Five Trends in the Development of China’s Call Center AI Knowledge Bases in 2026
By Tian Zhigang， Founder of the Knowledge Management Center
In 2025, with the open-source popularization and cost reduction of domestic large language models (LLMs) such as DeepSeek and Tongyi Qianwen, customer service centers—at the forefront of enterprise AI applications—are undergoing profound transformation. As the “brain” and core of intelligence in customer service systems, the strategic status of knowledge bases is being redefined: they are no longer merely auxiliary tools for answering questions, but have become testbeds and critical infrastructure for enterprises’ intelligent transformation.
While a handful of enterprises achieved results in call center intelligence in 2025, most attempts yielded insignificant outcomes. As revealed by the grim reality in MIT’s Generative AI Divide: 2025 Enterprise AI Status Report: despite enterprises investing $30–40 billion in AI, 95% of organizations have not reaped any returns.
The root cause does not lie in AI technology itself, but in the foundation on which AI is built. Many organizations overlay complex AI technologies onto fragmented, inconsistent, and ungoverned knowledge bases, yet wonder why their AI initiatives fail to create value.
Garbage In, Garbage Out. The true intelligence of customer service relies on the construction and operation of high-quality knowledge bases. Many call centers have blindly integrated LLMs without a clear understanding of technological boundaries, assuming that simply deploying LLMs internally will naturally deliver magical results. However, the outcomes speak for themselves, leaving many projects in an awkward predicament.
Despite the rapid evolution of AI technology, the intelligent upgrading of customer service depends not only on technology, but more importantly on high-quality internal knowledge content systems within each organization.
Technology can be purchased, but a high-quality internal knowledge system cannot.
Furthermore, the construction and operation of a high-quality knowledge base involve not just technical aspects, but also a comprehensive set of multi-dimensional factors including management cognition, establishment of knowledge base standards, and improvement of professional capabilities. It requires systematic thinking and scenario-based validation to achieve.
Based on years of research and practical experience of the Knowledge Base Research and Consulting Team at the Knowledge Management Center, we present the following predictions for the trends of China’s call center knowledge bases in 2026 (for more detailed information, please contact us via WeChat: 511956894, or Email:club@kmcenter.org):
Trend 1: Elevated Strategic Importance of Knowledge Bases – From Support Tools to Critical Infrastructure
Most large organizations have already established knowledge bases within their customer service departments. Inside customer service centers, knowledge bases and their teams will become pivotal, garnering greater attention and resource support. In some companies, call center knowledge bases will even break through the boundaries of the customer service department and become a vital source for enterprise-level knowledge asset platforms. They will not only serve frontline agents, but also support cross-department collaboration in sales, product development, training, and other functions, forming a knowledge network characterized by “one-stop maintenance, enterprise-wide sharing”. They will also evolve into an important knowledge source for fine-tuning internal enterprise LLMs.
This transformation stems from LLMs’ insatiable demand for high-quality structured knowledge—only systematic, standardized knowledge can fully unleash the reasoning and generative capabilities of LLMs. Previously, however, most other business departments within companies lacked systematic knowledge bases.
As a result, knowledge base managers may gain greater voice within organizations, and members of knowledge management teams will likely become key contributors to enterprise-level knowledge middle-office departments.
For small and medium-sized enterprises (SMEs), 2026 will be the “year of awakening” for knowledge base construction. The traditional model of maintaining customer service operations solely through Excel spreadsheets or scattered documents has become fraught with difficulties, and the popularization of LLMs has made SMEs recognize the value of knowledge bases.
A large number of SMEs will begin to systematically consider building knowledge bases tailored to their products and services, aiming to support AI applications, reduce customer service costs, and improve customer satisfaction. However, most of these organizations lack the necessary cognition and methodologies for knowledge base construction, and will need to start from scratch.
It is expected that in 2026, most of these enterprises will opt for cloud services and domestic CRM systems to build their knowledge bases, in order to support the deployment of AI-powered intelligent customer service. This trend will further drive the expansion of the knowledge base software market size.
