Jul 2026· International Conference Computing Methodologies and Communication· pp. 1732-1738· 0 citations· 17 references
Abstract
Organizations today rely on multiple diversified digital platforms and tools for task planning, daily schedule aggregation, human resource support, communication, document handling, etc. These tools often operate independently, expecting employees to switch between multiple apps, leading to excessive cognitive load, reduced productivity, and unnecessary chaos. This paper introduces Collabrium, an AI-enabled enterprise collaboration platform that unifies HR assistance, daily agenda aggregation and management, In-chat reminder, Business Language translator, Contextual Tagging, Multi-Message Abstraction, and Document-based question answering features. These features make Collabrium a unique and unified platform for improving productivity and workflow management. The system incorporates semantic text understanding, retrieval-augmented generation, and vector-based similarity search to convert conversations into vectors and fetch contextually similar vectors within a vector database. The proposed approach improves functional effectiveness by incorporating artificial intelligence directly into communication workflows and tasks, for better workflow operation.
Abstract—Many everyday business and personal tasks share a hidden structure: some input arrives, an intelligent decision needs to be made about it, and an output has to be produced quickly and repeatedly, at a scale no single person can sustain by hand. This paper presents a single, general-purpose AI workflow automation platform built around this idea, using n8n – a visual workflow automation engine – as the layer that actually thinks, decides, and adapts, while a conventional web application supplies the interface and storage around it. Rather than hard-coding every rule and every prompt into the backend, the platform externalises all of its decision logic into n8n, so that behaviour can be revised the moment real usage reveals a gap, without ever redeploying the application. The platform is demonstrated through two representative use cases built on the identical architecture: automated, conversational practice for a task that benefits from repeated rehearsal, and automated, personalised outreach for a task that benefits from scale. Both use cases were piloted informally, and both showed the same underlying pattern – consistent turnaround within seconds, and a steady improvement in outcome quality as the workflow layer was iterated on. This paper describes the architecture, the shared design rationale, the pilot results, and the limitations that inform future work.
Sanketh B M, T. Vasudev, Karthik R· International Journal of Res...· 0 citations
Much of the knowledge that keeps an industrial operation running is never written down: it sits with individual employees, scattered across incompatible systems, and cannot be found in time—a risk that becomes acute whenever people change roles or retire. Artificial intelligence (AI) is widely proposed as a remedy, but existing approaches concentrate on documentary, white-collar work; embodied, blue-collar work is comparatively underserved, particularly by large language models with no native grounding in physical activity. Existing work also treats AI as a single undifferentiated capability, leaving practitioners without a principled basis for choosing among technologies or integrating their outputs. This paper proposes a taxonomy of five AI paradigms (perceptive, dialogic, interpretive, structural and contextual), each defined by its contribution to one of three knowledge processes (capture, structuring, transfer) and by the type of work it serves, with a boundary marked where collective tacit knowledge resists codification. A separate orchestration layer, realised by autonomous agents and enabling technologies (knowledge graphs, retrieval-augmented generation, augmented reality), connects the paradigms into a pipeline. Applied diagnostically to three tools in one manufacturer’s training programme, an assembly-guidance system, a structured interview system, and a RAG-based conversational assistant, the taxonomy shows all three occupy the capture or transfer columns while structuring goes unserved, leaving each tool’s knowledge inaccessible to the others. A pump-assembly scenario shows how an agent-orchestrated pipeline over a shared knowledge graph, with human validation, could unify their outputs. The tools’ reported gains, a 29 per cent onboarding-time reduction and an over 90 per cent retrieval-time reduction for 3,000+ daily users, are taken at face value; whether integration compounds them, and for whom, is a working hypothesis, not a demonstrated result. The paper concludes with a staged evaluation strategy measuring cross-tool retrieval coverage and validation throughput to isolate the structural layer’s impact.
Mohamed Amine Guedria, Maximilian Dommermuth· European Conference on Knowl...· 0 citations
The rapid advancement of LLMs has opened new opportunities in automated software engineering, driving progress in code understanding, agent-based workflows, and productivity tools. However, existing code intelligence systems have largely sidelined the end-users they aim to serve—the developers themselves. Developers exhibit substantial heterogeneity across multiple dimensions: coding style, toolchain preferences, domain-specific expertise, and problem-solving strategies. Failing to account for these individual differences directly compromises both the effectiveness of code intelligence and the likelihood of its adoption. For example, a senior architect and a junior engineer ask: "Describe the authorization module." Without personalized context, the system produces a uniform response—verbose for the expert, incomprehensible for the novice. This gap motivates a fundamental shift: from one-size-fits-all to one-size-fits-one code intelligence. A developer's dynamic in-IDE behaviors—code authoring patterns, navigation pathways, debugging trajectories—implicitly encode a rich representation of their competencies and habits. If captured and interpreted systematically, these signals can enable Personalized Code Intelligence, formalized as: [EQUATION] where P is the developer persona derived from IDE behaviors, injected alongside code context C and instruction ℐ.
Yuhong Liu, Yu Su, Zhipeng Peng et al.· SIGSOFT FSE Companion· 1 citation
This workshop aims to bring together researchers and practitioners to examine how enterprise AI agents can successfully move from prototypes to production, and focuses on three pillars: 1) Agent architectures and systems; 2) Enterprise applications and deployments; 3) Evaluation and governance.
Min Du, Anbang Xu, Jasmine Jaksic et al.· Proceedings of the 32nd ACM...· 0 citations
Generative artificial intelligence (GenAI) is moving from a novelty confined to chatbots and content drafting into something enterprises are beginning to fold into how they actually decide things: pricing, hiring, supply chain routing, capital allocation. This paper examines that shift through the lens of decision intelligence, the discipline concerned with engineering better organizational decisions by combining data, models, and human judgment. Using a PRISMA-informed narrative review of academic and industry literature published mainly between 2019 and 2026, the paper traces how large language models and related generative systems are being embedded into enterprise decision workflows, what measurable value they are producing, and where they fall short. The review finds genuine opportunities: compressed analysis cycles, wider access to sophisticated reasoning for non-specialist decision-makers, and new forms of scenario generation once reserved for expert analysts. At the same time, the literature converges on a stubborn set of challenges, including hallucinated or unreliable outputs, algorithmic bias, unclear governance accountability, and a persistent gap between pilot-stage enthusiasm and enterprise-level financial return. The paper argues that organizations capturing durable value are not necessarily those with the most advanced models, but those that have redesigned decision workflows, built human-in-the-loop verification into high-stakes processes, and treated GenAI as a collaborator rather than an oracle. It closes with practical implications and a short research agenda.
Naresh Sharma, Rohit Kumar, Himanshu Verma et al.· Journal of Intelligent Decis...· 0 citations
Experiments on public benchmarks and real-world industrial BI workloads show that QwenPaw-Data improves both verifiable data access capability and higher-level analytical quality, offering a practical foundation for reliable, traceable, and continuously improving enterprise data agents.
Tian Zeng, Yuntao Hong, Zhongjun Ding et al.· 1 citation