PODCAST · science
1 More Paper (Brief)
by Per André Rønsen
Discover how organizations adopt AI, reshape work, and govern its use. Research abstracts and key conclusions, in the authors' own words, read verbatim (by AI).
Episodes
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When Agents Fail to Act: A Diagnostic Framework for Tool Invocation Reliability in Multi-Agent LLM Systems — Huang et al. (2026)
Multi-agent systems powered by large language models (LLMs) are transforming enterprise automation, yet systematic evaluation methodologies for assessing tool-use reliability remain underdeveloped. We introduce a comprehensive diagnostic framework that leverages big data analytics to evaluate...
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Getting in Contract with Large Language Models—An Agency Theory Perspective on Large Language Model Alignment — Kaltenpoth & Müller (2026)
Getting in Contract with Large Language Models – An Agency Theory Perspective on Large Language Model Alignment Research Paper Sascha Kaltenpoth1 and Oliver Müller1 Paderborn University, Department of Business Administration and Economics, Paderborn, Germany Abstract. Adopting Large language…
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Adopting Large Language Models to Automated System Integration — Pesl (2025)
Adopting Large Language Models to Automated System Integration Robin D. Pesl University of Stuttgart, Stuttgart, Germany the email address Abstract Modern enterprise computing systems integrate numerous subsystems to resolve a common task by yielding emergent behavior. A widespread approach is...
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The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges — Bandi et al. (2025)
Agentic AI systems are a recently emerged and important approach that goes beyond traditional AI, generative AI, and autonomous systems by focusing on autonomy, adaptability, and goal-driven reasoning. This study provides a clear review of agentic AI systems by bringing together their…
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Siyasat: AI-Powered AI Governance Tool to Generate and Improve AI Policies According to Saudi AI Ethics Principles — Alboaneen et al. (2025)
The rapid development of artificial intelligence (AI) and growing reliance on generative AI (GenAI) tools such as ChatGPT and Bing Chat have raised concerns about risks, including privacy violations, bias, and discrimination. AI governance is viewed as a solution, and in Saudi Arabia, the Saudi...
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The implementation of artificial intelligence in organizations by functional areas: A review and conceptual model — Aguado-García et al. (2026)
Abstract The rapid expansion of artificial intelligence has accelerated its adoption across organizational functions. However, existing reviews often adopt sectoral or technology-focused perspectives, limiting understanding of its implementation within core firm activities. This study addresses...
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Artificial Intelligence: Objective or Tool in the 21st-Century Higher Education Strategy and Leadership? — Bollaert (2025)
Since the launching of ChatGPT (generative) AI has been developed so much and fast that it has entered higher education (HE) and higher education institutions (HEIs). The article is meant to help HE(Is) how to deal with AI strategically and in leadership. It investigates which influences AI and…
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AI-Enabled Customised Workflows for Smarter Supply Chain Optimisation: A Feasibility Study — Javidroozi et al. (2025)
This study investigates the integration of Large Language Models (LLMs) into supply chain workflow automation, with a focus on their technical, operational, financial, and socio-technical implications. Building on Dynamic Capabilities Theory and Socio-Technical Systems Theory, the research…
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A Complex Leadership Perspective on Generative AI Adoption in SMEs: The Interplay of TAM, TMT, and RBV — Peñarroya-Farell et al. (2025)
Although Generative Artificial Intelligence (GenAI) is one of the strategic choices for digital transformation in small and medium-sized enterprises (SMEs), its adoption remains constrained by leadership decision-making that must balance strategic aspirations with resource limitations and...
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Artificial Intelligence Adoption in SMEs: Survey Based on TOE–DOI Framework, Primary Methodology and Challenges — Sánchez et al. (2025)
Despite the transformative potential of artificial intelligence (AI), small and medium-sized enterprises (SMEs) continue to face significant challenges in its effective adoption. While prior studies have emphasized strategic benefits and readiness models, there remains a lack of operational...
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Approach or Avoidance: How Does Employees’ Generative AI Awareness Shape Their Job Crafting Behavior? A Sensemaking Perspective — Yan et al. (2025)
Given the significant impact of Generative AI (GenAI) in the workplace, there are surprisingly few empirical studies examining how employees’ GenAI awareness shapes differently oriented job crafting behaviors. In organizations, understanding this is important because GenAI is unlikely to fully...
