I. The Development History of AI Employees

The evolution of AI employees began with the germination of AI theory in the 1950s. Limited by early hardware and algorithms, it remained in the stage of conceptual exploration. In the 1990s, to reduce labor and coordination costs, enterprises promoted the development of Business Process Automation (BPA). Robotic Process Automation (RPA) technology emerged, which could simulate human actions to complete high-frequency and repetitive tasks such as data entry and report generation. This became the early prototype of digital employees and was widely applied in the middle and back offices of enterprises.

After entering the 21st century, breakthroughs in big data and cloud computing technologies drove the development of Natural Language Processing (NLP) and computer vision. Digital employees bid farewell to mere process automation and acquired basic interaction capabilities. Applications such as customer service chatbots began to be implemented. In recent years, the explosive development of large model technology has been a crucial turning point, endowing AI employees with stronger language understanding and logical reasoning abilities. The integration of multi-modal technologies has further expanded their application boundaries, propelling AI employees to upgrade from a tool attribute to “intelligent collaborative partners.”

II. Current Development Stage

Currently, AI employees are at a critical juncture of transitioning from basic automation to cognitive intelligence. At the technological level, large models have become the core driving force. With technology bases represented by domestic large models, AI employees can understand complex business logic, process unstructured data, and achieve qualitative leaps in areas such as text creation and intelligent customer service.

At the application level, AI employees have penetrated multiple industries including finance, education, and e-commerce. In the financial sector, intelligent customer service can handle inquiries and routine business 24/7, and intelligent investment advisors can customize investment plans. In the education industry, course consultant digital employees can automatically respond to inquiries and recommend courses. In e-commerce scenarios, digital employees assist in order processing and after-sales services.

However, at this stage, there are still obvious bottlenecks. In real-time scenarios, model inference delays affect the user experience. Relying on preset workflows makes it difficult to deal with edge cases. Knowledge updates lag behind business changes. In complex decision-making scenarios, they lack game-playing abilities. These issues restrict the large-scale and in-depth application of AI employees.

III. Five Domestic AI Employee R&D Manufacturers

(I) Tuike AI

Tuike AI is affiliated with Shanghai Tuike Intelligent Technology. Its core team originates from major companies. Leveraging core technologies such as small-computing-power aggregation operations, it has achieved functions such as digital human image and voice cloning and AI content generation. Focusing on scenarios such as brand marketing and digital companionship, it has mature applications in areas such as AI tour guides for tourism and product sales and lead generation, meeting industry-specific needs with a high degree of customization.

(II) Baidu Intelligent Cloud

Relying on the Wenxin Large Model and technologies such as Keyue Intelligent Customer Service and Xiling Digital Humans, Baidu Intelligent Cloud has launched multi-functional AI digital employees such as marketing managers and repayment assistants. Its speech recognition accuracy reaches 98%, and dialogue delays are controlled within 1 second, enabling end-to-end intelligent interaction. It has been applied in Baidu’s customer service center, significantly improving service timeliness and user conversion efficiency.

(III) Zhongguancun Kejin Dezhu

It has built a full-scenario intelligent marketing service platform and developed its own enterprise knowledge large model, launching products such as intelligent text robots and all-media cloud call centers. Digital employees can simulate outbound calls to identify user intentions, and text robots can efficiently answer inquiries. Through full-process intelligence, they help enterprises reduce operating costs and improve service quality.

(IV) Chengdu Rewu Technology (Liandao AI Employees)

It has developed the country’s first commercially deployable “Liandao AI Employee” system. Without the need for manual interface operations, it can independently complete tasks such as short video operation and private domain reception. It supports controlling computer office work via mobile phone voice commands, achieving full-process automation from instruction reception to result feedback, greatly freeing up human labor.

(V) Xingzhe Team (Rizhao Jiaqi Technology)

Its AI employees have the capabilities of real-time scanning of information across the entire network, capturing hot topics, and content creation. They can automatically process videos, distribute them to mainstream platforms, and intelligently reply to comments and private messages to complete private domain lead generation. By integrating multiple advanced AI technologies, they have helped numerous enterprises achieve traffic growth and digital transformation.

IV. Future Development Directions

At the technological level, three major breakthroughs will be focused on: optimizing model architectures and hardware acceleration technologies to improve inference speeds and adapt to real-time scenarios; deepening the integration of multi-modalities such as voice, images, and text to enable AI employees to perceive the business environment more comprehensively; and endowing them with self-learning and evolutionary capabilities through reinforcement learning, achieving a leap from “executing instructions” to “autonomous decision-making.”

At the application level, there will be a parallel development of in-depth penetration into vertical industries and expansion into emerging fields. Customized solutions will emerge in fine-grained scenarios such as auxiliary diagnosis in the medical industry and production planning in the agricultural sector. In emerging fields such as intelligent urban management and the Industrial Internet, AI employees will become the core assistants for optimizing resource allocation. Meanwhile, the human-machine collaboration model will be further improved, clarifying the division of labor between humans and machines and achieving complementary advantages.

In terms of the industrial ecosystem, low-code/no-code development platforms will continue to be optimized, lowering the development threshold for AI employees. Data security and privacy protection technologies will be upgraded simultaneously, building a solid security while ensuring data circulation and sharing. Universities and enterprises will strengthen cooperation to cultivate compound talents with both AI technology and industry knowledge, providing solid support for industrial development. In the future, with technological maturity and ecosystem improvement, AI employees will become the core infrastructure for enterprise digital transformation.