Next-generation ITSM: Yan Qianyun reconstructs the enterprise intelligent service ecosystem

Modern enterprise service management has formed two core areas: technology-driven IT services and people-oriented employee services. The former encompasses the entire chain, from infrastructure operations and maintenance to the integration of emerging technologies. Examples include troubleshooting new system functionality, resolving system errors, or when a business department determines that the existing system cannot meet its business development needs. The latter encompasses employee lifecycle management. For example, HR services encompass the entire employee lifecycle, from onboarding, regularization, transfer, and resignation. These services include applying for work cards, issuing income certificates, checking social security payment standards, and handling cross-departmental transfers.

As enterprise digitalization continues to deepen, the number of IT service requests has also shown explosive growth. Most enterprise leaders have listed employee experience as a strategic priority, and enterprise service management has moved from back-end support to the strategic core.

1. Shared pain points of traditional enterprise service management

1. Knowledge management dilemma

In traditional enterprise service sharing systems, building and updating knowledge reserves is fraught with difficulties. Cold-start costs are extremely high. From the beginning of building a knowledge base to its initial usability, it often takes anywhere from 14 days to a month, involving extensive data collection, organization, and data entry. Subsequent knowledge operations are also extremely demanding, requiring continuous investment of manpower and time to update and optimize knowledge content.

For example, frequently asked questions (FAQ) sorting relies entirely on manual effort, requiring the organization of thousands of question and answer pairs. Furthermore, similar questions often require matching hundreds of similarly posed questions, significantly increasing both labor and time costs. Multi-round dialogue configuration requires individual setup for each business scenario, creating a significant workload and making it difficult to reuse these configurations in other scenarios, leading to significant resource waste.

2. Low Q&A coverage and low interception rate

Due to the high difficulty of knowledge maintenance, when users ask questions that go beyond the scope of the existing knowledge base, the system often cannot provide effective answers. At the same time, the system also lacks the ability to integrate different knowledge modules, making it difficult to form comprehensive and accurate answers.

In actual multi-round question-and-answer scenarios, irrelevant answers often occur. For example, when a user repeatedly asks related questions, the system cannot accurately understand the user's intent based on the context and cannot provide coherent and appropriate answers. This significantly reduces the user experience and reduces the efficiency of problem solving.

3. Slow service flow and low efficiency

From the initial service request to problem handling, resolution, and knowledge accumulation, the entire process is lengthy in traditional enterprise service sharing models. After an employee submits a service request, it must pass through multiple stages, involving customer service robots, human agents, and various specialized processing teams.

Delays or poor information transmission between each link can lead to slow service support and problem resolution. This not only affects employee productivity but also negatively impacts the company's overall operational efficiency, making it difficult to meet the needs of internal and external customers in a timely manner.

2. Intelligent Upgrade Solution for Enterprise Service Sharing

Yan Qianyun's digitally intelligent operations solution addresses the pain points and challenges of IT operations management for medium and large enterprises. Based on AIGC technology and the ITIL 4 framework, it creates a closed-loop IT service management system that integrates process automation, knowledge intelligence, and proactive services. This solution covers the entire service request lifecycle, from initiation and intelligent dispatch to efficient processing and knowledge accumulation. It seamlessly integrates notifications, chat, approvals, bots, organizational structures, and personal status, building a digitally intelligent "IT service hub" for enterprises and comprehensively improving the efficiency and quality of IT operations.

1. "Dual-link intelligent upgrade" achieves a paradigm breakthrough in the service system

Yan Qianyun's GenAI, based on large-scale models, focuses on intelligent upgrades for shared enterprise services. On the customer consultation side, the integration of natural language processing (NLP) and multimodal interaction technologies creates a zero-threshold service portal. The intelligent conversation engine not only achieves high intent recognition rates but also guides users to precisely express their needs through contextual understanding, significantly improving self-service resolution rates.

On the service support side, Yan Qianyun's work order processing system, based on the Large Language Model (LLM), automates the entire process from classification and dispatch to solution generation, providing enterprises with intelligent work order lifecycle management. Through automatic work order classification, labeling, and auxiliary analysis, support personnel can quickly understand requirements, automatically generate summaries and solutions, and reduce average processing time.

2. Build a sustainable and evolving service ecosystem for enterprises

Yan Qianyun is deeply integrated with various third-party systems and tools, breaking down information silos and enabling the free flow and collaboration of service resources inside and outside the enterprise. In addition, Yan Qianyun provides convenient service access interfaces for various departments within the enterprise, optimizes service processes through a closed-loop data system (collection → analysis → action → verification), analyzes models for repeated problems, identifies service request patterns for repeated problems, and evaluates and optimizes various services in the service ecosystem, ensuring that the entire service ecosystem remains vibrant and competitive, closely following the pace of business development and dynamic changes in the market environment, achieving continuous evolution and upgrading, and providing enterprises with long-term, stable, and high-quality service support.

3. Achieve the transformation from a cost center to a value engine

Yan Qianyun's digital enterprise service solution uses the triple engines of "management system standardization × intelligent decision-making × data assetization" to build a sustainable growth engine by reconstructing business processes, strengthening organizational governance, and activating service value. It promotes leapfrog growth in customer satisfaction, transforms enterprise service functions from basic support to a strategic empowerment center, injects long-term development momentum into the organization, and comprehensively reconstructs the competitive advantage of the enterprise service value chain.

Taking IT operation and maintenance as an example, Yan Qianyun can help enterprises significantly shorten the average fault repair time, significantly reduce the cost of work order processing, greatly improve operation and maintenance efficiency, effectively reduce operating costs, and achieve efficiency leap; significantly improve the success rate of changes, significantly shorten the compliance audit cycle, and effectively reduce operation and maintenance risks, ensuring that corporate operations are compliant and risks are controllable; improve user satisfaction, significantly reduce the time spent on internal collaboration, and significantly improve the user experience of internal employees and customers, promoting IT operation and maintenance from a simple cost center to a core competitiveness in the process of enterprise digital transformation, creating greater value for the enterprise.

Yan Qianyun, with its large-scale model as its foundation and Agent intelligence at its core, has built a next-generation ITSM system that breaks through traditional paradigms. This system helps enterprises overcome the deep-seated problems of fragmented knowledge management, delayed service responses, and inefficient resource collaboration found in traditional ITSM systems. It also propels enterprise services from passive response to value creation, achieving a leap from cost consumption to strategic empowerment. Zhenzhi Technology will continue to experiment with improving service efficiency, providing solid support for enterprises to steadily advance in the digital age.