This article answers a very common confusion: tools have been bought, classes have been taken, but why isn't AI adoption effective? The problem usually lies in two areas. One is direction: most adoptions just make a certain process a little faster, but the process itself hasn't changed, and the savings that could be made from the original software have already been achieved. The other is resistance: even if you understand the direction and want to change the process, you will still hit the organization's wall, and that wall cannot be removed by buying software.
- The company is pushing AI, but the effect is only that of making something a little faster
- People who are assigned to push AI within the company but can't move it forward
- Entrepreneurs who want to adopt AI, have bought the tools but haven't seen any results
- Employees who are already using AI but don't plan to tell their company
- Integrating AI into existing processes results in digital optimization outcomes
- What's worth doing is the approach that wouldn't exist without AI, which is called native
- Employees hiding their AI capabilities is rational, not non-cooperation
- Adopting AI should be paired with organizational consultants; only buying tools without changing the system will remain stagnant
At the very beginning, steel was just used to reinforce wooden houses
When a new technology first appears, humans' first reaction is always to use it for what they were already doing.
When steel was first manufactured, what did the construction industry use it for? Making nails. Using it to reinforce wooden houses by hammering them.
Then everyone happily discovered: originally wooden houses could only be built up to two floors, but with nails, they could be built up to three floors.
This situation lasted for a long time. Until one day, someone thought: steel is such a great material, why don't we directly use steel to build houses?
This question led to the emergence of skyscrapers. And it's important to note that it wasn't just raising wooden houses higher; it couldn't use the structure of wooden houses anymore, and the entire structure had to be redesigned from scratch.
The steam engine is also the same shape. Its earliest use was to replace water wheels, and the factory was still the same factory, just the thing that drove the wheel changed. It took another several decades before someone asked that question: since the power is no longer from rivers, why should factories still be built by rivers?
Factories could be built near raw materials, near markets, or near labor. The entire industrial geographical map was therefore redrawn.
From the emergence of new technology to someone asking that question, there was a gap of several decades.
Which floor are you on now?
Dividing the use of AI into three layers will quickly let you know which layer you're on.
The process remains completely unchanged, with AI speeding up one step. A common example is a social media editor using AI to create visual posts: the publishing process stays the same, while image generation becomes faster.
Starting to question whether this process itself is reasonable, breaking down the steps and reassembling them. What changes is the process, not just a single node.
First principles, completely rebuilding from scratch. Assuming AI was available from the beginning, this task would have been done in a completely different way.
Most companies stop at the first layer and call it AI transformation.
There is one thing I need to make clear: anything that can be digitized has already been optimized by various software over the past two decades. ERP, CRM, automation workflows, and reporting tools have already gone through one round of cost-cutting. Adding AI now usually gives you a 20% to 30% improvement. That's good, but that's not AI's scenario; it's the end of digital optimization.
The things that emerge from the third layer are different. They will include practices that would not have existed without AI: things that were never done before because they required too much human effort, things that were only done by intuition because the judgment cost was too high, and cases that were too big for one person to handle and thus were never taken on.
So when I talk about this now, I will directly say: There is no AI transformation; there is only AI-native. Transformation means the original thing still exists, but it's done in a different way; native means the thing didn't exist before, and it only emerged after AI was introduced.
A rough but useful criterion
How can you tell which layer you're in now? I use a very rough but useful algorithm.
AI can help you in two directions:
- Empower the brain: Decision-making, reasoning, research, and seeing blind spots. If this is done well, it can multiply the effect by ten times.
- Empower the hands: Automation, batch processing, and repetitive execution. If this is done well, it can also multiply the effect by ten times.
If you can get both, theoretically, it's a hundredfold scale. So my criterion is: if the application you're thinking about has a ceiling of only a 20% to 30% improvement, it's likely digital optimization; if it has the potential for a quantity-level difference, that's a worthwhile native scenario.
How can you ask questions to uncover the third layer? Three questions you can directly use:
- Would I have done this differently if I had AI from the start? Yes, that would be an existing process; no, then the latter answer is your third layer.
- What things did I want to do but couldn't because of high labor costs? Those ideas that were blocked by costs are now worth recalculating.
- What judgments do I always make based on intuition? Relying on intuition means there are no rules, and writing down the rules is itself the ticket to enter the third layer.
The third sentence is the easiest to skip, but it has the most value. Because the third layer is not about faster tools, it is about turning your brain's judgments into something that can be handed over to AI.
Once the direction is correct, you will hit a wall.
The above talks about direction. Once the direction is understood, the real difficulties begin.
I have talked to many people in enterprises who are pushing AI, including those in manufacturing, public departments, and internal corporate pushers. The difficulties they encounter are almost all unrelated to whether AI is easy to use, and they are all stuck on organizational issues.
Let's solve a problem first.
Assume an employee uses AI to increase their productivity tenfold. What will happen next? The company will not add ten times the salary, and they will not let the employee leave work early. More likely, they will give the employee more work.
What is the most rational choice for this employee? To pretend to be dead.
He will continue to submit work at his original pace, keeping the saved time for himself and his abilities for the right occasion. This is not about his character being bad; it is about him making a very reasonable calculation.
