PODCAST · science
1 More Paper
by Frontkom
Listen to the latest research on how organizations adopt AI, reshape work, and govern its use. Full papers, read verbatim (by AI).
Episodes
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A critical juncture: Integrating large language models in biostatistical workflows — Sahu (2026)
This Full Reading presents the paper's text as an audio edition. Authors: V. Sahu Publication date: 2026 Read the paper Source license: Creative Commons Attribution 4.0 International Narration: Read verbatim by an AI-generated voice. The authors and publisher do not sponsor or endorse this...
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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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ChatGPT and Beyond: Exploring the Responsible Use of Generative AI in the Workplace — Söllner et al. (2025)
This Full Reading presents the paper's text as an audio edition. Abstract source: unavailable Authors: Matthias Söllner, Thomas Arnold, Alexander Benlian, Ulrich Bretschneider, Caroline Knight, Ohly Sandra, Lena Rudkowski, Gerhard Schreiber, Domenik Wendt Publication date: 2025 Read the paper...
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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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Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering — Lu et al. (2024)
Responsible Artificial Intelligence (RAI) is widely considered as one of the greatest scientific challenges of our time and is key to increase the adoption of Artificial Intelligence (AI). Recently, a number of AI ethics principles frameworks have been published. However, without further guidance...
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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)
A full audio edition of this paper. Authors: Christopher Koch Published in: arXiv Publication date: 2026-05-19 Read the paper The authors and publisher do not sponsor or endorse this recording. Brief episode
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The European code of conduct for research integrity — ALLEA (2023)
This Full Reading presents the paper's text as an audio edition. Authors: ALLEA Literature type: Report Issuing organisation: ALLEA Peer review: Not stated in supplied metadata Publication date: 2023 Read the report Source license: Creative Commons Attribution 4.0 International Narration: Read...
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Living guidelines on the responsible use of generative AI in research — Commission (2026)
This Full Reading presents the paper's text as an audio edition. Authors: European Commission Literature type: Report Issuing organisation: European Commission Peer review: Not stated in supplied metadata Publication date: 2026-05 Read the report Source license: Creative Commons Attribution 4.0...
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Task-State Governance: A Virtual Machine for Verifiable and Recoverable GUI Agent Execution — Yang et al. (2026)
Graphical user interface (GUI) agents now execute workflows across real applications, but when execution drifts, users must determine what changed, what should have remained untouched, and what can still be recovered. Plan-centric interfaces leave users supervising procedure, but users live with...
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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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Living guidelines on the responsible use of generative AI in research — European Commission (2026)
This Full Reading presents the paper's text as an audio edition. Abstract source: unavailable Authors: European Commission, Directorate-General for Research and Innovation Literature type: Report Peer review: Not stated in supplied metadata Publication date: 2026-05 Read the report Source…
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Symbolic Separation: Grounding Deep Agents in Knowledge Graphs for Trustworthy Operational Data Analytics — Davletiyarov et al. (2026)
Generative AI promises natural language access to the massive numerical telemetry of data centers and Industry 4.0 installations, yet text-to-query and tool-using agents stay unreliable: even frontier models answer little more than half of real-world database questions, and far fewer of the...
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Intelligent Agents For Enterprise Knowledge Management-Construction And Application Based On Large Language Models — Tian et al. (2026)
A full audio edition of this paper. Authors: S. Tian, H. Li, Y. Chen Publication date: 2026 Read the paper Source license: Creative Commons Attribution 4.0 International The authors and publisher do not sponsor or endorse this recording.
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From AI Adoption to AI Transformation: The AX-5R Framework for Socio-Technical Work System Redesign — Shin et al. (2026)
Generative artificial intelligence (AI) has diffused rapidly, yet adoption has not reliably progressed to AI transformation (AX). Firms grant tool access but fail to redesign workflows, clarify accountability, govern risks, or measure value. The gap is a socio-technical systems problem, not a...
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A Policy-Aligned Agentic RAG Framework for Risk-Aware Decision Support in Enterprise Customer Relationship Management — Ganesan (2026)
Enterprise customer relationship management (CRM) systems function as decision-support environments where AI-generated recommendations can directly affect customer rights, refund eligibility, service level commitments, and privacy-sensitive decisions. Existing generative AI approaches for CRM,...
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Argo-Bench: Evaluating Data Agents on Enterprise-Scale Workflows — Tomitsuka et al. (2026)
Real-world enterprise data science and analytics workflows require reasoning across dozens of tables, performing statistical analyses, and acting on the results. Established text-to-SQL benchmarks evaluate query generation alone, and audits have found their answer keys frequently wrong. Because...
