联系我们

论文查询结果	返回搜索

论文编号：	15992
作者编号：	2320224081
上传时间：	2026/6/2 12:07:13
中文题目：	M公司AIGC项目客户期望管理改进研究
英文题目：	Research on Customer Expectation Management Improvement in M Company''''''''s AIGC Projects
指导老师：	安利平
中文关键字：	AIGC项目；客户期望管理；服务质量差距；乙方视角；项目交付
英文关键字：	AIGC Project; Customer Expectation Management; Service Quality Gap; Service Provider Perspective; Project Delivery
中文摘要：	随着大语言模型技术快速商业化，AIGC企业级项目在国内市场迅速普及，但客户期望与实际交付之间的差距已成为突出的管理难题。消费级AI产品形成的高期望基准、AI输出的概率性与不可控性、验收标准难以量化及技术快速迭代带来的承诺风险，使AIGC项目的期望管理呈现出有别于传统IT项目的特殊挑战。 本研究以M公司（某IT服务提供商）三个AIGC项目为案例，立足乙方视角，运用服务质量差距模型（GAP模型）、期望不一致理论与服务接触理论，对M公司AIGC项目客户期望管理的现状与问题进行系统诊断，并提出改进策略与实施保障体系。 研究发现，M公司AIGC项目的客户期望差距呈现从售前到交付的传导结构：售前阶段既缺乏探测隐性期望的机制，模型实际能力边界也未经实测确认（GAP1）；双方对"智能程度"缺乏共同量化标准，导致预期无法对齐（GAP2）；进入实施后，技术条件持续偏离预设规范，AI概率性输出使交付质量不可控（GAP3）；技术路线变更时客户告知缺位，客户只能被动接受既成事实（GAP4）。四类差距沿项目生命周期逐级传导、叠加，最终共同推动了客户感知差距（GAP5）的形成。 针对上述问题，本研究提出了九项改进策略，售前阶段五项、实施阶段四项，分别对应隐性期望识别、能力边界场景验证、知识库质量预评估、承诺可行性审核、智能化程度共同评价基准的建立以及AI输出的动态监控等核心机制。配套保障措施则从组织人员、制度流程和标准化模板三个维度展开设计。 本研究为M公司构建了立足AIGC项目特性、可直接落地的客户期望管理改进体系，对同类服务提供商亦具参考价值。
英文摘要：	With the rapid commercialization of large language model technology, enterprise-level projects centered on AI-Generated Content (AIGC) have proliferated rapidly in the Chinese market. However, the gap between customer expectations and actual delivery has emerged as a prominent management challenge. High expectation baselines set by consumer-grade AI products, the probabilistic and uncontrollable nature of AI outputs, difficulties in quantifying acceptance criteria, and the risk of commitment fulfillment posed by rapid technological iteration all present unique challenges in customer expectation management that differ fundamentally from those encountered in traditional IT projects. This study focuses on M Company, a provider of IT implementation services, and adopts the perspective of the service vendor. Three AIGC projects delivered by M Company are selected as case samples. The Service Quality Gap Model (GAP Model) serves as the primary analytical tool, complemented by the Expectation Disconfirmation Theory and the Service Encounter Theory. Based on this framework, the study systematically diagnoses the current state and structural problems of customer expectation management in M Company's AIGC projects, and proposes targeted improvement strategies along with an implementation support system. The diagnostic findings reveal that customer expectation gaps in M Company's AIGC projects exhibit a cascading structure from pre-sales through delivery. In the pre-sales phase, the process lacks proactive mechanisms to detect clients' latent expectations, and the actual boundaries of AI model capabilities remain unverified through real-world testing (GAP1); meanwhile, the absence of a shared quantifiable standard for intelligent performance leaves expectations misaligned between both parties (GAP2). As projects move into execution, technical conditions persistently deviate from established service specifications, and the probabilistic nature of AI output renders delivery quality inherently uncontrollable (GAP3); when technology roadmap changes occur, the lack of a structured client notification mechanism forces clients to passively accept decisions as a fait accompli (GAP4). These four gap types cascade and compound along the project lifecycle, ultimately driving the formation of the customer perceived service gap (GAP5). In response to the identified problems, this study proposes nine improvement strategies spanning both the pre-sales and project implementation phases, five for the pre-sales phase and four for the implementation phase. These strategies focus on establishing a latent expectation identification mechanism, a scenario-based POC validation process for AI capability boundaries, a pre-sales knowledge base quality assessment procedure, a commitment feasibility review mechanism, a shared intelligent performance evaluation benchmark, and a continuous AI output quality monitoring mechanism. In terms of implementation support, complementary measures are proposed across three dimensions: organization and personnel, institutional processes, and standardized templates. This study constructs a customer expectation management improvement framework for M Company that is grounded in the distinctive characteristics of AIGC projects and directly actionable in practice. The findings also offer reference value for AIGC project service providers with similar business contexts.
查看全文：	预览  下载(下载需要进行登录)