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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).

  1. 5d ago

    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 multi-step, operational ones, because the LLM must compose how heterogeneous sources relate and hallucinates the relations, not just the fields. We propose symbolic separation: a deep agent reasons freely but may act on data only through an ontology-constrained Virtual Knowledge Graph with deterministic pre-execution validation. Unlike a tool API's interface contract, this domain-semantic contract turns a complex question into one validated graph traversal instead of LLM-inferred joins. Instantiated as the Neurosymbolic Deep Analyst and evaluated on 49.9 TB of superconputer telemetry against a rigid workflow and a non-symbolic ablation, it raises end-to-end task success from 43% to 86%, prevents silent data-integrity errors that no syntactic check catches, and cuts token cost by 2.4x, letting a smaller on-premise model outperform a larger one. This Brief reads the authors' abstract in the authors' own words. Abstract source: doi-metadata Authors: Baibek Davletiyarov, Junaid Ahmed Khan, Andrea Bartolini Published in: arXiv Publication date: 2026-09-15 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.

  2. 5d ago

    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 productivity problem: AI tools are inserted into existing routines without redesigning task interdependencies, decision rights, oversight loops, or performance feedback. This paper develops AX-5R, a socio-technical systems architecture that converts fragmented AI use into accountable, governable, and measurable work systems. Synthesizing seven literature streams, it maps failure modes to five interdependent design functions: readiness, redesign, role, risk, and return. AX-5R treats transformation as joint optimization of technical and social subsystems requiring configurational alignment. A supplementary ablation probe generated 252 artifacts across three workflows and seven prompt arms from two language models, scored by blinded cross-provider judges; the full frame outscored a sham five-part control and its four artifact-relevant ablations, significant under two-sided Holm-corrected testing, with an independent human-expert-rating check. With a failure-mode derivation matrix, implementation artifacts, and six testable propositions, the framework specifies a minimum architecture in which readiness sets boundaries, redesign restructures tasks, role assigns accountability, risk establishes control, and return supplies learning feedback. This Brief reads the authors' abstract and conclusion in the authors' own words. Abstract source: doi-metadata Authors: K.S. Shin, I.S. Kang, M. Lee 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 recording.

  3. 5d ago

    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, including standard retrieval-augmented generation (RAG), lack dedicated mechanisms for policy validation, risk-aware escalation, and decision auditability. This study proposes the Policy-Aligned Agentic Retrieval-Augmented Generation (PAL-CRM-RAG) framework for risk-aware intelligent decision support in enterprise CRM. The framework integrates ten processing layers encompassing CRM query understanding, risk-aware intent classification, hybrid BM25 and dense vector retrieval, reciprocal rank fusion, cross-encoder reranking, policy validation, agentic control, evidence-grounded generation, self-verification, and human escalation with audit logging. Using a synthetic CRM ticket corpus of 20,000 records across five issue categories and a constructed policy-knowledge corpus of 30 documents spanning six policy groups, PAL-CRM-RAG is evaluated against six baseline systems across retrieval quality, generation faithfulness, policy compliance, unsafe response rate, escalation accuracy, and response latency. Risk classification across three classes (Low, Medium, High) achieves an accuracy of 95.1% and a macro-F1 of 94.4%. PAL-CRM-RAG achieves a policy compliance rate of 100% and an escalation F1 of 1.000, compared with 0% compliance for the non-RAG baseline, and 90% compliance for the strongest retrieval-only baseline. An ablation study confirms that each architectural module contributes measurably, with removal of the policy validator reducing compliance to zero and removal of the risk classifier eliminating all escalation capability. These results demonstrate that policy alignment and risk-aware escalation can be operationalised within an Agentic RAG pipeline for enterprise CRM, advancing intelligent decision support theory and the practical governance of generative AI in customer service information systems. This Brief reads the authors' abstract and conclusion in the authors' own words. Abstract source: doi-metadata Authors: C. Ganesan 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 recording.

  4. 6d ago

    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 real enterprise warehouses are too sensitive to release, these benchmarks are built on public datasets where a business event fits in a single table. We introduce Argo-Bench, an evaluation framework comprising 210 data science and analytics tasks. Drawing on public data, peer-reviewed industry literature, and regulatory filings, we simulate a food delivery platform in New York City at true scale, with 81 million orders in 2024, grounded economics, fraud patterns, and marketplace incentives. We export this world to an ERP warehouse of 235 tables and 7.5 billion rows, modeled on the Oracle E-Business Suite schema. The simulator's ground-truth state is withheld from the warehouse the agent sees, so tasks require reconstructing facts by navigating the warehouse before acting on them. Argo-Bench goes beyond text-to-SQL: the agent files actions such as banning fraudulent accounts, allocating courier incentive budgets, or issuing back pay, and the grader scores each by its consequences in the simulator. Every task has an executable reference solution that demonstrates solvability using only the warehouse. The strongest of 14 frontier and open-weight models scores 95 or higher on only 34.8% of tasks and averages 59.5 points. We hope Argo-Bench drives progress toward agents that understand, navigate, and act within real data environments. This Brief reads the authors' abstract in the authors' own words. Abstract source: doi-metadata Authors: Gabriel Tomitsuka, Arman Raayatsanati, Emma Xing, Duke Gand, Joseph J Ma Published in: arXiv Publication date: 2026-10-01 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.

