Five Trends in the Development of Enterprise Knowledge Management and Knowledge Bases in China for 2026
On January 20, 2025, DeepSeek released and open-sourced its reasoning model DeepSeek-R1, a landmark event.
Adopting the Mixture of Experts (MoE) architecture, this model achieved performance comparable to the world’s top-tier AI models with relatively limited computing resources. For the first time, various Chinese institutions realized that the capabilities of large models could be accessed and applied conveniently and at low cost, propelling China’s AI implementation into the experimental exploration phase. Governments, enterprises, schools, and other entities began attempting to integrate large models into their business operations.
The boom of large models also aligns with Amara’s Law of technological development: overestimating short-term impacts while underestimating long-term ones.
In the initial stage, leaders held unrealistic illusions about large models, which led to rampant gimmicks and hype. They believed that simply deploying a large model within an enterprise, collecting some data, could immediately reduce costs, improve efficiency, and enhance competitiveness.
The rapid pilot application of large models in 2025 “ignited” the popularity of knowledge bases.
Many institutions that had never considered knowledge management issues before started to understand the need to build knowledge bases to implement AI applications, driven by software vendors. For the small number of institutions that already had knowledge bases and knowledge management systems in place, the value and priority of these systems rose sharply.
However, overall, most enterprises that built knowledge bases have a relatively superficial understanding of knowledge management and knowledge bases. Many institutions assume that AI applications can be implemented by simply aggregating various documents scattered across different systems and held by different employees, and conducting basic knowledge classification.
But after a year of intensive trials with generative artificial intelligence, all institutions have finally recognized a core fact:
Generative AI can only deliver optimal performance when fed high-quality content. If the knowledge foundation is weak and unstable, artificial intelligence cannot create practical value.
Looking to the future, KMCenter believes that 2026 will be a year of abandoning hype and focusing on practical implementation—whether it comes to enterprise AI applications, knowledge management execution, or knowledge base construction.
Based on years of research and practical experience of the Knowledge Base Research and Consulting Team at the Knowledge Management Center, we make the following predictions about the trends in enterprise knowledge management implementation and knowledge base development in China for 2026 (for more detailed information, you can contact us via WeChat: 511956894 or Email:club@kmcenter.org):
Trend 1: Correcting Misconceptions—From Purchasing Knowledge Bases to Adopting Knowledge Management Strategies
2026 will be a crucial year to dispel the illusion that intelligent applications can be achieved simply by collecting and uploading data.
The craze for large models has led many enterprises to mistakenly equate building a knowledge base with achieving intelligence, and to view knowledge bases as nothing more than document repositories.
Yet practice has repeatedly proven the principle of Garbage In, Garbage Out. A knowledge base lacking systematic knowledge management will only amplify chaos and hallucinations, making the so-called intelligent applications impossible to realize.
There are two main reasons for this misconception. First, there is a misunderstanding of the relationship between knowledge management and knowledge bases.
In fact, a consensus emerged early in the development of knowledge management that enterprise knowledge management relies on three core tools: knowledge bases, knowledge maps, and communities of practice.
A knowledge base is only one tool for implementing knowledge management, similar to a chef’s kitchen knife. But the key to cooking a good dish does not lie in the knife, but in the chef’s culinary skills. The second reason is the intentional or unintentional misleading by relevant IT vendors, who claim that AI applications can be achieved simply by uploading data to their so-called knowledge bases, making it easier to complete projects quickly.
Enterprises that have experienced setbacks in AI implementation will, in 2026, begin to consider knowledge base construction and operation from the perspective of knowledge management. They will gradually realize that artificial intelligence is no longer an isolated IT project, but must be deeply integrated with knowledge management—they are inseparable.
A high-quality knowledge management system, as the core cornerstone of enterprise AI applications, will receive unprecedented attention.
More and more enterprises will shift from simply purchasing knowledge bases to systematically implementing knowledge management. Knowledge management teams will start from enterprise strategies and core business objectives, establish corresponding knowledge processes, formulate relevant standards and systems, cultivate professional talents, and build a knowledge ecosystem.
