1 More Paper (Brief)

<div data-description-manual="false"><p class="episode-abstract" data-source="doi-metadata">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.</p><p>This Brief reads the authors&#x27; abstract in the authors&#x27; own words.</p><p>Authors: Devi Willieam Anggara, Muhammad Reffy Haykal, Mohd Shafry Mohd Rahim, Suhono Harso Supangkat, Lenny Putri Yulianti, Agil Achmad Kurniawan</p><p>Abstract source: DOI metadata</p><p>Publication date: 2026-10-06</p><p><a href="https://doi.org/10.21203/rs.3.rs-10573181/v1">Read the paper</a></p><p>Source license: <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International</a></p><p>Narration: Read verbatim by an AI-generated voice.</p><p>The authors and publisher do not sponsor or endorse this recording.</p></div>

<div data-description-manual="false"><p class="episode-abstract" data-source="zotero">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.</p><p>This Brief reads the authors&#x27; abstract in the authors&#x27; own words.</p><p>Authors: Maikel Leon</p><p>Abstract source: Zotero metadata</p><p>Published in: The Journal of Supercomputing</p><p>Publication date: 2026-08-16</p><p><a href="https://doi.org/10.1007/s11227-026-08760-7">Read the paper</a></p><p>Source license: <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International</a></p><p>Narration: Read verbatim by an AI-generated voice.</p><p>The authors and publisher do not sponsor or endorse this recording.</p></div>

<div data-description-manual="false"><p>This Brief reads the authors&#x27; abstract and conclusion in the authors&#x27; own words.</p><p>Authors: Tumaini Kabudi</p><p>Institution: University of Agder</p><p>Publication date: 2023</p><p><a href="https://hdl.handle.net/11250/3062984">Read the paper</a></p><p>Source license: <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International</a></p><p>Narration: Read verbatim by an AI-generated voice.</p><p>The authors and publisher do not sponsor or endorse this recording.</p></div>

<div data-description-manual="false"><p>This Brief reads the authors&#x27; abstract and conclusion in the authors&#x27; own words.</p><p>Authors: Stefan Schmager</p><p>Institution: University of Agder</p><p>Publication date: 2025</p><p><a href="https://hdl.handle.net/11250/3188919">Read the paper</a></p><p>Source license: <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International</a></p><p>Narration: Read verbatim by an AI-generated voice.</p><p>The authors and publisher do not sponsor or endorse this recording.</p></div>

<div data-description-manual="false"><p>This Brief reads the authors&#x27; abstract and conclusion in the authors&#x27; own words.</p><p>Authors: Sofie Bang</p><p>Institution: Norwegian University of Science and Technology</p><p>Publication date: 2023</p><p><a href="https://hdl.handle.net/11250/3105044">Read the paper</a></p><p>Source license: <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International</a></p><p>Narration: Read verbatim by an AI-generated voice.</p><p>The authors and publisher do not sponsor or endorse this recording.</p></div>

<div data-description-manual="false"><p class="episode-abstract" data-source="zotero">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.</p><p>This Brief reads the authors&#x27; abstract in the authors&#x27; own words.</p><p>Authors: Ilias Cheimariotis, João Neves Costa, Francesca Piazza, Chiara Lodi, Michal Rataj, Jakub Zawieska, Marcin Stępniak, Monica Grosso</p><p>Abstract source: Zotero metadata</p><p>Published in: European Transport Research Review</p><p>Publication date: 2026-10-26</p><p><a href="https://doi.org/10.1186/s12544-026-00854-w">Read the paper</a></p><p>Source license: <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International</a></p><p>Narration: Read verbatim by an AI-generated voice.</p><p>The authors and publisher do not sponsor or endorse this recording.</p></div>