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A Paradox of Fit: How Job Complexity Shapes AI Self-Efficacy and AI Adoption Through a Curvilinear Mechanism — Akben & Dong (2025)
The rapid emergence of generative AI is transforming how employees engage with technology to perform tasks, make decisions, and create value. Despite its transformative potential, empirical findings on AI adoption remain inconsistent, particularly regarding how job characteristics shape…
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Examining News Innovation Narratives, Practices and Institutional Logics: A Study on GenAI Adoption in Egyptian Newsrooms — Arafat & Cooper (2025)
AI and algorithms play a crucial role in the processes of gathering, filtering and sharing news. But scholars have focused primarily on the use of such tools in big western media organizations, leaving outlets in developing countries under-researched. This paper aims to redress the balance by...
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AI Integration in the IT Professional Workplace: A Scoping Review and Interview Study with Implications for Education and Professional Competencies — Clear et al. (2025)
As Artificial Intelligence (AI) continues transforming workplaces globally, particularly within the Information Technology (IT) indus-try, understanding its impact on IT professionals and computing curricula is crucial. This research builds on joint work from two ∗Working Group Co-leader; All...
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Divisional perspectives on trust in generative AI: an empirical study of senior decision-makers — Khac et al. (2025)
ChatGPT opened a new chapter for using artificial intelligence (AI). Our results from 500 senior corporate decision-makers, focus on trust in introducing company internal generative AI applications. We find that decision-makers’ perspectives differ between organisational departments, particularly...
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Balancing Automation and Accuracy: A Comparative Analysis of AI Integration in News Production — Ebrahimi (2026)
This study examines generative artificial intelligence integration in news production through comparative institutional analysis of the BBC (public service broadcaster), Reuters (commercial news agency), and The Guardian (independent trust). Employing institutional theory as an analytical lens…
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Infusion of generative artificial intelligence: boosting organizational agility and effectiveness — Koh & Yuen (2026)
Organizational agility is vital when responding to turbulent market environments, and effectiveness to ensure long-term competitiveness in the market. Generative Artificial Intelligence tools have been increasingly incorporated and infused into organizational processes due to their promising...
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Exploring artificial intelligence adoption in public organizations: a comparative case study — Neumann et al. (2022)
Despite the enormous potential of artificial intelligence (AI), many public organizations struggle to adopt this technology. Simultaneously, empirical research on what determines successful AI adoption in public settings remains scarce. Using the technology organization environment (TOE)…
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One Size Fits Some: How Journalistic Roles Shape the Adoption of Generative AI — Møller et al. (2026)
The rise of generative artificial intelligence (AI) has sparked debate about its implications for journalism and the roles of journalists. Yet, the interplay between journalistic roles and AI adoption remains underexplored. Drawing on a survey of Danish journalists (N = 299), our study addresses...
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Factors Affecting the Adoption of Generative AI Tools Among Information Technology Employees: A UTAUT3, TTF, and SOR Perspective — Prasad et al. (2026)
This empirical study examined the components that affect the adoption and actual use of generative AI tools among information technology (IT) employees in Hyderabad. This study also attempted to unravel the SOR, UTAUT3, and TTF models by integrating them for practical application in industry and…
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The Proof Is in the Eating: Lessons Learnt from One Year of Generative AI Adoption in a Science-for-Policy Organisation — De Longueville et al. (2025)
This paper presents the key results of a large-scale empirical study on the adoption of Generative AI (GenAI) by the Joint Research Centre (JRC), the European Commission’s science-for-policy department. Since spring 2023, the JRC has developed and deployed GPT@JRC, a platform providing safe and...
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Vibe Coding in Product Teams: Reconfiguring AI-Assisted Workflows, Prototyping, and Collaboration — Li et al. (2026)
Generative AI is reshaping product design practices through “vibe coding”, where product team members express intent in natural language and AI translates it into functional prototypes and code. Despite rapid adoption, little research has examined how vibe cod- ing reconfigures product…
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Unlocking the potential of generative artificial intelligence: adoption drivers and organisational performance — Pokar (2025)
Generative artificial intelligence (GenAI) is rapidly transforming business practice, yet systematic evidence on organisational adoption remains scarce. This study applies a streamlined technology-organisation-environment (TOE) framework to investigate how firms adopt GenAI, focusing on perceived...
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The role of generative AI in improving the sustainability and efficiency of HR recruitment process — Koteczki et al. (2025)
Abstract Generative artificial intelligence (GAI) is becoming increasingly important in business processes, including human resource management. GAI can offer the potential to automate repetitive tasks in recruitment processes, optimise decision making, and reduce administrative burdens. Although...
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The evolution of organisational AI readiness toward an orchestration capability — Jonak & Wodecki (2026)
Abstract As Artificial Intelligence (AI) transitions from experimental pilots to enterprise-wide deployment, the central managerial challenge has shifted from simple adoption to the complexities of effective scaling and integration. While existing AI readiness models provide a foundational...
