AI is not a tool for the organization, but its "competitor".

How to transform into an AI-native organization is a key topic in enterprise management in 2026.

Employees can write prompts, departments have developed Agents, and individual costs have decreased. It seems that the transformation is underway. But at the company level, delivery cycles, customer experience, revenue and profits have not changed synchronously.

As a result, an increasingly widespread confusion has emerged: all employees have improved their efficiency, why hasn't the company become more efficient?

In the latest issue of Hunhe Online Course, teacher Ren Xin's answer is: The problem is not that AI is not used enough or well enough, but that AI is only applied at the "task layer", does not transform the "coordination layer", nor connects internal efficiency to the "ecosystem and market layer".

This lesson talks about the core principle of AI-native organizations: it is not "organization + AI", but to take AI as the core, reconstruct the organizational coordination mode around tasks.

01

Why employees using AI does not turn the enterprise into an AI-native organization

I am an AI entrepreneur, investor and architect. What I want to share with you is that last year many people came to ask me: How should the company implement AI? Which AI tool should we use?

But this year the questions have all become: Our company seems to have tried all AI tools, many links and many people have used AI, but the organizational efficiency has not changed, the company is still the same as before. Although manpower and material resources are saved after using AI, the overall cost is reduced, but we have not earned more money, and the market has not expanded. What is going on here?

After listening to all these cases, I feel that the current AI implementation has entered a "ghost story" stage: smoke is everywhere but there is no real fire; flowers are blooming everywhere but no fruits are visible. Everyone is working faster, but the company is not moving faster.

How to achieve real transformation? I think first of all, everyone needs to think about which stage they are in.

The first stage is still in the exploration phase, not knowing what AI can do; the second stage is that employees and departments have started to use AI, but the overall organization has not achieved obvious efficiency improvement; the third stage is that certain costs and working hours have decreased, but commercial returns have not appeared; the fourth stage is when efficiency improvement and market value are realized at the same time.

In reality, I see a large number of enterprises stuck in the second and third stages. Why? Because AI is not just a tool, it affects three levels of the enterprise at the same time:

The first is the task layer: AI makes us do things faster, makes us work more efficiently.

The second is the coordination layer, which affects how the entire organization schedules resources, aligns directions, and promotes results. Whether the information is consistent, how work is discovered and assigned, how upstream and downstream handover, and how quality is inspected all belong to this layer.

The third is the ecosystem and market layer, which changes the ecology of the entire market, determining whether the enterprise is still needed by customers, and whether it can continuously generate revenue, profit and competitiveness.

Task efficiency improvement is only the basic first layer. To transform from individual efficiency improvement to organizational output, we need to move up to the coordination layer; and to transform from organizational output to commercial value, we need to move up further to the market layer.

Many transformation projects only complete the first jump, and rush to declare victory. But the most important thing in business is not to produce something efficiently, but to meet demand and occupy a favorable ecological niche. Efficiency can be determined by the internal efforts of the company, but value must be determined by the market. High efficiency without demand will only create more efficient waste.

Then how should we use AI to transform the coordination layer? Many people may still think of making fine adjustments based on the existing organizational form, for example, should certain departments be merged? Or should a certain process be modified? This kind of thinking assumes that the organization itself remains unchanged, and only needs to embed AI into the existing process.

But we should first go back to the first principle to think: Why do we need an organization?

An organization is a product invented to solve coordination problems. Hierarchies, departments, KPIs, approvals and job ranks are all built on the premise that human bandwidth, memory and professional capabilities are limited. Now AI has changed this premise. As a result, the organizational forms that were reasonable in the past may become "historical fossils" remaining today.

For example, there used to be a folk custom that "you can't wash your hair or take a bath during the postpartum confinement period", but this was because there were no water heaters or hair dryers in ancient times. Now that conditions are better, there is no need to worry about these at all.

Therefore, our thinking on AI organizational transformation should also have a subversive change in concept, it is not to adjust the organization to make good use of AI, but to use AI to replace the organization. Think about how to make good use of AI to replace various functions of the original organization, so as to improve your overall efficiency.

From this perspective, AI is not only a tool for the organization, but also a competitor of the old organizational coordination method.

Then in which aspects should it compete with the organization? In fact, they are the three core functions of the organization: alignment, advancement, and closed loop. In the past, they were mainly completed by people, meetings, hierarchies and systems, and in the future they will be undertaken by continuously running AI systems. The essence of an AI-native organization is to think about how to use AI to achieve better organizational alignment, task advancement, and business closed loop.

