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Per André Rønsen
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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).

  1. 2d ago

    Development of an Agentic Retrieval-Augmented Generation System with Hybrid Retrieval for Budget Planning and Validation — Anggara et al. (2026)

    Abstract Budget planning and validation require users to examine cost references, compare proposed budget items with relevant guidelines and historical records, and provide traceable evidence for each decision. This process becomes challenging when the required information is scattered across formal cost standards, budget plans, project realization records, and internal narrative documents. This study develops a budget validation system based on Agentic Retrieval-Augmented Generation (RAG) with hybrid retrieval. Small and medium enterprises are used as the application domain, while the main contribution lies in combining structured retrieval and semantic retrieval to support evidence-grounded budget validation. Structured information, including cost guideline items, budget components, and historical realization data, is stored in a relational database. Meanwhile, narrative documents are converted into Markdown chunks and indexed in a vector database. Three workflows are implemented and compared: Single-Agent RAG, Routing-Based Agentic RAG, and Orchestrator-Workers Agentic RAG. The evaluation is conducted using 30 test cases and four RAG-based metrics: context precision, context recall, faithfulness, and answer relevancy. Latency is also measured as an additional indicator of response time. The results show that Routing-Based Agentic RAG achieves the highest context recall score of 0.873 and the highest faithfulness score of 0.878, while sharing the highest context precision score of 0.731 with Orchestrator-Workers Agentic RAG. Single-Agent RAG obtains the highest answer relevancy score of 0.775 and the lowest latency of 26.763 seconds. Based on these findings, Routing-Based Agentic RAG is selected as the most suitable workflow because it provides stronger retrieval quality and factual grounding, which are critical for evidence-based budget validation. This Full Reading presents the paper's text as an audio edition. Authors: Devi Willieam Anggara, Muhammad Reffy Haykal, Mohd Shafry Mohd Rahim, Suhono Harso Supangkat, Lenny Putri Yulianti, Agil Achmad Kurniawan Abstract source: DOI metadata Publication date: 2026-10-06 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 recording. Brief episode

  2. 5d ago

    Governing generative AI in organizations: a design theory and quasi-experimental field study of sociotechnical guardrails — Leon (2026)

    Abstract Generative AI adoption has outpaced organizational governance capabilities. We conceptualize AI guardrails as sociotechnical governance mechanisms, comprising policy, technical, and workflow components that embed organizational norms in deployed AI systems. Extending norm-based coordination accounts, we specify three mechanisms (norm encoding, output monitoring, and escalation) targeting four properties: predictability, fairness, safety, and auditability. We instantiate the theory in a three-layer artifact at a Fortune 500 firm and evaluate it through a stepped-wedge quasi-experiment covering 20 teams, 28 weeks, and 10,200 interactions. Guardrails reduced interaction entropy by 35%, narrowed the fairness gap from 0.18 to 0.05, halved hallucinations, and raised audit-trail completeness from 53% to 96%, at a 12% task-time cost and a temporary satisfaction decline. At least 35% of teams circumvented guardrails perceived as opaque or disproportionate, identifying perceived legitimacy as a boundary condition. Analysis of nine US executive orders (2019–2025) yields design implications for regulatory resilience. This Full Reading presents the paper's text as an audio edition. Authors: Maikel Leon Abstract source: Zotero metadata Published in: The Journal of Supercomputing Publication date: 2026-08-16 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 recording. Brief episode

  3. Oct 29

    Towards Designing AI-Enabled Adaptive Learning Systems — Kabudi (2023)

    This Full Reading presents the paper's text as an audio edition. Authors: Tumaini Kabudi Institution: University of Agder Publication date: 2023 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 recording. Brief episode

  4. Oct 29

    Human-Centered Artificial Intelligence: Design Principles for Public Services — Schmager (2025)

    This Full Reading presents the paper's text as an audio edition. Authors: Stefan Schmager Institution: University of Agder Publication date: 2025 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 recording. Brief episode

  5. Oct 29

    Applications of AI in Construction: From ambition to practice — Bang (2023)