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The Present and Future of Accountability for AI Systems: A Bibliometric Analysis — Bartsch et al. (2025)
Abstract Artificial intelligence (AI) systems, particularly generative AI systems, present numerous opportunities for organizations and society. As AI systems become more powerful, ensuring their safe and ethical use necessitates accountability, requiring actors to explain and justify any...
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Separation of Concerns for Privacy-Preserving LLM Adoption: A Banking Architecture Framework — Kong et al. (2026)
Banking employees using third-party Large Language Models risk inadvertently exposing sensitive customer data through prompts. Existing cryptographic solutions impose prohibitive computational costs unsuitable for real-time applications. We present the Bank- ing Prompt Privacy Detector (BPPD), a...
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SCOPE: A System for Customized and Optimized Project Enhancement for IT Project Management — Samame et al. (2025)
Project management in the development of Information Technology (IT) solutions faces significant challenges due to the diversity of methodologies available and the complexity of selecting the most appropriate approach for each context. Widely used tools such as Jira, Asana, Trello, Click Up, or...
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Redefining Middle Management: How Generative AI Reshapes Roles and Competencies — Jean-Baptiste (2026)
This ongoing research investigates how Generative Artificial Intelligence (GAI) technologies are reshaping the roles and competencies of middle managers. While much of the existing literature emphasizes macro-level impacts of AI—such as productivity gains and strategic transformation—this study...
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Policy-As-Prompt: Rethinking Content Moderation in the Age of Large Language Models — Palla et al. (2025)
Policy-as-Prompt: Rethinking Content Moderation in the Age of Large Language Models KONSTANTINA PALLA, Spotify, UK JOSÉ LUIS REDONDO GARCÍA, Spotify, Spain CLAUDIA HAUFF, Spotify, Netherlands FRANCESCO FABBRI, Spotify, Spain HENRIK LINDSTRÖM, Spotify, Sweden DANIEL R. TABER, Spotify, US ANDREAS...
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LLM-Powered Knowledge Graphs for Enterprise Intelligence and Analytics — Kumar et al. (2025)
Disconnected data silos within enterprises obstruct the extraction of actionable insights, diminishing efficiency in areas such as product development, client engagement, meeting preparation, and analytics-driven decision-making. This paper in-troduces a framework that uses large language models...
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LLM support for enterprise modelling based on integrating prompt and enterprise modelling meta-models — Sandkuhl et al. (2026)
Abstract Large language models (LLMs) are considered by many researchers to be a promising technology for automating routine tasks in conceptual modeling, such as in enterprise modeling. Enterprise Modelling (EM) is a significant method-based endeavour that is often seen as a prerequisite to...
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Instructional designers and generative AI: a systematic and bibliometric review of adoption, role adaptation, and entrepreneurial integration — Ngate (2026)
Generative artificial intelligence (AI) is reshaping instructional design practice across higher education, adult learning, and digital pedagogy. Yet evidence on how instructional designers adopt AI tools, how their roles change, and how they integrate AI strategically remains fragmented across...
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Impacts of generative artificial intelligence on the future of labor market: A systematic review — Salari et al. (2025)
Background: Generative AI (GenAI) has the ability to autonomously collect and process data to generate contents, inform decisions, solve problems, and perform tasks that typically require human reasoning. This Systematic Review is conducted to examine the impacts of GenAI on the future of...
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Human-aligned AI for supply chain risk detection: validation and calibration of LLM-based early-warning assessments — Purk (2026)
Purpose This paper investigates how a large language model (LLM)-based early-warning system (EWS) for supply chain risk can be systematically aligned with human expert judgement. Building on prior work that introduced the overall system architecture, this study focuses on the detailed prompting...
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Genflow Ad Studio: A Compound AI Architecture for Brand-Aligned, Self-Correcting Video Generation — Das et al. (2026)
Recent advancements in generative video models demonstrate high visual fidelity, yet their integration into enterprise environments is restricted by temporal inconsistencies and severe brand misalign- ment. Current monolithic architectures struggle to enforce rigid brand constraints, frequently...
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Generative AI in project management: Impacts on corporate values, employee perceptions, and organizational practices — Aramali et al. (2025)
This study examines the evolving role of generative AI tools, particularly ChatGPT, in project management, focusing on their impact on corporate values, employee perceptions, and practical application across project phases and roles. Using a mixed methods design comprising a literature review…
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GAI-enabled CRM and sales unit performance: A user-level pathway through perceived quality and technology acceptance, moderated by adoption duration — Saadé & Sabri (2026)
Generative artificial intelligence (GAI) is being progressively integrated into customer relationship management (CRM) systems, yet empirical evidence linking GAI-enabled CRM to performance outcomes remains limited. This study examines the association between the implementation of GAI-enabled CRM...
