account management

This study addresses the trade-off between cost and service quality in multichannel customer service by modeling the entire service process as a gated system. It jointly optimizes decisions across three levels: strategic (channel deployment), tactical (staffing and AI allocation), and operational (real-time scheduling). Leveraging operations research, dynamic modeling, and numerical simulation, the work derives a structured optimal request-handling policy and uncovers a counterintuitive insight: judicious deployment of AI chatbots not only enhances service efficiency but also significantly improves service quality, thereby achieving simultaneous optimization of cost and customer experience.

This study investigates the “dual burden” experienced by power-grid customer service agents when using AI assistants: while AI reduces operational burdens (e.g., typing, information retrieval, memory load), it simultaneously introduces new cognitive and regulatory demands—including learning overhead, compliance pressure, and psychological strain. Drawing on ethnographic observation and semi-structured interviews with 13 customer service representatives, complemented by qualitative thematic analysis, the research uncovers the implicit cognitive and affective efforts required to sustain effective human–AI collaboration. Its key contribution is a novel analytical framework—“technology-enabled burden shifting”—which challenges the conventional unidirectional efficiency paradigm by foregrounding how AI deployment redistributes, rather than eliminates, labor burdens within socio-technical systems. The findings provide empirically grounded insights for human-centered AI design and organizational support strategies in intelligent transformation initiatives.

This study addresses the inefficiencies in SaaS onboarding within regulated enterprises, where siloed security and compliance controls—spanning third-party risk management, cybersecurity, identity and access management, and disaster recovery—often result in process delays, redundant assessments, and ambiguous accountability. To overcome these challenges, this work proposes an end-to-end, control-driven SaaS onboarding framework that integrates multi-domain controls into a unified lifecycle model encompassing requirement intake, architectural validation, identity design, resilience assessment, and post-deployment governance. By leveraging cross-domain control mapping, phased process modeling, and governance checklists, the framework codifies key design patterns such as secure connectivity, federated identity, least-privilege access, and shared-responsibility disaster recovery. Empirical implementation demonstrates that the approach significantly reduces onboarding friction, enhances audit traceability, and strengthens both the security posture and operational resilience of SaaS platforms.

This work addresses the challenge in customer service automation where coupling routine requests with complex operations often leads to execution errors. The authors propose a difficulty-aware routing architecture that employs a lightweight classifier to direct simple interactions along an efficient baseline path, while routing high-risk, multi-entity, or backend-conflicting requests to an enhanced workflow. This enriched path triggers conflict-aware dialogues and a re-evaluation mechanism prior to critical write operations. By applying stringent controls only when necessary, the approach avoids global overhead while substantially improving system reliability. Evaluated on the τ²-bench suite across retail and airline domains, the architecture effectively ensures accurate record binding, supports fallback strategies, and orchestrates multi-step operations in a robust and orderly manner.

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.

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.

研究通过分析和分类客户信件来识别寿险取消原因，以提高保险公司对客户行为的理解，采用数据获取与准备等方法。

This study addresses the profound transformations in user roles, workflows, and collaboration patterns within enterprise software platforms driven by artificial intelligence, which existing role frameworks—such as the BTP user type matrix—struggle to accommodate. Through 20 expert interviews and a participatory design workshop involving 24 participants, the research employs qualitative methods to investigate structural shifts in developer roles on the SAP Business Technology Platform. Findings reveal three key trends: automation of operational tasks, expanded human-AI collaboration, and increased reliance on agent-based systems. In response, the study argues for a necessary reconfiguration of role taxonomies and governance mechanisms, offering both theoretical grounding and practical guidance for designing and governing AI-native enterprise software.

Electronic health record (EHR) audit logs contain rich, multidimensional information about clinical activities, yet lack a unified modeling framework. This work proposes a “multi-axis trace” perspective that simultaneously associates each logged action with clinician behavior, patient care trajectories, team collaboration patterns, and repetitive workflow structures, thereby uncovering its multifaceted clinical semantics. Building on this insight, we develop a representation learning framework that preserves the multi-axis structure by pretraining a foundation model directly on raw audit log streams to learn general-purpose representations. The resulting approach establishes a unified data representation and evaluation paradigm applicable to diverse downstream tasks, including clinical workload analysis, patient outcome prediction, team coordination assessment, and workflow modeling.

This study addresses the ambiguity in responsibility and agency between AI coding agents and human developers during pull request (PR) lifecycles, where proactive AI actions intersect with human-led merge governance. The authors propose an “Initiator × Approver” taxonomy and construct a collaboration–assistance spectrum alongside state-machine models of various tools. Through systematic log analysis of 29,585 PRs, they disentangle the distinct roles of AI and humans in the PR workflow. Their findings reveal that over 96% of PRs in collaborative tools are initiated by AI, yet merge decisions remain almost exclusively under human control. While automated merges record execution behavior, they do not engage with core governance functions. This work thus provides the first clear delineation of operational boundaries and governance demarcation for AI coding agents.