    This Full Reading presents the paper's text as an audio edition. Authors: Sofie Bang Institution: Norwegian University of Science and Technology Publication date: 2023 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 recording. Brief episode

  6. Oct 28

    Enhancing transport innovation knowledge management workflows using artificial intelligence: a TRIMIS database case study — Cheimariotis et al. (2026)

    The study reports an LLM-assisted public-sector knowledge-management workflow using iterative prompt refinement, structured outputs and verbatim evidence grounding, compared with expert judgment across a large transport-project corpus. This Full Reading presents the paper's text as an audio edition. Authors: Ilias Cheimariotis, João Neves Costa, Francesca Piazza, Chiara Lodi, Michal Rataj, Jakub Zawieska, Marcin Stępniak, Monica Grosso Abstract source: Zotero metadata Published in: European Transport Research Review Publication date: 2026-10-26 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 recording. Brief episode

  7. Oct 26

    Generative Artificial Intelligence (GenAI) in the research process – A survey of researchers’ practices and perceptions — Andersen et al. (2025)

    This study explores the use of generative AI (GenAI) and research integrity assessments of use cases by researchers, including PhD students, at Danish universities. Conducted through a survey sent to all Danish researchers from January to February 2024, the study received 2534 responses and evaluated 32 GenAI use cases across five research phases: idea generation, research design, data collection, data analysis, and writing/ reporting. Respondents reported on their own and colleagues’ GenAI usage. They also assessed whether the practices in the use cases were considered good research practice. Through an explorative factor analysis, we identified three clusters of perception: "GenAI as a work horse", "GenAI as a language assistant only", and "GenAI as a research accelerator". The findings further show varied opinions on GenAI’s research integrity implications. Language editing and data analysis were generally viewed positively, whereas experiment design and peer review tasks faced more criticism. Controversial areas included image creation/modification and synthetic data, with comments highlighting the need for critical and reflexive use of GenAI. Usage differed by main research area, with technical and quantitative sciences reporting slightly higher usage and more positive assessments. Junior researchers used GenAI more than senior colleagues, while no significant gender differences were observed. The study underscores the need for adaptable, discipline-specific guidelines for GenAI use in research, developed collaboratively with experts to align with diverse research practices and minimize ethical and practical misalignment. This Full Reading presents the paper's text as an audio edition. Authors: Jens Peter Andersen, Lise Degn, Rachel Fishberg, Ebbe K. Graversen, Serge P.J.M. Horbach, Evanthia Kalpazidou Schmidt, Jesper W. Schneider, Mads P. Sørensen Abstract source: Zotero metadata Published in: Technology in Society Publication date: 06/2025 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 recording. Brief episode

  8. Oct 19

    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 procedural reliability in intelligent agent systems, addressing critical needs for SME-centric deployment in privacy-sensitive environments. Our approach features a 12-category error taxonomy capturing failure modes across tool initialization, parameter handling, execution, and result interpretation. Through systematic evaluation of 1,980 deterministic test instances spanning both open-weight models (Qwen2.5 series, Functionary) and proprietary alternatives (GPT-4, Claude 3.5/3.7) across diverse edge hardware configurations, we identify actionable reliability thresholds for production deployment. Our analysis reveals that procedural reliability—particularly tool initialization failures—constitutes the primary bottleneck for smaller models, while qwen2.5:32b achieves flawless performance matching GPT-4.1. The framework demonstrates that mid-sized models (qwen2.5:14b) offer practical accuracy-efficiency trade-offs on commodity hardware (96.6% success rate, 7.3s latency), enabling cost-effective intelligent agent deployment for resource-constrained organizations. This work establishes foundational infrastructure for systematic reliability evaluation of tool-augmented multi-agent AI systems. This Full Reading presents the paper's text as an audio edition. Abstract source: pdf-extraction Authors: D. Huang, G. Malwe, Z. Wang Publication date: 2026 Read the paper Narration: Read verbatim by an AI-generated voice. The authors and publisher do not sponsor or endorse this recording. Brief episode

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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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    Per André Rønsen
  • Years Active
    2026
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