To The Moon

Artificial intelligence is moving beyond “answering questions” and “generating content” and is increasingly entering real enterprise work scenarios.

For enterprises, the most important change is not simply the addition of another AI tool, but whether AI can continuously participate in employees’ work processes: understanding problems, retrieving knowledge, supporting decision-making, simulating practice, and providing feedback and adjustments based on actual outcomes. As AI begins to participate in the process of “how capabilities are formed,” talent development is also shifting from one-off training programs to intelligent capability support that takes place continuously on the job.

YXT.com Group Holding Limited ( $云学堂(YXT)$ ) launched NeoLearning to explore this direction. Its core role, “Neo,” is not a traditional AI teacher, but an AI mentor that provides continuous coaching around specific workplace problems, helping employees move from “knowing what to do” to “being able to do it in real work.”

From “Knowing” to “Doing”: AI Enters the Capability Formation Process

The work of frontline managers rarely comes with standard answers. Take “effective delegation” as an example. Some managers may be dealing with capable employees who lack initiative, others with inexperienced new hires, and still others with employees who resist delegation itself. What managers really need is not another set of delegation methods, but the ability to judge in a specific context: How should this problem be handled? Why should it be handled this way? What should be done next?

Traditional large-scale training struggles to address these differences on an ongoing basis. Courses can be standardized, but each person faces different problems. Knowledge can be delivered, but true capability is often built through repeated practice.

NeoLearning aims to change this process. Using “effective delegation” as an example, the system starts with the actual problem faced by the manager, diagnoses the specific pain points, and then matches relevant knowledge and methods. The manager can then enter an AI scenario-based practice session and engage in realistic conversations with AI employees in different states, practicing how to handle questions, communicate goals, clarify boundaries, and address resistance in a simulated environment.

After each practice session, AI does not simply tell the manager whether the response was “right or wrong.” Instead, it analyzes the manager’s communication strategy, expression style, and problem-solving logic, then provides suggestions for improvement. The manager can practice again based on the feedback, forming a cycle of “practice, feedback, adjustment, and further practice.” In this process, AI is no longer only a provider of knowledge; it begins to participate in how human capabilities are developed.

AI Mentors Do More Than Support Learning: They Help Solve Real Work Problems

One important shift in NeoLearning is that development does not end when a course ends. When managers return to their roles, they may still encounter new problems: a delegation effort does not produce the expected result, an employee resists a task assignment, or communication conflicts arise within the team. These issues are often impossible to fully anticipate in a fixed course.

At this point, the AI mentor can continue to provide support around real workplace problems. Managers can ask questions, describe specific situations, receive analysis and suggestions, and review management actions that have already taken place. As a result, the relationship between AI and employees shifts from “learn once, use once” to continuous capability support embedded in the role.

Currently, NeoLearning covers eight core management task groups, 22 high-frequency management pain points, and nine categories of typical response experience for frontline managers. This means enterprises can further transform many complex scenarios that previously depended on individual managerial experience and mentor coaching into capability tasks that can be designed, practiced, and improved through feedback. Its value is not to make every manager work in the same way, but to provide more managers with timely intelligent support when facing different problems.

Enterprise Capability Building Is Changing

The significance of NeoLearning is not limited to manager development. If the previous stage of enterprise AI focused more on “faster search, faster writing, and faster content creation,” the next and more important question is whether AI can truly enter roles and business processes and become part of organizational productivity.

This is also an important direction of YXT’s “intelligent productivity” strategy. YXT believes enterprise intelligence is not simply about deploying AI models or adding a few AI tools. More importantly, it requires building an intelligent knowledge system that can continuously evolve, while enabling AI to enter roles and business processes. The knowledge, experience, and business rules that enterprises have accumulated over time need to be understood, retrieved, and continuously applied by AI in real work.

From this perspective, NeoLearning explores a broader question: If every employee had an AI capability partner that understands their role, has access to organizational knowledge, and can accompany them in solving real problems, how would the way enterprises build capabilities change?

In the past, organizational capability largely depended on the transfer of experience from outstanding employees, managers, and experts. In the future, these experiences can be further structured, accessed, and replicated through intelligent knowledge systems and AI capabilities. This also means enterprise talent development is gradually shifting from “providing learning content” to “providing continuous role-based intelligence.”

The Real Value of AI Is Helping Organizations Keep Getting Smarter

From courses and knowledge to capabilities and real business outcomes, enterprise AI applications are crossing new boundaries. NeoLearning shows that AI can be more than an entry point for employees to access knowledge. It can also become a “second pair of eyes” and a continuous capability partner in the work process, helping employees understand problems, retrieve knowledge, simulate scenarios, review behavior, and take the next action.

For enterprises, the long-term value of this shift is that individual capability no longer depends entirely on personal accumulation, and best practices no longer need to remain with only a small number of people. When knowledge can be understood by AI, capabilities can be continuously trained, and experience can be continuously captured, organizations gain the potential to keep evolving.

From AI mentors to role-based agents, NeoLearning is not exploring “how AI can replace training.” Instead, it is exploring how AI can enter people’s work processes and ultimately translate into organizational productivity. This may be the next step worth paying closer attention to as AI moves deeper into the enterprise.

About YXT.com

YXT.com (NASDAQ: YXT) is a technology company focusing on enterprise productivity solutions. With a mission to "Empower people and organization development through technology," the Company strives to become the supreme provider in building and boosting enterprise productivity by combining over a decade of experience in tech-enabled talent learning and development and with AI-augmented task copilots and unleashing the power of knowledge and synergy. Since its inception, YXT.com has supported and received recognition from numerous Global and China Fortune 500 companies.