This shift will drive enterprises to establish more robust knowledge governance mechanisms, update content reserves, optimize the classification of knowledge resources, and improve metadata management systems.
With the support of systematic knowledge management, enterprises will gradually build an enterprise knowledge brain that can both support knowledge workers and serve AI applications.
Trend 2: Moving Beyond Luck—From Hit-or-Miss Intelligent Applications to Establishing AI-Ready Knowledge Content Standards
After previous pilot projects of enterprise AI applications, leaders and IT departments have recognized that without high-quality, AI-ready knowledge content, intelligent applications cannot be successfully implemented.
However, there are no corresponding standards or specifications for defining content that is truly compatible with AI and can be understood by machines. Most institutions’ practices are at a stage of blind men touching an elephant, where success or failure depends entirely on luck.
Traditional knowledge bases are more like document warehouses. In 2026, knowledge bases will no longer serve only humans, but will also cater to artificial intelligence systems, including generative AI, Retrieval-Augmented Generation (RAG), AI Agent workflows, and automation engines.
These systems have completely different knowledge requirements compared to what traditional knowledge bases can provide: they require clarity, structure, metadata, modularity, and governance mechanisms. Most importantly, the way knowledge content is written and organized must be reliably interpretable by machines.
If the quality of knowledge content is low, AI tools will produce contradictory answers, fabricate information, or even execute incorrect automated operations. The accuracy of AI outputs directly depends on the quality of the knowledge it accesses. Disorganized and chaotic information can never support the construction of an intelligent enterprise.
This situation arises because previously, knowledge content was primarily created for human employees to read—what humans can understand is not necessarily comprehensible or memorable to AI. For example, lengthy descriptive documents can confuse AI models; scanned PDFs can introduce errors and are difficult to recognize; inconsistent terminology within the same company can lead to contradictions; multiple versions of the same policy can cause response deviations; knowledge scattered across ERP systems, CRM platforms, cloud drives, Confluence, various portals, and email inboxes becomes completely fragmented and lacks contextual relevance.
Human employees can navigate this chaos, but artificial intelligence cannot. Low-quality knowledge input results in subpar AI outputs.
At this point, many people mistakenly assume that what they need is “more advanced artificial intelligence”, but in reality, what they truly need is “higher-quality knowledge”.
Yet there is no clear idea of what standards define high-quality knowledge.
In 2026, outstanding enterprises will develop and establish their own standards for high-quality knowledge, and then conduct knowledge governance within the enterprise based on these standards. This will truly provide a solid knowledge foundation for enterprise AI applications.
Knowledge has become one of the most strategic assets in enterprise operations, but its true value can only be unleashed when it is structured in a way that is compatible with artificial intelligence.
In 2026, AI-ready knowledge bases will no longer be an optional “nice-to-have”, but a fundamental requirement for all automation projects, support department transformations, and AI deployments.
Of course, establishing such standards is not easy. KMCenter has relevant practices and methodologies to share, and is willing to cooperate with enterprises that are truly committed to practical implementation.
Trend 3: Addressing Talent Gaps—From Shortages to Cultivating Internal Knowledge Management Professionals
Most enterprises find that when promoting AI projects, their IT departments lack talents who understand both traditional informatization and digitalization as well as AI. Moreover, professionals who understand knowledge management and knowledge bases while also grasping artificial intelligence are even scarcer.
This is easy to understand: most enterprises have never systematically implemented knowledge bases or knowledge management, so they have no corresponding talent reserves. Even for enterprises that did engage in these areas previously, the capabilities of relevant personnel are similar to those of document librarians, with little knowledge of how to apply artificial intelligence in enterprise scenarios.
Many large enterprises turn to KMCenter for talent recommendations when launching enterprise AI applications, but professionals with the required capabilities are in short supply across the entire society. Our suggestion to them is that they will most likely have to select potential candidates and cultivate them internally through learning and project experience.