<div data-description-manual="false"><p class="episode-abstract" data-source="zotero">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.</p><p>This Brief reads the authors&#x27; abstract in the authors&#x27; own words.</p><p>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</p><p>Abstract source: Zotero metadata</p><p>Published in: Technology in Society</p><p>Publication date: 06/2025</p><p><a href="https://linkinghub.elsevier.com/retrieve/pii/S0160791X2500003X">Read the paper</a></p><p>Source license: <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International</a></p><p>Narration: Read verbatim by an AI-generated voice.</p><p>The authors and publisher do not sponsor or endorse this recording.</p></div>

<div data-description-manual="false"><p class="episode-abstract" data-source="pdf-extraction">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.</p><p>This Brief reads the authors&#x27; abstract and conclusion in the authors&#x27; own words.</p><p>Abstract source: pdf-extraction</p><p>Authors: D. Huang, G. Malwe, Z. Wang</p><p>Publication date: 2026</p><p><a href="https://doi.org/10.1109/icaibd69640.2026.11637248">Read the paper</a></p><p>Narration: Read verbatim by an AI-generated voice.</p><p>The authors and publisher do not sponsor or endorse this recording.</p></div>

<div data-description-manual="false"><p class="episode-abstract" data-source="pdf-extraction">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 models (LLMs) in organizations potentially revolutionizes our lives and work. However, they can generate off-topic, dis- criminating, or harmful content. This AI alignment problem often stems from misspecifications during the LLM adoption, unnoticed by the principal due to the LLM’s black-box nature. While various research disciplines investigated AI align- ment, they neither address the information asymmetries between organizational adopters and black-box LLM agents nor consider organizational AI adoption pro- cesses. Therefore, we proposeLLM ATLAS(LLMAgencyTheory-LedAlignment Strategy) a conceptual framework grounded in agency (contract) theory, to mitigate alignment problems during organizational LLM adoption. We conduct a conceptual literature analysis using theorganizational LLM adoption phasesand the agency theoryas concepts. Our approach results in providing an extended literature analysis process specific to AI alignment methods during organizational LLM adoption and providing a first LLM alignment problem-solution space. Keywords:Large Language Models, Organizational LLM Adoption, LLM Align- ment, Agency Theory</p><p>This Brief reads the authors&#x27; abstract in the authors&#x27; own words.</p><p>Abstract source: pdf-extraction</p><p>Authors: S. Kaltenpoth, O. Müller</p><p>Publication date: 2026</p><p><a href="https://doi.org/10.1007/978-3-032-08480-4_4">Read the paper</a></p><p>Narration: Read verbatim by an AI-generated voice.</p><p>The authors and publisher do not sponsor or endorse this recording.</p></div>

<div data-description-manual="false"><p class="episode-abstract" data-source="pdf-extraction">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 using services implemented with Web technologies like REST or OpenAPI, which offer an interaction mechanism and service documentation standard, respectively. Each service represents a specific business functionality, allowing encapsulation and easier maintenance. Despite the reduced maintenance costs on an individual service level, increased integration complexity arises. Consequently, automated service composition approaches have arisen to mitigate this issue. Nevertheless, these approaches have not achieved high acceptance in practice due to their reliance on complex formal modeling. Within this Ph.D. thesis, we analyze the application of Large Language Models (LLMs) to auto- matically integrate the services based on a natural language input. The result is a reusable service composition, e.g., as program code. While not always generating entirely correct results, the result can still be helpful by providing integration engineers with a close approximation of a suitable solution, which requires little effort to become operational. Our research involves (i) introducing a software architecture for automated service composition using LLMs, (ii) analyzing Retrieval Augmented Generation (RAG) for service discovery, (iii) proposing a novel natural language query-based benchmark for service discovery, and (iv) extending the benchmark to complete service composition scenarios. We have presented our software architecture as Compositio Prompto, the analysis of RAG for service discovery, and submitted a proposal for the service discovery benchmark. Open topics are primarily the extension of the service discov- ery benchmark to service composition scenarios and the improvements of the service composition generation, e.g., using fine-tuning or LLM agents.</p><p>This Brief reads the authors&#x27; abstract in the authors&#x27; own words.</p><p>Abstract source: pdf-extraction</p><p>Authors: R.D. Pesl</p><p>Publication date: 2025</p><p><a href="https://doi.org/10.1007/978-3-031-94590-8_37">Read the paper</a></p><p>Narration: Read verbatim by an AI-generated voice.</p><p>The authors and publisher do not sponsor or endorse this recording.</p></div>