  5. 6d ago

    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 several organizations have developed frameworks detailing the principles of responsible AI. Nevertheless, the understanding of how such principles can be operationalized in designing, executing, moni- toring, and evaluating AI applications is limited. The literature is disparate and lacks cohesion, clarity, and, in some cases, depth. Subsequently, this scoping review aims to synthesize and critically reflect on the research on responsible AI. Based on this synthesis, we developed a conceptual framework for responsible AI governance (defined through structural, relational, and procedural practices), its antecedents, and its effects. The framework serves as the foundation for developing an agenda for future research and critically reflects on the notion of responsible AI governance. This Brief reads the authors' abstract and conclusion in the authors' own words. Abstract source: pdf-extraction Authors: Emmanouil Papagiannidis, Patrick Mikalef, Kieran Conboy Published in: The Journal of Strategic Information Systems Publication date: 2025-06 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.

  6. 6d ago

    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 years of ethnographic fieldwork on AI innovation in two Finnish organizations and three cases of such tools to critically explore the positioning of users as ‘moral crumple zones’ who bear the burden of ensuring the effective and ethical use of these emerging tools. The findings capture five mechanisms that produce such zones in the use of generative AI tools for administrative and frontline work: the assumption of human oversight and liability, the immaturity of tested tools marketed as ‘sparring buddies’, trial-and-error learning offloaded to users, limited transparency of AI tools' development and persuasive fallibility and the burden of vigilance placed on users. These challenges place users in roles that tend to shield the tools from broader scrutiny, meaning that users effectively absorb the burden of ethical and operational oversight and liability. This article contributes to understanding the often- thorny challenges around the uptake of generative AI tools in public sector and emphasizes the need for stronger accountability pathways—not only for users but also for other actors shaping these tools and their deployment. Authors: M. Choroszewicz 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.

  7. 6d ago

    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, free-form descriptions and often lack timely guidance on applicable requirements. We present an NLP-based backlog enrichment system that detects security-relevant backlog items and links them to relevant security requirements. The approach combines a security-relevance classifier with a retrieval-augmented generation (RAG) pipeline over security requirements documents. The approach was developed and evaluated in the context of a large enterprise in highly regulated domains. We present three contributions. First, we release a dataset of 288 backlog items labeled for security relevance by nine security practitioners, with substantial agreement (Fleiss' $κ=0.787$). Second, a recall-oriented classifier achieving $F2=0.774$ in-distribution and mean zero-shot G-measure $\approx 0.65$ across five established benchmarks, matching or outperforming most published classical-ML and open-source GPT baselines. Third, we preliminarily evaluated a four-stage security requirements document-grounded RAG pipeline with two practitioners on industrial backlogs using company-internal security policies and CIS Benchmarks. Of the retrieved 24 clauses, 12 were rated at least 4/5 for relevance. Our findings provide first indicators that NLP-based product backlog enrichment can support engineers in identifying security requirements early in the development process. With this work we aim to facilitate continuous security compliance through proactive introduction of security requirements in continuous software engineering. This Brief reads the authors' abstract in the authors' own words. Abstract source: doi-metadata Authors: Ignacio García Núñez, Florian Angermeir, Fabiola Moyón Constante Published in: arXiv Publication date: 2026-09-20 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.

  8. 6d ago

    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, explainable artificial intelligence, policy retrieval, SQL analytics, and controlled agent-based workflows. An XGBoost model is trained on Lending Club data (1,345,310 loans, 18 features) using a temporal split: 2007--2016 for training, 2017 for validation, and 2018 for testing. On the 2018 test set, the calibrated model achieved a ROC-AUC of 0.7109, PR-AUC of 0.2993, F1-score of 0.3714, and accuracy of 65.44\%. Calibration reduced the Brier score from 0.2157 to 0.1273 and the expected calibration error from 0.2862 to 0.0585. SHAP explanations were temporally stable, with a Spearman correlation of 0.9959 between 2017 and 2018 feature rankings. On 28 labeled queries covering nine policy sections, FAISS achieved the best Hit@1 (0.929) and MRR (0.964), while all three retrieval methods reached Hit@5 = 1.0. Agent routing achieved 95.6\% accuracy (43 of 45 cases), and the SQL benchmark scored 1.0 on exact-match, execution-success, and result-match across six cases. These results show that CredWise can combine predictions, explanations, policy evidence, and structured analytics in one controlled workflow. It is an academic research prototype, and final decisions remain with a human reviewer. This Brief reads the authors' abstract and conclusion in the authors' own words. Abstract source: doi-metadata Authors: Aakash Kumar Tiwari Published in: arXiv Publication date: 2026-09-29 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.

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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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