02

The premise of AI transformation: everyone faces the same world

What is alignment? There are two types of alignment in an organization: one is factual alignment, where the project has progressed to, what happened to the customer, which data is abnormal, and whether everyone is looking at the same map; the other is tactical alignment, after sharing the same map, whether everyone follows the same priorities, boundaries, principles and methods when making choices.

In the past, the larger the organization and the more people there were, the more lines needed to be aligned, and the more difficult it was to achieve alignment. Therefore, large companies would be divided into departments, teams, and processes, compress information through meetings, reports and PPTs, and then the receivers reconstruct the information with their own experience. Information will continue to be distorted in this process of compression and reconstruction, and finally everyone thinks they understand, forming an "organizational illusion", but in fact they hold different versions of facts.

Shared documents once tried to solve this problem, but they have two problems: no one is willing to keep writing, and no one is willing to keep reading. Most of the company's knowledge bases are abandoned because no one updates them.

But now AI can become that "person" who maintains information for a long time, updates it at any time, answers questions whenever asked, and continuously and quickly allows everyone to easily get the part of information they need. Therefore, AI can help us achieve factual alignment, so that the whole company shares the same real-time map.

The most extreme case I heard in this regard is Mobvoi. Each of their projects has an Agent managing all the context, progress, and to-dos. The founder Li Zhifei even said that "he prohibits meetings and communication only between people, all meetings must be held with the Agent, so that the Agent knows everything".

So think about it, a real AI-native company does not do a single thing extremely well, but creates a working object that can be aligned.

For example, if a product exists in the materials of the marketing, R&D, e-commerce, customer service and offline teams separately, its name, selling points, status and responsible person are very likely to conflict with each other. A better way is to let all departments work around the same continuously updated "product identity", and AI is responsible for identifying changes, prompting conflicts and supplementing context. When all teams collaborate around a unified digital object, many alignment work no longer relies on verbal synchronization.

In addition to aligning facts, we also need to align tactics.

In the past, enterprises usually used training, systems and KPIs to align tactics, but these three methods often faced the problem of "paying lip service". Because people will forget, and rules may also conflict with each other. For example, the advertising team may overdraw brand assets in pursuit of short-term ROI; the sales team may leave risks to subsequent performance in order to close a deal.

Therefore, when we align tactics, we must not write them into specifications and set indicators only to be "put on the shelf", but let the tactics enter the entire work process, so that it can be invoked and maintained by the system.

Then we can use AI to write the principles directly into the working environment, and automatically pop up constraint reminders when working. For example, automatically check brand elements when submitting design drafts, automatically bring in risk boundaries when generating plans, and automatically call relevant historical cases before key decisions.

All in all, using AI to help organizational alignment, the best state is that there is no need to deliberately align.

Factual alignment does not prevent people from communicating with each other, but allows everyone to operate the same object, which preferably has a specific entity and does not need to be described in words. Tactical alignment is not to make demands on everyone, but to let everyone follow the rules unconsciously in the process of working.

If these two points can be achieved, the whole company will be more united. This is the first step of AI organizational transformation.

03

Let work emerge and flow on its own

The second step is to use AI to promote work. Advancement means breaking down the company's decisions into tasks and ensuring that the tasks are truly completed. For example, who to assign to? When to do it? Who to send for approval? And how to check the final result.

The key to this step is that in the past, work losses in organizations often did not lie within the tasks, but in the links between tasks. It is not that the task is done slowly, but that people wait too long: waiting for information, waiting for plans, waiting for scheduling, waiting for products, waiting for approval. Very often, the work gets stuck in "touch base again", "study it again", "ask someone else for their opinion". Then why not let AI urge progress? AI knows the context and dares to offend people, so it is very suitable to act as a PMO (Project Management Office).

Therefore, when we design organizational-layer AI, we should not design a bunch of Agents for employees to choose whether to use or not, but let AI promote the process, and people make judgments at key nodes.

Only when AI promotes the process, using AI will become a default mandatory option. Otherwise, employees will feel that even if they learn to use AI to do work, they will still get stuck in the human process, so the faster they do, the more troublesome it will be.

Specifically, using AI to promote work is to do four things: discover work, assign work, transmit work, and inspect work.

Discover work: Let anomalies raise their hands on their own

In the past, we needed people to discover work, for example, people would check reports regularly, hold meetings for analysis, and inspect the site. But now AI can continuously observe the business status, analyze at any time, send out work signals when anomalies occur, and bring a preliminary diagnosis.

