Must Companies Undergo AI Transformation? Agent Deployment Starts with Standards, Then Automation

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Must Companies Undergo AI Transformation? Agent Deployment Starts with Standards, Then Automation

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Why must companies undergo AI transformation? Starting from a discussion on Bilibili, this article explains that AI transformation is not about having the boss learn tools, but about handing repetitive labor to AI agents, and emphasizes the deployment path of first streamlining standard processes before introducing agents.

Article Content

Many business managers are asking: is AI transformation really necessary? A discussion on Bilibili about enterprise AI transformation offers a blunt verdict—without AI transformation, a company will sooner or later be left behind by its peers. This claim sounds a bit alarmist, but think back to what happened to the companies that refused to go online back in the day—the answer isn't hard to find. Doing well now doesn't mean you'll stay securely on top in the future.

AI Transformation Is Not About Making the Boss Learn Tools

When it comes to AI transformation, many people's first reaction is to have the boss or management learn a few AI tools—for example, learning to use a chatbot to write copy or make spreadsheets. This discussion explicitly points out that this understanding misses the mark.

True AI transformation is about handing the company's fixed, repetitive labor over to purpose-built AI agents. The boss's job is to run the business well, not to become an agent expert. Agent deployment and similar matters can be left to professionals. This viewpoint actually highlights a common misconception: AI transformation is a redesign of organizational processes, not a remedial course in personal skills.

What Agents Can Do: Starting with Repetitive Q&A

The most typical scenario is customer inquiries. Customers ask the same questions every day. In the past, someone had to be assigned to answer them daily, and different people gave inconsistent answers—consuming manpower and hurting the experience.

An agent can connect to the company's own business materials and just get the work done directly, rather than only being able to chat briefly like an ordinary chatbot.

What's saved is not just manpower, but cost and response speed. Customer questions can be answered instantly and consistently—that in itself is part of competitiveness. For high-frequency repetitive scenarios such as customer service, after-sales, and internal Q&A, the value of agents is most direct.

The Difference Between AI Agents and Ordinary Chatbots

An AI agent is an AI system that can perceive input, autonomously plan steps, call external tools, and execute multi-step tasks to achieve goals, whereas an ordinary chatbot typically only generates single-turn or multi-turn dialogue and lacks the ability to operate external data and business systems. The key difference is that an agent can connect to business interfaces such as enterprise knowledge bases, CRM, and ticketing systems, extending "answering questions" into "completing tasks"—for example, querying orders, modifying appointments, generating tickets, and routing them to the relevant departments. Achieving this capability usually relies on Retrieval-Augmented Generation (RAG) technology—first retrieving relevant content from the company's own materials, then having the large model organize the answer, ensuring responses are based on the company's real data rather than model speculation. This also explains why the article emphasizes that agents must "connect to the company's own business materials": the quality and degree of structure of the materials directly determine whether the agent can do the job correctly and completely.

Standards First, Agents Second: Prerequisites for Deployment

However, agents cannot directly take on just any business. The discussion specifically emphasizes an order: first streamline the standard processes, then bring in the processes.

If the process itself isn't clear, even a real person hired wouldn't know what to do—let alone an agent. This statement is very down-to-earth—agents execute clearly defined rules and processes; when processes are chaotic, automation only amplifies the chaos. So the correct path is "standards first, agents second"—this step cannot be skipped.

The Order of Process Standardization and Automation

Process standardization refers to defining the inputs, judgment rules, exception branches, and output results of a business activity in a clear, reproducible way, so that different people executing it can achieve consistent results. This is a prerequisite for automation to take effect, because agents, like traditional automation tools, can only execute clearly defined rules; when facing vague links that depend on personal experience and judgment, they have no way to start. The industry's common saying "standardize first, then automate, then intelligentize" is precisely the progressive expression of this logic: standardization solves "what to do and by what rules," automation solves "executed by the system," and intelligentization solves "the system can handle a certain degree of unstructured input." Skipping standardization and going straight to agents is equivalent to handing undefined processes to machines; when errors occur, it's hard to tell whether it's a process problem or a model problem, and troubleshooting costs are actually higher.

Entry Points for Enterprises of Different Sizes

For small and medium-sized enterprises, you can start with the links that are most repetitive and have the clearest rules, such as answering common customer questions, order status queries, and internal policy Q&A. These scenarios have clear boundaries, and agents are likely to show results once connected to business materials.

For larger enterprises, process mapping is itself a project. You need to first solidify cross-departmental standardization, then consider the scope of agent deployment. Rushing often leads to frequent errors after agents go live, which instead undermines the team's confidence in AI.

Regardless of size, one thing is common: the starting point of AI transformation is business mapping, not tool procurement. Only by identifying repetitive labor and standardizing processes can agents have a role to play.

Differences in AI Deployment Between SMEs and Large Enterprises

SMEs' advantage is a short decision chain and relatively concentrated business scenarios, making them suited to a "single-point breakthrough" strategy: choose a high-frequency scenario with clear boundaries, launch quickly, validate results within a few weeks, then gradually replicate to other links. Large enterprises' difficulty lies in lengthy cross-departmental processes and data scattered across multiple systems; for agents to be effective, data governance and interface integration often need to be completed first, requiring a longer investment cycle. Therefore, large enterprises more commonly set up a cross-departmental AI task force first, select one or two pilot business units to run through the closed loop, form reusable templates, and then scale up. The common risk for both types of enterprises is "AI for AI's sake"—procuring tools without a clear business problem and measurement metrics, ultimately making it hard to prove value.

The Urgency of Transformation and a Realistic Path

Back to the original question: why must companies undergo AI transformation? Because competition won't wait. Most companies that refused to go online back then were later left behind by peers that did. The logic of AI transformation is similar—it changes how enterprises handle repetitive labor and respond to customer needs, and these are precisely the underlying efficiencies of daily operations.

But urgency does not equal blind action. The path offered by this discussion is clear: first streamline standard processes, then hand fixed repetitive labor to purpose-built agents, with the boss focusing on the business itself and deployment execution left to professionals. This approach neither exaggerates AI's capabilities nor underestimates the difficulty of process redesign—for enterprises considering AI transformation, it is a pragmatic starting point.

One-line Summary

AI transformation is about handing repetitive labor to agents, on the premise of first streamlining standard processes.

Paragraph Summary

The article addresses the question of whether AI transformation is necessary, pointing out that companies that don't transform may be left behind by peers, just like companies that refused to go online back in the day. The article clarifies a common misconception: AI transformation is not about having the boss learn AI tools, but about handing the company's fixed, repetitive labor to purpose-built agents, with the boss focusing on the business itself and deployment execution left to professionals. Taking customer inquiries as an example, agents connected to the company's own business materials can respond to high-frequency questions in a unified and instant way, saving manpower and cost. The article emphasizes that the deployment order is "standards first, agents second": when processes are unclear, automation only amplifies chaos. For enterprises of different sizes, the article suggests that SMEs start with high-frequency scenarios with clear rules, while large enterprises first complete cross-departmental standardization before deploying in batches.