<div data-description-manual="false"><p class="episode-abstract" data-source="doi-metadata">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 definitions, frameworks, and architectures, and by comparing them with related areas like generative AI, autonomic computing, and multi-agent systems. To do this, we reviewed 143 primary studies on current LLM-based and non-LLM-driven agentic systems and examined how they support planning, memory, reflection, and goal pursuit. Furthermore, we classified architectural models, input–output mechanisms, and applications based on their task domains where agentic AI is applied, supported using tabular summaries that highlight real-world case studies. Evaluation metrics were classified as qualitative and quantitative measures, along with available testing methods of agentic AI systems to check the system’s performance and reliability. This study also highlights the main challenges and limitations of agentic AI, covering technical, architectural, coordination, ethical, and security issues. We organized the conceptual foundations, available tools, architectures, and evaluation metrics in this research, which defines a structured foundation for understanding and advancing agentic AI. These findings aim to help researchers and developers build better, clearer, and more adaptable systems that support responsible deployment in different domains.</p><p>This Brief reads the authors&#x27; abstract and conclusion in the authors&#x27; own words.</p><p>Abstract source: doi-metadata</p><p>Authors: Ajay Bandi, Bhavani Kongari, Roshini Naguru, Sahitya Pasnoor, Sri Vidya Vilipala</p><p>Published in: Future Internet</p><p>Publication date: 2025-09-04</p><p><a href="https://doi.org/10.3390/fi17090404">Read the paper</a></p><p>Narration: Read verbatim by an AI-generated voice.</p><p>The authors and publisher do not sponsor or endorse this recording.</p></div>

<div data-description-manual="false"><p class="episode-abstract" data-source="doi-metadata">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 Data and Artificial Intelligence Authority (SDAIA) has introduced the AI Ethics Principles. However, many organizations face challenges in aligning their AI policies with these principles. This paper presents Siyasat, an Arabic web-based governance tool designed to generate and enhance AI policies based on SDAIA’s AI Ethics Principles. Powered by GPT-4-turbo and a Retrieval-Augmented Generation (RAG) approach, the tool uses a dataset of ten AI policies and SDAIA’s official ethics document. The results show that Siyasat achieved a BERTScore of 0.890 and Self-BLEU of 0.871 in generating AI policies, while in improving AI policies, it scored 0.870 and 0.980, showing strong consistency and quality. The paper contributes a practical solution to support public, private, and non-profit sectors in complying with Saudi Arabia’s AI Ethics Principles.</p><p>This Brief reads the authors&#x27; abstract and conclusion in the authors&#x27; own words.</p><p>Abstract source: doi-metadata</p><p>Authors: D. Alboaneen, S. Alhajri, K. Alhajri, M. Aljalal, N. Alalyani, H. Alsaadan, Z. Al Thonayan, R. Alyafer</p><p>Publication date: 2025</p><p><a href="https://doi.org/10.3390/computers14110452">Read the paper</a></p><p>Narration: Read verbatim by an AI-generated voice.</p><p>The authors and publisher do not sponsor or endorse this recording.</p></div>