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Responsible artificial intelligence governance: A review and research framework — Papagiannidis et al. (2025)
The widespread and rapid diffusion of artificial intelligence (AI) into all types of organizational activities necessitates the ethical and responsible deployment of these technologies. Various na- tional and international policies, regulations, and guidelines aim to address this issue, and…
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Positioning public sector practitioners as ‘moral crumple zones’: Mechanisms in the early use of generative AI work support tools — Choroszewicz (2026)
Work support tools built on generative artificial intelligence (AI), like Microsoft 365 Copilot and other AI as- sistants, are increasingly the object of experimentation in public sector. Yet, empirical research on workers' experiences with these tools remains limited. This article draws on 2.5...
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Homo agenticus in the age of agentic AI: Agency loops, power displacement, and the circulation of responsibility — Leonardi (2025)
A defining characteristic of humanity is our relentless pursuit of control over our environment and our own destinies. We are, in essence, homo agenticus—humans defined by our search for agency. Philosophers have grappled with questions of voluntarism and determinism at least since the time of...
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From Backlog Items to Security Guidance: Towards Continuous Security Compliance — Núñez et al. (2026)
Continuous software engineering in regulated domains requires engineering teams to address security throughout the development lifecycle. Yet making security requirements explicit in backlog items is still problematic. Engineers must instead infer security relevance of backlog items from brief,...
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CredWise: A Controlled Agentic Decision-Intelligence Framework for Explainable and Auditable Credit-Risk Assessment — Tiwari (2026)
Credit-risk prediction is important in banking, but a prediction alone does not explain why an applicant is risky or how it should be combined with other evidence. This paper presents CredWise, a decision-support framework that integrates credit-risk prediction, probability calibration…
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Agentic information systems — Holldack et al. (2026)
Abstract Recent advancements in artificial intelligence (AI) have catalyzed the emergence of agentic information systems (IS), which exhibit autonomous behavior and advanced cognitive capabilities. Unlike traditional IS, which functioned primarily as reactive tools supporting humans, agentic IS…
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Agentic AI: Autonomous Intelligence for Complex Goals—A Comprehensive Survey — Acharya et al. (2025)
Agentic AI, an emerging paradigm in artificial intelligence, refers to autonomous systems designed to pursue complex goals with minimal human intervention. Unlike traditional AI, which depends on structured instructions and close oversight, Agentic AI demonstrates adaptability, advanced...
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Evaluation of Generative Artificial Intelligence Implementation Impacts in Social and Health Care Language Translation: Mixed Methods Case Study — Martikainen et al. (2025)
Abstract Background Generative artificial intelligence (GAI) is expected to enhance the productivity of the public social and health care sector while maintaining, at minimum, current standards of quality and user experience. However, empirical evidence on GAI impacts in practical, real-life...
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To hasten slowly: The prudence of slow AI implementation in public relations — Christensen & Andersson (2025)
This Full Reading presents the paper's text as an audio edition. Abstract source: unavailable Authors: E. Christensen, R. Andersson Publication date: 2025 Read the paper Source license: Creative Commons Attribution 4.0 International Narration: Read verbatim by an AI-generated voice. The authors…
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Causal mapping of the risks of using generative AI in software development — Hein et al. (2026)
This Full Reading presents the paper's text as an audio edition. Abstract source: unavailable Authors: David Kinnberg Hein, John Stouby Persson, Victor Vadmand Jensen, Anders Rysholt Bruun, Martin Gilje Jaatun Published in: Information and Software Technology Publication date: 2026-09 Read the...
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Knowledge-as-Skill: A Structural Design for Autonomous Knowledge-Base Use by LLM Agents — Wu (2026)
Retrieval-augmented generation (RAG) gives large language models (LLMs) access to external knowledge, but its conventional retrieve-concatenate-generate pipeline makes retrieval decisions on behalf of the model. As tool use and agent loops become more reliable, an agent can decide whether to...
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The Work Behind Delegation: A Framework for Supervising AI Coding Agents — Park et al. (2026)
As AI coding agents carry out development tasks with greater autonomy, developers are shifting from direct implementation toward supervising delegated work. Yet existing research offers limited understanding of how developers organize supervisory activities into connected workflows. Drawing on...
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FlowCheck: Helping End-Users Specify and Verify Intent in Vibe-Coded Web Apps — Vir et al. (2026)
Vibe-coded applications often contain silent behavioral failures in which the interface appears functional even though user-visible information does not flow to the expected state or output. We introduce FlowCheck, a constraint language to specify these user-visible information flows directly...
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Listen to the latest research on how organizations adopt AI, reshape work, and govern its use. Full papers, read verbatim (by AI).
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