Trend 2: Knowledge Governance Becomes Critical – Establishing Standards for High-Quality Knowledge Bases
Although leaders hold high expectations for the integration of knowledge bases and LLMs, hoping to rapidly reduce costs and increase efficiency through AI, many companies began with overly optimistic expectations, only to face significant obstacles in actual implementation.
In 2026, the cognition of customer service center leaders and knowledge base teams will continue to improve. They will realize that the existing knowledge base content suffers from low levels of structuring and customization, and that its semantic richness falls far short of AI requirements. Fragmented content descriptions and inconsistencies in the same content across different documents are impeding the AI readiness of knowledge bases.
Such issues were tolerable in the era of human agents (who possessed background information and knowledge, as well as access to support channels), but when applied to LLMs, they will lead to significant deviations, inefficiencies, or even complete errors in model outputs.
Currently, while many enterprises have recognized that their existing knowledge base architectures and content are unsuitable for AI understanding and application, they remain unclear about the standards for high-quality knowledge base architectures and content. They are eager to improve but lack clarity on what to do and how to do it—there is a willingness to govern, but a lack of direction and guidelines.
Therefore, establishing standards for high-quality knowledge bases will become a key priority in 2026.
Standards for high-quality knowledge bases can be divided into multiple levels, encompassing both semantic and content expression requirements, such as structuring degree, customization level, scenario support, and content security. KMCenter offers relevant courses on methodologies for developing customized standards, along with case sharing.
Trend 3: Deepening AI Application Scenarios – From Retrieval Tools to Real-Time Intelligent Partners
In 2026, the integration of knowledge bases and AI will focus on two core scenarios:
External Customer Service
Knowledge bases will evolve from “passive query” systems into intelligent agents capable of understanding user intent through natural dialogue and proactively providing solutions. This requires knowledge bases to support the understanding of multi-turn dialogue contexts and dynamically organize knowledge presentation methods based on dialogue progress. Multimodal knowledge fusion will become critical—knowledge bases will not only contain text, but also integrate diverse content such as images, videos, and operation demonstrations to solve complex problems in a more intuitive manner.
Shifting from “retrieval-based FAQs” to “generative dialogue”. In 2026, intelligent customer service will no longer rely on keyword matching to return pre-set answers, but will implement multi-turn dialogue through Retrieval-Augmented Generation (RAG). For example, when a customer asks, “How much can I borrow?”, the AI will respond with a follow-up question: “What is your monthly income and credit history?” Through multi-turn dialogue to clarify intent, it will ultimately generate personalized solutions.
This places higher demands on knowledge bases, which need to evolve from traditional “question-answer pairs” to “task-oriented knowledge networks” that support conditional branching, computational logic, and compliance checkpoints.
Internal Empowerment
The value of knowledge lies in its utilization. AI will be deeply embedded into agent workflows to achieve “knowledge on demand”, truly transforming post-hoc search into real-time assistance. During customer service conversations, AI will transcribe voice to text in real time, the system will analyze customer semantics to predict intent, and automatically push relevant knowledge cards, script suggestions, and even early warning information—greatly reducing agents’ cognitive load and training costs. This kind of assistance not only improves response efficiency, but also ensures the accuracy and standardization of service responses.
To achieve this effect, knowledge bases must possess “temporal awareness”, enabling dynamic adjustment of push strategies based on call duration, customer sentiment scores, and historical complaint records.
Meanwhile, in terms of knowledge base operation, AI Agents will be leveraged for knowledge gap identification, automated updating of mature content, retrieval optimization, and content generation.
Trend 4: Externalization of Customer Knowledge – Shifting Knowledge Bases from Product-Oriented to User-Oriented
Traditional knowledge bases are “internal-facing”, standing from an official perspective with core content focused on “what products and services we offer, and what marketing activities we run”. However, this is not what customers ask about—they do not care about what enterprises provide, but only about solving their own problems, such as “What should I do in this situation?”. In many cases, customers may not even be able to clearly articulate their questions.