<div data-description-manual="false"><p class="episode-abstract" data-source="doi-metadata">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 this gap through a systematic review of articles published in Web of Science and Scopus up to December 2025, following established methodological guidelines. A total of 160 peer-reviewed articles met the inclusion criteria. Findings reveal convergent patterns of adoption in human resources, marketing and customer services, logistics, and finance. Artificial intelligence enhances analytics, automates routine tasks, personalizes interactions, and supports decision-making. Human resources applications focus on recruitment and workforce planning; marketing relies on predictive analytics and conversational interfaces; logistics improves forecasting and supply chain resilience; finance strengthens risk assessment and process efficiency. The study proposes an integrative conceptual model and research propositions, highlighting cross-functional challenges in governance, organizational capabilities, socio-technical alignment, and responsible implementation.</p><p>This Brief reads the authors&#x27; abstract in the authors&#x27; own words.</p><p>Abstract source: doi-metadata</p><p>Authors: Juan-Manuel Aguado-García, Sara Alonso-Muñoz, Carmen De-Pablos-Heredero</p><p>Published in: Journal of Management &amp;amp; Organization</p><p>Publication date: 2026-07</p><p><a href="https://doi.org/10.1017/jmo.2026.10091">Read the paper</a></p><p>Narration: Read verbatim by an AI-generated voice.</p><p>The authors and publisher do not sponsor or endorse this recording.</p></div>

<div data-description-manual="false"><p class="episode-abstract" data-source="doi-metadata">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 the use of AI tools is having on HE(Is). Therefore 4 research questions are formulated: how does AI and AI tools influence HE(Is) in its mission, organization and context; should AI and its applications then be regarded as an strategic objective or only as a tool to realize the strategy; how is AI and the use of AI tools, as developed and described in an AI strategy, best managed to be adopted and integrated in an effective and responsible way, and finally which influence does AI and its tools have on the leadership and culture? In order to answer those questions, the article describes first our contemporary times, and the leadership needed, then delves into the history of the development of AI and its tools and investigates the current and future attitudes towards, degrees of implementation, and uses of AI and its tools among the internal and external stakeholders of HE(Is). The findings result from a global literature study of international surveys and 2 case studies. The selection is based on topical usefulness, international scope, (statistical) relevance and quality of research in general. In this way the article aims to help to develop an AI strategy and thus can be read as a policy paper underpinned by a meta-analysis. The main results are that, although the use of AI in HEIs is divided, the effective and responsible adoption and integration of AI is a new strategic objective in order to help to realize HE’s three-fold mission in a well-planned and managed way asking for a visionary leadership and a clear policy framework and guidelines, in which the words transparency, responsibility and critical thinking link AI use with an enhancement of unique human competences such as critical thinking.</p><p>This Brief reads the authors&#x27; abstract in the authors&#x27; own words.</p><p>Abstract source: doi-metadata</p><p>Authors: L. Bollaert</p><p>Publication date: 2025</p><p><a href="https://doi.org/10.3390/educsci15060774">Read the paper</a></p><p>Narration: Read verbatim by an AI-generated voice.</p><p>The authors and publisher do not sponsor or endorse this recording.</p></div>

<div data-description-manual="false"><p class="episode-abstract" data-source="doi-metadata">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 explores how LLMs can enhance logistics operations, increase workflow efficiency, and support strategic agility within supply chain systems. Using two developed prototypes, the Q inventory management assistant and the nodeStream© workflow editor, the paper demonstrates the practical potential of GenAI-driven automation in streamlining complex supply chain activities. A detailed analysis of system architecture and data governance highlights critical implementation considerations, including model reliability, data preparation, and infrastructure integration. The financial feasibility of LLM-based solutions is assessed through cost analyses related to training, deployment, and maintenance. Furthermore, the study evaluates the human and organisational impacts of AI integration, identifying key challenges around workforce adaptation and responsible AI use. The paper culminates in a practical roadmap for deploying LLM technologies in logistics settings and offers strategic recommendations for future research and industry adoption.</p><p>This Brief reads the authors&#x27; abstract and conclusion in the authors&#x27; own words.</p><p>Abstract source: doi-metadata</p><p>Authors: V. Javidroozi, A.-R. Tawil, R.M.A. Azad, B. Bishop, N.S. Elmitwally</p><p>Publication date: 2025</p><p><a href="https://doi.org/10.3390/app15179402">Read the paper</a></p><p>Narration: Read verbatim by an AI-generated voice.</p><p>The authors and publisher do not sponsor or endorse this recording.</p></div>

