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Three stages of AI implementation in enterprises

Written by

Audrey Miles

Updated on:June-09th-2025

Recently, I've been studying the landing of intelligent bodies in the B-end, and this article serves as the output of some of my thoughts.

The three phases of AI in enterprise landing: RAG, Workflow, Agent

Intelligent Body and Agent

AI is a localized translation of Agent.

Intelligent body is the localized translation of Agent, the two are essentially a thing.
In the early domestic product marketing and promotion, a lot of "cue word encapsulation", "simple plug-in call", "workflow" products, known as the intelligent body, but in fact their own level of intelligence is Insufficient
If a system is called "intelligent", but it is essentially a rule-based linear process (e.g., workflow), then it is not really intelligent, but more like a "pseudo-intelligent" or "intelligent workflow". It is more like a "pseudo-intelligence" or "intelligent workflow".

1. Blurring of marketing terms :
Many companies may intentionally blur some technical terms when promoting their products, packaging "workflow" as "intelligence" to emphasize the "intelligent" features of their products. to emphasize the product's "intelligent" characteristics. But in fact, workflow does not have the learning ability and autonomous decision-making ability of an intelligent body.
2. Functional crossover :
In some cases, workflow may embed some intelligent modules (e.g., AI-based recommendation or decision-making), which makes it look more like an "intelligent body". But overall, workflows are still centered on executing tasks according to rules rather than making autonomous decisions.
3. Misunderstanding of the concept of Intelligent Body :
In the process of technology dissemination at home and abroad, there may be a misuse or expansion of the concept of "Intelligent Body", resulting in some systems that do not have the core characteristics of Intelligent Body being called "Intelligent Body".

Why these three directions

2.1 RAG: Basic Landing

Characteristics

RAG is a technology that combines Retrieval and Generation to enhance the answering capability of a language model by calling external knowledge bases or databases.
The main role of RAG is to solve the problem of knowledge limitations of large models by enabling them to access up-to-date information or domain-specific knowledge in real-time and to generate more accurate and practical answers.

Reasons for landing

1. Low technical threshold :

The implementation of RAG is relatively simple, requiring only the combination of a Large Language Model (LLM) with a retrieval system (e.g., vector database, search engine).
Many off-the-shelf tools (e.g., dify, RAGflow, FastGPT, etc.) can be used to quickly build a RAG system.
2. Clear requirements :

In enterprise knowledge quizzes, document searches, customer support, and other scenarios, users need accurate, real-time information, and RAG can well meet these needs.
For example, for professional document Q&A in legal and medical fields, RAG can provide reliable answers by searching authoritative data sources.

3. Wide range of application scenarios :

RAG has been used in a large number of applications in the fields of enterprise document management, customer service, education, etc. The market demand is clear, and it is easy to verify the ROI.

Summarize

RAG is the foundation of intelligent body application because it directly improves the practicality of the large language model, and the technical realization and commercial value are clearer.

2.2 Workflow: Middle Level Landing

Characteristics

Workflow refers to the automation of complex business processes, such as approval, data processing, and task assignment, in the form of a fixed code flow.
It usually involves the collaboration of multiple steps, and may need to call different APIs, databases or other tools.

Reasons for landing

1. Increased complexity :

Workflow is more complex than RAG because it requires stringing together multiple tasks and ensuring logical and reliable execution.
For example, automated order processing may involve multiple steps such as inventory inquiries, payment verification, logistics arrangements, and so on.

2. Enterprise demand-driven :

There are a large number of repetitive and rule-based tasks in enterprises that need to be automated, and intelligentsia can significantly improve efficiency in workflow automation.
For example, in financial statement generation, the contract approval process, and other scenarios, an intelligent body can assist in completing.

3. The tool chain is maturing :

With the popularization of RAG and other technologies, many tool chains (e.g., Dify, Coze, n8n, etc.) can already support complex workflow automation.

Summarize

Workflow is an intermediate stage in the application of intelligence, as it requires more modules to be technically integrated, while the market demand is gradually shifting from single tasks (e.g., information retrieval) to the automation of complex tasks.

2.3 Agent: Advanced Landing

Characteristics

Agent is a more complex and intelligent form of application that can autonomously perceive the environment, plan tasks, and perform operations, and even make decisions in uncertain environments.
Intelligent bodies usually need to combine multiple techniques (e.g., reinforcement learning, dynamic planning) to achieve a high level of intelligent behavior.

Reasons for landing

1. Highest technical threshold :

The implementation of Agent requires stronger technical support, including task planning, contextual understanding, and multimodal interaction.
It not only needs to call external tools, but also needs to realize dynamic adjustment and self-adaptive capability in complex scenes.
Agent in vertical scenarios often needs to be fine-tuned, which is a huge cost of time, labor, and computing power for general enterprises.