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From Domain Documents to Requirements: Retrieval-Augmented Generation in the Space Industry — Arora et al. (2025)
Requirements engineering (RE) in the space industry is inherently complex, demanding high precision, alignment with rigorous standards, and adaptability to mission-specific constraints. Smaller space organisations and new entrants often struggle to derive actionable requirements from extensive,...
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From Automation to Transformation with AI-Tools: Exploring the Professional Norms and the Perceptions of Responsible AI in a News Organization — Cools & de Vreese (2026)
In the wake of the frenetic evolutions in (generative) AI, this study evaluates how these emerging technologies could be deployed and implemented in a responsible way at a national news com-pany in Denmark (Berlingske Media). By conducting expert inter-views with 14 news workers from various...
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Challenges and Opportunities for Generative AI in Software Engineering: A Managerial View — Rico & Öberg (2025)
Generative artificial intelligence (GenAI) has shown a strong poten- tial to automate tasks, increase productivity, and enhance software quality. Managers have an important role in adopting GenAI as they lead strategic planning and team coordination. This study examines the software engineering...
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Artificial Intelligence in Recruitment: A Multivocal Review of Benefits, Challenges, and Strategies — Trovão et al. (2025)
This study investigates the role of artificial intelligence (AI) in recruitment, with a specific emphasis on small and medium enterprises (SMEs) and cultural diversity, two dimensions frequently underrepresented in existing research. The objective is to evaluate the benefits, challenges, and...
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An organizational theory for multi-agent interactions integrating human agents, LLMs, and specialized AI — Borghoff et al. (2025)
Abstract Purpose Recent advances in AI, especially in large language models, have created new opportunities to integrate human and artificial agents through shared linguistic capabilities. This paper presents a multi-agent organizational framework in which human agents, LLMs, and specialized…
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AI-augmented Business Process Management Systems: A Research Manifesto — Dumas et al. (2023)
AI-augmented Business Process Management Systems (ABPMSs) are an emerging class of process-aware information systems, empowered by trustworthy AI technology. An ABPMS enhances the execution of business processes with the aim of making these processes more adaptable, proactive, explainable, and...
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A Cost-Benefit Analysis of On-Premise Large Language Model Deployment: Breaking Even with Commercial LLM Services — Pan et al. (2025)
Anthropic, and Google are attractive because they provide easy access to state-of-the-art models and are easy to scale. However, concerns about data privacy, the difficulty of switching service providers, and long-term operating costs have driven interest in local deployment of open-source…
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Evaluating LLMs as Agentic Workflow Executors in Real-World Customer Support Skills — Laskar et al. (2026)
Enterprise customer-support agents increasingly rely on Large Language Models (LLMs) to execute bounded, tool-using workflows. We study this problem through the lens of Skill LLMs: specialized executors that operate within a selected skill context and must complete a narrow, stateful workflow…
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ConSiDERS-The-Human Evaluation Framework: Rethinking Human Evaluation for Generative Large Language Models — Elangovan et al. (2024)
In this position paper, we argue that human evaluation of generative large language models (LLMs) should be a multidisciplinary undertak- ing that draws upon insights from disciplines such as user experience research and human behavioral psychology to ensure that the ex- perimental design and...
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Safe, Persistent, and Evolving Agent Harness for Understanding Partially Observable Worlds — Gao et al. (2026)
Large language model agents can invoke tools fluently, but enterprise workflows demand more than selecting the right tools: actions must strictly comply with organizational policies, tool feedback often conceals hidden side effects under partial observability, and long-horizon tasks require...
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Evaluating Local Language Model Agents for Reproducible Data Engineering: An Empirical Software Engineering Study of Mobility Workflows — García-Carrasco et al. (2026)
Context: Large language model (LLM) agents are increasingly used as software and data-engineering assistants, yet evidence about locally deployable open-weight agents remains limited. Existing evaluations often emphasize textual responses or isolated code generation rather than the validity of...
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Agentic Agile-V: From Vibe Coding to Verified Engineering in Software and Hardware Development — Koch (2026)
Agentic AI coding systems can inspect repositories, plan implementation steps, edit files, call tools, run tests, and submit pull requests. These capabilities make software and hardware development faster in some settings, but current evidence does not support the simple claim that autonomous…
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ABOUT THIS SHOW
Discover how organizations adopt AI, reshape work, and govern its use. Research abstracts and key conclusions, in the authors' own words, read verbatim (by AI).
HOSTED BY
Per André Rønsen
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Discover how organizations adopt AI, reshape work, and govern its use. Research abstracts and key conclusions, in the authors' own words, read verbatim (by AI).
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