<div data-description-manual="false"><p class="episode-abstract" data-source="doi-metadata">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 organizational inertia. Organizational leadership must face the dynamic and complex characteristics of digital transformation in the knowledge era. Drawing on Complexity Theory and integrating the Technology Acceptance Model (TAM), Temporal Motivation Theory (TMT), and the Resource-Based View (RBV), this study proposes a conceptual framework reflecting distinct strategic leadership orientations. Following a qualitative approach based on semi-structured interviews with SME leaders and an Interpretative Phenomenological Analysis (IPA) this conceptual framework contributes by reframing GenAI adoption as a complex, nonlinear process rather than a straightforward diffusion model, that includes four strategic profiles (Strategic Adopters, Aspiring Adopters, Opportunistic Adopters, and Operational Stabilizers) that affect a dynamic relationship between three key adoption dimensions: intention, motivation, and resource allocation. SME leaders can benefit from a delimitation of their strategic and operational goals while overcoming adoption barriers.</p><p>This Brief reads the authors&#x27; abstract and conclusion in the authors&#x27; own words.</p><p>Abstract source: doi-metadata</p><p>Authors: M. Peñarroya-Farell, M. Vaziri, S.K. Soto Rivera, F. Miralles</p><p>Publication date: 2025</p><p><a href="https://doi.org/10.3390/admsci15120494">Read the paper</a></p><p>Narration: Read verbatim by an AI-generated voice.</p><p>The authors and publisher do not sponsor or endorse this recording.</p></div>

<div data-description-manual="false"><p class="episode-abstract" data-source="doi-metadata">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 guidance tailored to SME realities—particularly regarding implementation barriers, resource constraints, and emerging demands for responsible AI use. This study presents an analysis of AI adoption in SMEs by integrating the technology–organization–environment (TOE) framework with selected attributes from the diffusion of innovations (DOI) theory to examine adoption dynamics through a dual structural and perceptual lens. Empirical insights from sectoral and regional contexts are also incorporated. Ten critical challenges are identified and analyzed across the TOE dimensions, ranging from data access and skill shortages to cultural resistance, infrastructure limitations, and weak governance practices. Notably, the framework is expanded to incorporate responsible AI governance and democratized access to generative AI—particularly open-weight large language models (LLMs) such as LLaMA, DeepSeek-R1, Mistral, and FALCON—as emerging technological and ethical imperatives. Each challenge is paired with actionable, context-sensitive solutions. The paper is a structured, literature-based conceptual analysis enriched by empirical case study insights. As a key contribution, it introduces a structured, six-phase roadmap methodology to guide SMEs through AI adoption—offering step-by-step recommendations aligned with technological, organizational, and strategic readiness. While this roadmap is conceptual and has yet to be validated through field data, it sets a foundation for future diagnostic tools and practical assessments. The resulting study bridges theoretical insight and implementation strategy—empowering inclusive, responsible, and scalable AI transformation in SMEs. By offering both analytical clarity and practical relevance, this study contributes to a more grounded understanding of AI integration and calls for policies, ecosystems, and leadership models that support SMEs in adopting AI not merely as a tool, but as a strategic enabler of sustainable and inclusive innovation.</p><p>This Brief reads the authors&#x27; abstract and conclusion in the authors&#x27; own words.</p><p>Abstract source: doi-metadata</p><p>Authors: E. Sánchez, R. Calderón, F. Herrera</p><p>Publication date: 2025</p><p><a href="https://doi.org/10.3390/app15126465">Read the paper</a></p><p>Narration: Read verbatim by an AI-generated voice.</p><p>The authors and publisher do not sponsor or endorse this recording.</p></div>