However, the competency requirements for professionals who can both drive traditional knowledge management and provide the knowledge foundation for enterprise AI applications are becoming increasingly clear (we also offer corresponding courses). The following are two core competency elements:
Another point to note is that usually, these capabilities do not need to be mastered by a single individual, but can be achieved by building a team.
1. Strategy and Business Translation Capability
The ability to translate enterprise strategies and departmental business needs into specific data-information-knowledge frameworks. This capability was already severely lacking in the traditional knowledge management phase, resulting in a disconnect between knowledge management and business operations. Business departments and strategic leaders believed that knowledge management could not help them solve core problems and key pain points, so knowledge management failed to gain attention and recognition.
In the AI era, this capability has become even more important.
For example, if an enterprise wants to replicate the ability of business experts to quickly identify and solve problems into an AI Agent, the AI cannot understand what the experts “see” at a glance, nor can it grasp the analysis and judgment processes behind their insights. To enable the AI to have this capability, it is necessary to list the content of “seeing” (essentially information acquisition), outline analytical frameworks and models for analysis and judgment, and clarify the weight of each dimension. Only then can this goal be achieved.
2. AI Collaboration Capability
To collaborate with AI, one needs to understand what AI can and cannot do, as well as its capabilities and limitations.
It is also necessary to understand its underlying basic logic. Although knowledge management professionals do not need to be able to program, they must have a high level of basic technical proficiency and be able to skillfully use mainstream digital tools.
For example, traditional knowledge management includes knowledge classification methods, metadata management, and ontology. What is the relationship between these elements and contextual information, reasoning logic, and knowledge graphs in knowledge governance? In the AI era, these elements no longer correspond to documents, but to specific atomic knowledge.
One must be able to use technologies such as knowledge graphs or ontologies to clearly map the relationships between knowledge content and employees’ professional knowledge, and optimize knowledge structures.
At this critical juncture of transformation, the shortage of talents who understand both knowledge management and AI will persist for a long time. We have already seen some forward-thinking individuals transitioning into this field.
Trend 4: From Information Overload to Knowledge Shortage—Turning Experience into Knowledge as a Priority Task
Large models are pre-trained on public knowledge available to all humans; this public knowledge is accessible to every individual and enterprise, serving as a foundation. However, an enterprise’s competitive advantage relies on its proprietary organizational knowledge. On the one hand, this knowledge is manifested in explicit forms such as internal processes, specifications, models, and methods. But according to data from Harvard Business School, over 70% of an enterprise’s most valuable knowledge—including judgment logic, failure lessons, and personal experience—exists in the form of tacit knowledge.
In the early stages of enterprise AI application, people assumed that valuable insights could be discovered by collecting large volumes of process-related content, such as instant messaging chat records, email correspondence, and multiple versions of design drafts. However, practice has proven that such low-quality content becomes knowledge noise, reducing the accuracy of AI outputs and making high-quality knowledge even more scarce.
According to KMCenter’s knowledge hierarchy theory, enterprise knowledge can be divided into three levels: reference-inspiration knowledge, guidance-instruction knowledge, and compliance-following knowledge. What managers and employees lack most in their work is guidance-instruction knowledge, which mostly resides in the minds of experienced business experts and cannot be replicated or reused because it has not been externalized.
In the implementation of enterprise AI applications, it has also been found that for truly valuable AI applications, collecting relevant documents and extracting data from various systems is relatively simple. However, the most valuable components of each application—the analytical and judgment rules and logic (core knowledge)—lack mature content. Enterprises have to rely on business experts to compile a few relevant rules temporarily, resulting in incomplete and unsystematic content and constant problems. All these are manifestations of knowledge shortages.
To address the shortage of an enterprise’s proprietary knowledge, it is necessary to carry out the work of turning experience into knowledge.
This task is different from traditional knowledge extraction (most PPTs and documents generated through traditional knowledge extraction have limited value). It requires first conducting a knowledge gap analysis to draw up a demand list, and then organizing business experts to produce the corresponding content.