2. Application scenarios are not yet fully mature :

The potential of Agent is huge, but the current market demand is still focused on clearer tasks (such as RAG and Workflow).
In some high-complexity scenarios (and B-side scenarios are mostly such complex, low fault tolerance scenarios), the Agent's ability is still being explored.

3. The inevitability of gradual evolution :

The construction of Agent often needs to be based on RAG and Workflow. RAG provides knowledge support, Workflow provides a task execution framework, and Agent realizes the higher level of intelligence on this basis.
For example, an intelligent customer service agent may need to acquire knowledge through RAG, then complete the specific process of user requests through Workflow, and finally realize the autonomy of the whole process.

Summarize

Agent is the advanced stage of intelligent body application, and its landing needs higher technical support and a more mature market demand.

Why RAG → Workflow → Autonomy?

Why RAG → Workflow → Agent?

The complexity of technology increases gradually :

From RAG to Workflow to Agent, the complexity of technical realization increases gradually, so it needs to be realized in stages.

Gradual increase in market demand :

The market demand for intelligence gradually expands from single-task (information acquisition) to complex tasks (process automation) and comprehensive intelligence (autonomous decision-making).

Ecosystem improvement :

The development of RAG and Workflow provides the technology and application foundation for Agent, which makes the landing of Agent possible.

This phased development path not only conforms to the logic of technology development but also fits the evolution of market demand.

The other side of the enterprise landing

In fact, the current enterprise, often from Workflow to RAG to promote the

RAG is easy to land, but it is difficult to do a good job, and high accuracy is even more difficult.

3.1 Why is the threshold of enterprise application of RAG higher?

Data Quality and Knowledge Base Construction

The core of enterprise-level RAG is the construction and maintenance of a "high-quality knowledge base". This requires:

Data collection and organization: Enterprises need to clean, classify, and structure their internal documents, databases, logs, and other data.
Knowledge base updating and management: The knowledge base needs to be updated in real time to ensure the accuracy and timeliness of the information.
Privacy and Security: Enterprise data usually involves sensitive information, and to protect privacy and meeting compliance requirements (e.g., departmental permissions, personnel permissions) in the retrieval process is a high threshold.

Scenario Complexity

RAG scenarios in enterprise applications are often very complex. For example:

In the legal or medical domain, RAG needs to retrieve authoritative documents and generate highly accurate responses with a high cost of error.
In finance, RAG may need to process dynamic data (e.g., stock quotes) in real-time and combine it with historical data to generate predictions.

Technical Complexity

The implementation of RAG requires the integration of multiple technologies:

Vector retrieval: Efficient vector databases need to be built, and query performance needs to be optimized. Even professional teams (e.g., data engineers, NLP experts) are required to develop and optimize the system.
Multi-modal data support: Enterprise data may be in text, table, image, video, etc. The RAG system needs to support multi-modal retrieval and generation.
Model fine-tuning: In order to meet the specific needs of enterprises, RAG often needs to fine-tune the large language model to increase its domain adaptability.

3.2 Why is the enterprise application of Workflow relatively simple?

Clear Rules and Processes

The core of Workflow is to automate business processes that already exist in the enterprise, and these processes are usually explicit and rule-based. For example:

Automated Approval Process: Clear logic from form submission to approval.
Data Processing Processes: Regularly extracting data from the database and generating reports.

Due to the regularity of these processes, the development difficulty is relatively low.

Mature tool ecosystem

There are many mature workflow-building tools on the market today, both commercialized products and open-source frameworks:

Low-code/no-code platforms: Such as Dify, Coze, and n8n, allow organizations to quickly build automated processes.
API Integration Tools : By calling off-the-shelf APIs, organizations can easily achieve cross-system workflow connectivity.
Modular design: The Workflow system is usually modular; enterprises can choose and combine functions according to their needs.

Lower technical realization threshold

Compared with RAG, the technical realization of Workflow is more focused on logic orchestration and system integration:

There is no need for complex model training, only the integration of existing tools and systems.
Data processing requirements are usually structured and less technically challenging.

Quickly Validate ROI

The effects of Workflow automation are easy to quantify: reduced manual labor time, increased efficiency, reduced error rates, etc. This makes it easier for organizations to validate their return on investment (ROI), thus speeding up the time to market.

Although RAG is the basis for intelligent body applications from the perspective of technology development path, Workflow automation may be easier to prioritize for landing from the perspective of enterprise-level applications, and the priority may be higher than that of RAG, but the two are complementary, and ultimately, they can work together to build an intelligent enterprise system.

Enterprises prefer to start with Workflow because it has a low threshold and quick results, while RAG is suitable for in-depth application in specific scenarios.
You can first automate Workflow to realize efficiency improvement, and then gradually introduce RAG technology to enhance the ability of knowledge-based tasks.