manage sales pipeline

This work addresses the limitations of traditional enterprise messaging systems, which rely heavily on external connectors and consequently suffer from fragmented data between messages and CRM records, achieving only eventual consistency through brittle synchronization tasks that hinder seamless automation and unified state management. To overcome these challenges, the authors propose and implement a platform-native messaging architecture that treats messages as first-class CRM entities. By leveraging platform events, asynchronous delivery, multi-tenant decoupling, and standard database object models, this approach deeply integrates the entire message lifecycle into CRM transactions, workflows, and reporting systems. The architecture has been successfully deployed at scale across multiple industries—including healthcare, sales, and field service—demonstrating significant improvements in system consistency, scalability, and automated integration capabilities.

Existing CRM evaluation benchmarks fail to capture real-world business complexity, hindering the integration and validation of AI agents in professional settings. Method: We introduce CRMArena—the first industrial-grade CRM workflow benchmark—featuring nine realistic tasks across three roles (service agent, analyst, manager), grounded in 16 highly interdependent object types and latent-variable modeling to encode intricate business logic and regulatory constraints. It uniquely integrates domain expert knowledge with latent-variable formalization and evaluates agents via dual paradigms: ReAct prompting and structured function calling, under high-fidelity object-relational modeling and dynamic data distribution simulation. Contribution/Results: CRMArena systematically exposes critical LLM agent limitations in rule adherence and structured function invocation. Experiments show state-of-the-art LLM agents achieve only 40% task completion under ReAct and 55% under function calling—highlighting the stringent demands of real-world CRM on robustness, compliance, and structured operational fidelity.

To address challenges in commercial management system development—including poor alignment between process models and execution platforms, low model reusability, and suboptimal development efficiency—this paper proposes a metamodel-based Model-Driven Development (MDD) approach. We design an evolvable and extensible business process metamodel framework and introduce a staged model transformation mechanism supporting QVT/ATL, enabling automated adaptation of extended BPMN models to diverse execution platforms. Crucially, we deeply integrate MDD into BPM system construction, establishing business models as the authoritative source governing system behavior. Experimental evaluation demonstrates significant improvements in development productivity and model consistency, robust cross-platform model reuse, and validates the metamodel’s effectiveness and flexibility in extended application scenarios such as resource management and customer relationship management.

Current software supply chain security risk management lacks systematic frameworks and quantitative assessment methodologies. Method: This paper proposes P-SSCRM v1—the first proactive, industry-informed risk management framework integrating nine real-world industrial practices and ten cross-domain standards. It employs framework engineering, comparative standard analysis, maturity modeling, and quantitative risk assessment to distill common elements and construct a unified, extensible risk management model with a standardized maturity mapping methodology. Contribution/Results: P-SSCRM v1 enables organizations to precisely identify capability gaps, conduct maturity benchmarking against established baselines, perform quantitative risk assessments, and implement incremental, systemic improvement pathways. By unifying heterogeneous practices and standards into a coherent, measurable framework, it significantly enhances the scientific rigor and operational feasibility of software supply chain security governance.

Manual identification of claim components in insurance claims processing creates a scalability bottleneck. Method: This study deploys large language models (LLMs) in a real production environment to automate knowledge-intensive tasks and introduces object-centric process mining (OCPM) for the first time to dynamically model AI-augmented process evolution. The approach integrates LLM-based reasoning, event log analysis, and OCPM modeling to quantitatively assess performance changes. Results: LLM deployment improves component identification efficiency by 3.2× and doubles daily throughput. OCPM uncovers three types of AI-induced latent rework paths, as well as novel process couplings and anomalous patterns. This work empirically validates OCPM’s capability to characterize AI-driven process evolution and establishes a reusable, evidence-based evaluation framework for human-AI collaboration optimization in knowledge-intensive domains.

研究通过整合CRM、MDM和CKM构建CRI框架，使用统计分析方法探讨了提升客户参与度的关键因素，发现CRM和CKM是主要驱动力。

This work addresses the challenge of effectively integrating process mining results into early-stage requirements engineering by proposing an automated modeling approach tailored to Use Case Maps (UCMs) within the ITU-T URN standard. By extending the PM4Py library, the authors develop the first process mining pipeline that treats UCMs as first-class outputs, supporting configurable actor mapping and nested hierarchical decomposition. The method enables high-fidelity bidirectional interoperability with the jUCMNav tool. Empirical evaluation on both public and synthetic event logs demonstrates its capability to accurately represent behavioral models across multiple abstraction levels, thereby advancing process mining as a practical enabler for model-driven requirements engineering.

This work addresses the limitation of existing text-to-process modeling approaches, which predominantly focus on control flow while neglecting resource and collaboration perspectives, thereby struggling to generate complete multi-party models. To overcome this, the authors propose a resource-aware generative pipeline that systematically incorporates the resource dimension into large language model (LLM)-driven process modeling for the first time. The method automatically constructs BPMN 2.0 collaboration diagrams from natural language descriptions, explicitly capturing organizational pools, role-based lanes, and inter-organizational message events, and employs an orthogonal layout algorithm for automated diagram arrangement. Experimental results across ten business processes and nine LLMs demonstrate that the approach accurately extracts resource-related information, maintains high control-flow quality, and incurs only minimal runtime overhead, advancing generative process modeling toward more collaborative and resource-complete representations.

This study addresses the vulnerability of language models to misleadingly optimistic assertions from CRM stakeholders during sales qualification, wherein incentive-misaligned statements are erroneously treated as objective evidence, thereby compromising decision-making. To investigate this, we propose a diagnostic framework that distinguishes persuasion effects from information deficits, employing bucket analysis, same-information controlled experiments, and computational step-length control for systematic evaluation. Our findings reveal that neither scaling nor enhanced reasoning mitigates such biases. Across seven mainstream models, false approval rates reach 87–97% under contradictory assertions, confirming that this failure mode is fundamentally rooted in persuasion rather than information insufficiency. This work offers a novel perspective on the reliability of large language models in high-stakes business applications.

This work addresses the limitations of existing CI/CD workflow analyses, which often focus narrowly on stage identification and struggle to assess reliability, maintainability, and optimization priorities. To overcome this, we propose a large language model–based CI/CD analysis pipeline that integrates repository context enhancement, anti-pattern detection, stage mining, and actionable recommendation generation. Our approach uniquely combines diagnostic reasoning, context awareness, and human-in-the-loop review to deliver observability tailored to cybersecurity engineering. Leveraging few-shot prompting, YAML parsing, and statistical tests (chi-square and Cramér’s V), the method identifies 434,769 anti-patterns across 75,201 workflows and generates an average of 8.25 syntactically valid optimization suggestions per repository, achieving a 96.1% compliance rate with YAML syntax standards.