So you will see a very paradoxical phenomenon: the person in the company who is most skilled at using AI is often the quietest one. And after the company has pushed AI for half a year, the effectiveness report shows nothing.
The second bottleneck is harder: permissions.
Another common bottleneck is even harder.
I have met a friend who is responsible for pushing AI in a company and asked: company computer accounts are bound, and even the tools they want to install cannot be installed. What should they do? I have also met a friend working in the public sector, where company accounts and personal accounts clash, and they are afraid to use AI services on both sides at the same time, fearing incidents.
My usual answer is usually disheartening: this is not an AI issue, it is an organizational governance issue, and it cannot be avoided.
You cannot bypass the company's information security policies through personal effort. The risk of forcing this is not something you should bear. The real issue to address is at the upper level: who exactly can make decisions, where is the boundary of information security, and are there compliant alternative solutions.
Following this logic down, it leads back to the earlier three-layer theory: Small companies are the only ones that can truly implement AI-native practices, while large companies often find themselves dealing with technical debt. Large companies possess existing systems, permission architectures, and established ways of division of labor. These elements were designed for efficiency when they were created. Implementing the third layer requires changing these things. This change involves more than just tools; it affects the organization itself. Therefore, large companies usually stop at the first layer because that is the only layer that does not require changing the organization.
Small companies don't have these burdens. You can decide today how to do things and change tomorrow. This is a structural advantage for small companies in this era, and it has a time limit.
Government departments are another version.
The situation for government departments is another version.
I've heard first-hand situations: upper management requires using AI to write official documents, even to identify personal information. However, in reality, handling such data in compliance is beyond the capabilities of local models, while online services with sufficient capability involve data transfer. No one has clearly defined where the line should be drawn in the middle.
Additionally, there is the urban-rural gap. A friend working in a government department in central Taiwan described it vividly: the latest things are in Taipei, and by the time they reach us, they are already something else. The same policy, when implemented in different places, has very different execution conditions.
None of these are technical issues with AI; they are all issues with the organization and system.
So the real things to handle are these three
My conclusion is clear: promoting AI must be accompanied by organizational consultants, otherwise you will get stuck with the above issues.
The tool part is actually the simplest, and you can get up to speed in one or two months. The difficulty lies in these three things:
Exchange it for more output, leaving work on time, or bonuses. Without an answer, employees won't be honest.
Which data can enter AI, which cannot, and which pipeline is compliant. Without this line, everyone can only do things in secret.
Do not implement it across the entire company. Start with a group, create visible results, and then expand horizontally.
The third point is something that has been done before. The manufacturing industry promotes lean production and improvement activities using this exact approach: demonstration group, create highlights, and expand horizontally. I spoke with a senior who has thirty years of manufacturing experience. He pointed out that this path resembles AI introduction. He also reminded me that to enter enterprise coaching, one must first understand human engineering. If you do not understand this, the senior craftsmen will not listen to you. This comparison seems very worth pursuing; however, it remains a hypothesis requiring verification, rather than an already verified methodology.
A way to convert for the boss
If you are a boss, I suggest converting the value of AI into an algorithm.
Most companies calculate how much labor time is saved, but that number is usually small and easily questioned. The real value of AI is not there.
Its value lies in helping you see blind spots and avoiding losses of millions. It does not derive its value from saving half an hour.
A clause not noticed in a contract, a cost missed in a quotation, a risk not checked before cooperation, or a market signal not captured. The cost of these things happening once is far greater than the labor time saved in a whole year.
Therefore, instead of requiring the entire company to use AI to increase typing speed, focus first on using it at the decision-making level: let it review what you are about to sign, test the case you are about to invest in, and ask the questions you haven't thought of.
This level also has two advantages. First, it aligns with the third point mentioned earlier, as these judgments used to rely on experience. Second, it does not interfere with employees' calculation problems, as it is not monitoring efficiency.
Conclusion
Steel was first used to reinforce wooden houses, and steam engines were first used to replace water wheels. This is the normal reaction of humans to new technology, and it is not shameful. What is shameful is not asking that question for eighty years.
But what needs to be added in the latter half of this article is: asking the question is just the first step. After the direction is correct, you still have to face the system.
Whether employees will honestly share efficiency depends on how the system distributes benefits; whether data can enter AI depends on who draws the governance line; whether results can be spread depends on whether there is a visible sample first. None of these three things can be solved by buying a software package.
Therefore, if you are struggling to introduce AI, do not change the tools first. Ask yourself two questions:
First question: If AI was available from the beginning, would we still do this the same way?
Second question: If he really achieves ten times the efficiency, what would he get?
Further Reading
- Why Your AI Has to Start Over Every Time: Four Steps to Teaching AI The third layer requires writing down judgments, this article explains how to do that.
- Information Lag and Market Forecasting: How to Be Ahead of the Market Without Being Too Early Where your customer is located determines whether they can understand the native aspect now.
- Solving AI Knowledge Anxiety: Finding Anchors That Remain Unchanged in a Rapidly Changing Era Tools will change, first determine that the task you want to complete will not change.