For example, a few years ago, Haidilao required waiters to "watch all directions and listen to all sounds", but now the store uses cameras to monitor the whole field in real time, judge which table is empty and needs to be cleaned, generate work items, and assign tasks.

We can think about it, in fact, a large number of past jobs are all about checking the situation, finding problems, and generating work items. These things should be done by AI. This is not to let AI replace people to make all judgments, but to shift people's attention from "checking all normal situations" to "dealing with real exceptions", and AI should not directly replace front-line workers.

Assign work: Let tasks find the most suitable resources

Today's work assignment still relies heavily on managers: who is free, who is good at it, who understands the history, which is often judged by experience. If it is an AI-native organization, after AI discovers the work, it will assign it to the person who is most suitable to complete the work. This is like the order dispatching logic of the intelligent scheduling system of food delivery platforms or travel platforms.

AI can understand the nature of the task, personnel capabilities, current workload and historical performance at the same time. Let AI control the overall process: it can assign tasks to AI first, and if AI cannot complete it, then judge what kind of human experience and judgment are needed to intervene.

In this way, what people get is not a series of fragmented operations, but a relatively complete problem worth taking responsibility for. After this process is completed, you can embed the entire AI into the organization.

Transmit work: Let the context follow the task handover

Assignment is about who the work belongs to, and transmission is about who the work is handed over to after it is completed. A classic example here is the "assembly line", where the conveyor belt allows work to automatically reach the next station.

Each station only needs to do its own work, the work will automatically be transmitted to you with all the work order information, and after you finish it, it will automatically be transmitted to the next station, no need to re-explain the background, so that the efficiency is the highest.

When work flows through such an observable pipeline, some past bottlenecks will also be exposed. For example, in the past, "manual transmission" would always get stuck in a certain link, because the person in charge of this link was already overloaded, but it was not easy to be found. In the past, during "manual transmission", people also needed to communicate face-to-face for some knowledge missing in the handover documents, but this invisible knowledge often only exists in the minds of employees, and the next time it needs to be re-communicated and supplemented.

Inspect work: Shift from person-to-person supervision to mechanism guarantee

The last point is very important, we need to use both AI and humans to carry out a lot of inspections.

Because AI may also make mistakes and have hallucinations, and people will also forget and falsify. Therefore, we can neither completely resist AI, nor completely rely on humans to review. A more feasible approach is to build a set of mechanisms to inspect AI, use AI to fight against AI, and use humans to check exceptions, so as to improve the overall quality.

Setting checkpoints is very important, even more important than designing a good process. We should not only set up machine checks, but also check whether the machine checks are correct, and many key things need humans to check again.

04

To make the organization evolve continuously, a closed-loop system needs to be established

Alignment allows everyone to see the same world, advancement allows work to flow continuously, but for the organization to truly evolve, the results need to be brought back to the system. This forms a closed loop. The term "loop" is very popular in the AI circle, which refers to closed loop.

There are two types of closed loops: one is capability closed loop, whether experience has been precipitated from the work; the other is market closed loop, the external feedback of the work.

Human nature is not very willing to form a closed loop, because producing results is a sense of accomplishment, but reviewing the results means facing the bad parts directly, which will bring a sense of frustration. So most people will work hard, but don't want to reflect. But the company will force reflection through mechanisms such as weekly meetings, strategic meetings, and review meetings. In this process, people often care about face, relationships and responsibilities, and dare not speak their minds. AI is particularly good at this, because it has no emotions.

Therefore, first of all, we need to develop a habit: precipitate any success or failure into the next round of capabilities. For example, organize it into invocable Skills, project descriptions or work rules, so that it is no longer just a piece of knowledge, but will directly affect the next execution.

For the company, it is necessary to share this individual-level review and summary, and establish organizational-level and project-level MD documents, which may be continuously abstracted and summarized by AI, and invoked in the next action.

However, internal efficiency improvement and capability improvement are not the end point, and ultimately external closed loop and market selection are required.

The most obvious example is advertising. After you use AI to improve efficiency and run more advertisements, you need to know the effect of the delivery: who clicked, who didn't click, is there any problem with the strategy? How should the materials be modified? You need to accept market inspection, and then bring the feedback back to the next round of decision-making.

So is it possible for us to get market feedback information faster and more directly, adjust strategies in time according to market feedback information, and make our tactics more and more correct? We can let AI enter this closed loop. Let AI continuously monitor user behavior, sales changes and leading indicators, to help you judge whether this thing is worth doing.

It should be noted that one of the cores of these designs is to put AI in the central position, allowing it to make decisions, so that a closed loop can be formed. Just like if it is your assistant and you make all the decisions,