In practical implementation, we have found that even if business experts are willing to undertake this work, they often lack the necessary methodologies.
Most people are only able to perform tasks, but have not reached the stage of understanding the underlying principles. The ability to perform tasks stems from scattered, fragmented insights, which are applicable only to specific scenarios, not necessarily universal, and do not rise to the level of knowledge.
To truly turn experience into knowledge, one needs to have the ability to analyze the data, information, and knowledge that underpin the ability to perform tasks—a capability that is not easily acquired.
Furthermore, a large number of employees born in the 1960s and 1970s will soon retire one after another. Coupled with widespread corporate layoffs and accelerated staff turnover, knowledge loss has become severe, making turning experience into knowledge a priority task that must be addressed.
Trend 5: Demystifying AI—From Mythologizing AI to Cultivating Expert-Level Talent Through Knowledge Management Methods
Generative AI can filter, classify, and push knowledge content in real time, enabling the wider reuse of truly valuable knowledge and improving efficiency and capabilities. But the final judgment on what constitutes true, reliable, and valuable knowledge rests with humans.
Where does truly valuable knowledge come from? It must come from expert-level employees within the enterprise.
Without high-level experts, it is impossible to generate high-quality knowledge. No matter how powerful AI is, it cannot enhance an enterprise’s competitiveness—a fact proven by numerous cases of internal AI applications in domestic enterprises.
On the one hand, AI can empower every individual. But within an organizational context, business experts need to empower AI: when business experts externalize their insights, judgments, and methods, and AI amplifies these, it represents the highest level of capability of the enterprise in that function or field, which is the source of the enterprise’s competitive advantage.
On the other hand, AI excels at automating repetitive, rule-based tasks, fundamentally changing the nature of work. Therefore, the remaining tasks are often complex, ambiguous, and require innovation—areas where experts excel, and only expert-level employees can complete. This shift highlights organizations’ growing reliance on experts, who are the only ones capable of addressing high-risk, non-routine challenges that AI cannot handle alone.
In 2026, enterprises’ demand for expert-level employees will become even more intense.
Organizations without a large number of experts have no future. AI is an accelerator that can amplify expert capabilities and share these capabilities within the organization. This will lead to a situation where strong enterprises become even stronger, while weaker ones find it increasingly difficult to catch up. Conversely, if an enterprise lacks experts, or its internal experts are not up to par, the upper limit of the enterprise’s capabilities in corresponding functions and businesses will be low, and it will be eliminated in the competition.
But how can enterprises cultivate a large number of expert employees on a large scale?
Traditional training methods, mentoring, and learning-by-doing have a certain effect, but their efficiency and outcomes are not ideal.
Truly effective expert cultivation requires leveraging knowledge management methodologies, combined with the construction of job-specific expert knowledge systems. It starts with a knowledge gap analysis to draw up a learning list. Based on the list content, employees conduct thematic research-oriented learning, acquire mature external content and adapt it to work scenarios, summarize personal experience through high-quality practical opportunities provided by the enterprise, consult others, and then achieve rapid improvement. At the same time, using output to drive input promotes the externalization of experiential knowledge.
Conclusion
In 2026, driven by AI applications, more and more institutions will begin to develop knowledge bases and implement knowledge management.
However, only those enterprises that abandon hype, regard knowledge management as a core business support system, are willing to implement it systematically, and focus on achieving breakthroughs in specific scenarios that address business pain points will truly achieve results. Instead of pursuing a comprehensive “universal brain”, they will build one “expert-level business assistant” after another.
In contrast, enterprises that hope to achieve intelligence simply by “buying an intelligent knowledge base software”, lack top-level design, fail to integrate knowledge management with business operations, and do not have professional operation teams will find themselves trapped in a predicament where they have knowledge bases but no real intelligence, and AI systems but no practical value.
(This article was written by Tian Zhigang, a renowned knowledge management expert,Founder Of KMCenter. You can contact him via Email: club@kmcenter.org.)
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