<div data-description-manual="false"><p class="episode-abstract" data-source="doi-metadata">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 replace employees; instead, it requires them to adopt adaptive strategies to work alongside GenAI. If employees engage in avoidance crafting behavior, it could have negative consequences for the organization. Based on sensemaking theory, we develop a theoretical model to explore how employees’ GenAI awareness affects their job crafting behavior, as well as the mediating mechanisms and boundary conditions of its influence. Using self-evaluation data from 316 employees at three time points, the results of our hypothesis testing reveal that when employees perceive high internal Corporate Social Responsibility (CSR), their GenAI awareness triggers harmonious work passion and leads to approach job crafting; conversely, when employees perceive high external Corporate Social Responsibility, their GenAI awareness triggers obsessive work passion and leads to avoidance job crafting. Finally, the theoretical and empirical implications of our findings are discussed.</p><p>This Brief reads the authors&#x27; abstract and conclusion in the authors&#x27; own words.</p><p>Abstract source: doi-metadata</p><p>Authors: Y. Yan, X. Qu, H. Lei, Y. Geng</p><p>Publication date: 2025</p><p><a href="https://doi.org/10.3390/bs15060789">Read the paper</a></p><p>Narration: Read verbatim by an AI-generated voice.</p><p>The authors and publisher do not sponsor or endorse this recording.</p></div>

<div data-description-manual="false"><p class="episode-abstract" data-source="doi-metadata">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 employees’ confidence and readiness to use generative AI. Grounded in the Task–Technology Fit framework and self-efficacy theory, this research examines the curvilinear relationship between job complexity and AI self-efficacy and its subsequent effects on AI adoption readiness and behavior. We conducted two survey studies to test the proposed hypotheses using structural equation modeling. Results reveal that employees in both low- and high-complexity roles exhibit a low level of AI self-efficacy and a subsequent lower level of AI adoption behaviors compared to those in moderately complex roles. These findings challenge the assumption that highly skilled roles typically lead AI integration and instead highlight the importance of aligning task structure with AI capabilities. This study advances theory by introducing a non-linear boundary condition to technology adoption and offers practical guidance for organizations to design jobs and training programs that cultivate confidence and foster sustainable human–AI collaboration.</p><p>This Brief reads the authors&#x27; abstract and conclusion in the authors&#x27; own words.</p><p>Abstract source: doi-metadata</p><p>Authors: M. Akben, S. Dong</p><p>Publication date: 2025</p><p><a href="https://doi.org/10.3390/bs15121659">Read the paper</a></p><p>Narration: Read verbatim by an AI-generated voice.</p><p>The authors and publisher do not sponsor or endorse this recording.</p></div>

<div data-description-manual="false"><p class="episode-abstract" data-source="pdf-extraction">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 examining the use of Generative AI tools in Egyptian newsrooms on three levels: narrative, practice and technological infrastructure, including wider technological and organizational factors that facilitate or hinder innovation adoption. In doing so, the paper employs diffusion of innovation and institutional logics as conceptual frameworks and adopts 26 in-depth interviews with journalists, chief editors, developers and media experts at four newsrooms in Egypt. Findings demonstrated how news innovation becomes a priority when it not only aligns with the newsroom’s external marketing strategy as an early adopter of smart tech, but also when it proves to be financially viable with low tech-related costs, and has the potential to increase profitability. Individual journalists are burdened with the responsibility of self-learning about news technology, including GenAI tools. This added workload makes them less inclined to significantly transform their daily work routines, leading to more resistance to adopting change or innovation.</p><p>This Brief reads the authors&#x27; abstract in the authors&#x27; own words.</p><p>Abstract source: pdf-extraction</p><p>Authors: R. Arafat, G. Cooper</p><p>Publication date: 2025</p><p><a href="https://doi.org/10.1080/17512786.2025.2579810">Read the paper</a></p><p>Narration: Read verbatim by an AI-generated voice.</p><p>The authors and publisher do not sponsor or endorse this recording.</p></div>