Previously, this reliance on agents to “translate” customer needs—extracting keywords from fragmented customer statements and searching the knowledge base—worked when agents were highly skilled. However, most agents are of average proficiency, leading to a common problem: knowledge base teams believe the answers to customer questions exist in the system, but agents cannot find them. The result is poor service quality and low customer satisfaction.
At its core, this stems from a lack of customer knowledge. In a future where AI directly serves customers, without agents acting as intermediaries, the outcome would be unimaginable. Future knowledge bases must not only store official content (products, services, marketing activities, etc.), but also knowledge about customer needs: how customers phrase their questions, what content they actually require, and in what scenarios they raise issues.
Traditional knowledge bases are mostly centered on “what the enterprise has”, focusing on explicit knowledge such as product functions and service policies. An important shift in 2026 will be the externalization and systematization of customers’ tacit knowledge. The “informal knowledge” accumulated by agents through interactions with a large number of customers—such as the scenarios where customers encounter difficulties, their commonly used expressions, unmet latent needs, and evaluations of competitors—will be systematically collected, analyzed, and integrated into knowledge base systems.
KMCenter offers relevant methodologies and practices for accumulating customer knowledge. Based on different scenarios, this can be achieved through various approaches: for example, using dialogue analysis engines to automatically mine high-frequency questions and emotional pain points from historical service records, supplemented by annotations from senior agents; establishing a “customer problem map” to connect scattered inquiries into a complete picture of customer experiences with a specific product or service; and transforming knowledge base content creation from “function descriptions” to “scenario-based solutions”. Only in this way can knowledge bases truly become carriers of “customer understanding”, driving service from passive response to proactive care, and fundamentally supporting AI in directly serving end customers.
Trend 5: Shortage of Professional Talent – Cultivating In-House Knowledge Base Experts for the AI Era
The AI era places higher capability requirements on knowledge base professionals. Those who previously specialized solely in content compilation, training, and quality inspection need to develop new skills adapted to artificial intelligence. There is no need for everyone to master algorithm programming, but knowledge base professionals must deeply understand customer service business logic and knowledge management processes, while also having a clear grasp of LLMs’ technical boundaries—knowing in which scenarios AI is reliable and when human intervention is required.
KMCenter’s training courses for call center knowledge base talent have already incorporated these required capabilities into their curriculum.
For example, top-tier professionals, in addition to hands-on operational skills, must possess business abstraction capabilities: they should be able to design knowledge architectures and tagging systems that are easily understandable by AI, and evaluate the business risks and accuracy of AI output results.
They should be capable of transforming agents’ experiential intuition into explicit knowledge structured around customer problems (phenomenon-cause-problem-countermeasure), decomposing narrative descriptions of customer complaints into structured expressions, and further developing problem ontologies and extending them into relevant knowledge graphs.
Knowledge base professionals need to understand AI: they should be able to identify which knowledge is suitable for AI generation (such as general policy interpretations) and which must be manually written (such as high-risk compliance clauses). They also need to be aware of technical constraints such as LLM context window limits and RAG recall rate bottlenecks.
However, such talent is undoubtedly in short supply and difficult to recruit in the market. It is predicted that in 2026, more enterprises will launch relevant training programs. But training alone will not suffice to solve the talent gap—beyond classroom learning, a mentorship approach combined with project-based practice is needed, allowing trainees to gain hands-on experience through real-world applications to develop into qualified professionals. It is recommended that enterprise customer service centers identify and select their internal business experts as early as possible, and consciously cultivate them into knowledge base specialists for the AI era. (For more information, please contact us via Email:club@kmcenter.org，Chinese version link)
经典培训课程
企业AI知识库搭建与运营培训课程
呼叫中心AI知识库培训课程
个人知识体系构建能力课程
书籍和资料
《卓越密码如何成为专家》
《你的知识需要管理》
免费电子书《企业知识管理实施的正确姿势》
免费电子书《这样理解知识管理》
