-06) Seidel, Stefan; Nickerson, Jeff; Lindberg, Aron; Saltz, Jeffrey

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