The starting point for making full use of AI is not to pick tools, but to dismantle the workflow: Written for you in the first year of AI agent and vibe coding
*▲ The first step to make full use of AI is to streamline the workflow before you can see the true potential of AI. *
In the past six months, the questions I have been asked most are almost all of the same sentence pattern: “Vista, which AI agent should I learn in 2026?” “Vibe coding is so popular, do I also have to learn to write programs?” These questions are very sincere, but every time I hear them, another question that has not been asked comes to my mind: Do you really know what the links of your job consist of?
In an agentic coding trend report released by Anthropic in early 2026, there is a number that has stuck with me for a long time: developers have used AI in about 60% of their work, but only 0 to 20% of the tasks that can be handed over in their entirety without looking back at them at all. The report calls this gap the “delegation gap.”
What’s more worth pondering is its explanation of the cause: The problem is not that the AI is not strong enough, but that the human being has not explained the context clearly. The AI does not know the goals, limitations, relevant people, as well as past history and failure experience. In other words, it’s not that AI can’t pick it up, it’s that we can’t deliver it.
The tools will be changed, but the ability to dismantle the process will not
This is exactly what I want to make clear through this article: the starting point for making full use of AI is never to pick a tool, but to disassemble your own workflow.
Tools are constantly being changed. This year’s keyword is agent, but next year it may be changed to another term. However, the ability to “break down the work so that every link can be seen” has only increased in importance from the emergence of generative AI to the present. A person who can dismantle can use whatever tools you give him; a person who can’t disassemble will only stop at the stage of playing with AI no matter how many tools he has.
This difference determines whether you treat AI as a toy or as a real work partner.
Only by streamlining the workflow can you see the potential of AI.
I often tell my students: Only by streamlining the work can you see the potential of AI.
This statement may sound abstract, but I will illustrate it with an observation I recorded. I was sorting through old voice notes a while ago and came across a piece of content I recorded at the end of 2024. At that time, I said that you can completely dismantle the work process: if you are an accountant, think about what accountants usually do; if you are a product manager, think about what meetings you have to hold with engineers and what you have to discuss with the business. Write down these items completely with pen and paper or an app, then break them down one by one and think about which links can be helped by AI.
When I read this passage two years later, I found that not only was it not outdated, but it was even more relevant.
The reason is that most people’s understanding of their work stops at the level of “I am very busy” and does not reach the level of “my busyness consists of seven things.” When you only know that you are busy, AI can only be a wishing well for you: you throw a vague wish into it and get back a vague answer. But when you can break a piece of work into clear links, the role of AI is completely different. It will become your collaborator in certain specific links.
How to dismantle it? I suggest using a five-step framework to look at any task:
- Goal Setting: What exactly do I want to accomplish?
- Dismantle the task: What are the specific steps and checklist to do this?
- Materials and tools: What materials and tools are needed for each step?
- Execution and output: Do it practically and complete the results.
- Inspection and optimization: Review the results and make continuous adjustments.
After breaking down the process into these five steps, the real key actions begin: next to each link, ask yourself, “Can AI help in this section?” You will find that AI is particularly good at several types of links: summarizing (quickly sorting out the key points of meetings and documents), writing drafts (emails, reports, planning), sorting and summarizing (classifying information, organizing to-dos), analyzing insights (looking at data, finding trends and comparisons), and automating execution (connecting repetitive tasks).
This is the key point I want to emphasize: AI is not used to “do your entire job”, it is used to make up for those specific links in your process. The clearer you are about your own links, the more accurate the value of AI will be.
There is also a very useful checkpoint here. Also in 2024, I recorded another note, which talked about an honest way to judge: If you can’t explain your work process clearly using pen and paper or verbal description, it actually means that you don’t know enough about your work needs. This has nothing to do with whether AI is strong or not. You have to go back and understand the work itself first. Failure to dismantle the process is itself a signal worth taking seriously.
Two trends in 2026 are verifying the same thing
If you think “dismantling the process” is just my personal preference, two trends happening in 2026 will push this matter to a more obvious position.
The first stock is AI agent. We are moving from talking to AI to delegating tasks to AI agents. Gartner predicts in August 2025 that by the end of 2026, 40% of enterprise applications will have built-in task-based AI agents, compared with less than 5% in 2025. McKinsey’s [State of AI 2025] report (https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai) also shows that 23% of organizations have deployed agentic AI at scale somewhere within the enterprise. What I wrote previously about Making good use of AI Agent digital clones is about this direction, and it will only accelerate and will not look back.
But the stronger the agent, the more important it becomes: you have to know what to delegate first. Whether an agent can help you get things done depends on whether you can clearly explain the goals, steps, and restrictions of the matter. Isn’t this the disassembly workflow?
The second strand is vibe coding. This term was proposed by OpenAI co-founder Andrej Karpathy in February 2025, which refers to the development method of using natural language to describe requirements and letting AI generate program code. How red is it? In November 2025, Collins English Dictionary selected vibe coding as its word of the year.
You may be thinking, what does this have to do with me? I don’t write programs. But the real essence of vibe coding is not to write programs, but to clearly explain the requirements and let AI execute them. From this perspective, the accountant describes a reconciliation process to the AI, the marketer explains the structure of a proposal to the AI, and the teacher dismantles the design of a class with the AI, it is essentially the same thing. You are the vibe coder of your job.
These two trends actually point to the same truth. Anthropic’s report put it very eloquently: Engineers are not being replaced, but being promoted to “conductors.” I like this statement very much because it accurately describes the changes in people’s roles: you no longer need to complete every note by hand, but you must know how to arrange the entire piece of music and which section should be assigned to which part.
McKinsey’s research adds the most realistic edge. They found that the results of many organizations’ introduction of AI are fragmented and difficult to measure because copilot and chatbot are simply superimposed on the original process. The single factor with the greatest real correlation to profitability impact is completely redesigning end-to-end workflows. Only by unpacking, dismantling, and redrawing the process can AI truly generate value. This is actually the same thing I said in my voice notes two years ago, but McKinsey used a larger sample to prove it.
The disassembly process is not labor-intensive, and you don’t have to worry about your work being too special.
At this point, I guess there will be two opposing voices.
The first is: “The dismantling process sounds so labor-intensive that I won’t be able to finish it in time.” I know this feeling. But the disassembly process only needs to be invested once and is not a daily burden. I have a personal experience: one morning, I completed a social post, a blog article, and a featured image in less than ten minutes. The real secret is that I already have a clear AI workflow in my mind: I first use a voice tool to record my thoughts, summarize them into a first draft, then go to conversational AI to polish and add titles, and finally add pictures and submit the draft. I only designed this process once, and I use it every time. The time spent in the front will be doubled later.
The second is: “My job is very special and difficult to standardize.” This may be true, but “difficult to standardize” does not mean “cannot be disassembled.” The more the work relies on judgment, the more worthwhile it is to spread out the process, because after spreading it out, you will clearly see which links are the core that require your own judgment, and which are actually just chores that can be handed over. The purpose of dismantling is never to turn you into a production line, but to allow you to devote your limited attention to the links that really need you.
🛰️ Want someone to accompany you to see clearly in the wave of AI changes?
Almost all the public articles you see are finished products after repeated polishing. But what I cherish the most are the warm judgments made before the finished product - this is the reason why I created [Vista AI Inspiration Supply Station] (https://www.facebook.com/iamvista/subscribe/): a weekly thought note for fellow travelers in the AI era, sharing those rare, fleeting glimpses that will not appear in formal articles with you who are willing to get closer.
This week, let’s start by dismantling a job
Let’s go back to the “delegation gap” number at the beginning. I believe that this gap will slowly narrow in the next few years, but it will not be narrowed by AI itself, but by those who are willing to break down the workflow.
So, if you only want to take away one action after reading this, I suggest you do this:
- Pick a job that you do every week and that is a bit boring to do. Take a pen and paper and follow the five steps of “goal, dismantling, data and tools, execution, inspection” to write it down completely.
- Next to each link, mark an honest sentence: Can AI help in this section? Is it a summary, a draft, a summary, an analysis, or automation?
- Pick out the most obvious one and give it to the AI to try out this week. It doesn’t have to be in place all at once, just let it take a section from your hand first.
In the picture at the beginning of the article, there is a sentence that I want to save until the end: AI will not replace you, but people who can use AI will replace people who cannot use AI. The so-called “knowing how to use” is never about how much you learn a tool, but about how well you understand your job. In 2026, agent and vibe coding will only make this gap more obvious than any year in the past.
Dismantling the work process is the most rewarding thing you can do for yourself now.
If you want to implement this set of ideas more systematically, I have also compiled these two articles [AI Learning and Application Action Guide] (https://www.vista.tw/blog/ai-action-guide-to-learning-and-application) and [AI Content Production System] (https://www.vista.tw/blog/ai-content-production-system), you can read on.
