5 Steps to Introducing AI in the Workplace | How note Inc. Switched from Cursor to Claude in 4 Months

There is a company that discarded the AI tools it had distributed to its entire staff after just four months. That company is note Inc. In December 2025, they distributed "Cursor," an AI-powered editor for engineers, to all job roles. By April 2026, they had switched the entire company to Claude. They had to redo their training materials and have employees who had just gotten used to the tool relearn everything—yet they still made the switch.

This article explains the substance of that decision and the 5 steps to apply the same procedure to a scale of 1 to 30 people. "We gave them AI, but they don't use it." "We trained them, but they went back to their old ways the next day." This happens regardless of company size. What note Inc. did with 15 teams and 72 people, mostly non-engineers, was a procedure designed to tackle that problem head-on.

The source is note Inc.'s official note, "Switched from Cursor to Claude in 4 Months After Distributing to the Whole Company. How note's Company-wide AI Promotion Was Led by Two Engineers" (published September 18, 2026), and the content was verified on September 20, 2026. All figures and judgments were published by note Inc., and this media's role is to translate them into 'how would I act if this were my company or my job'. Note that the calculations in this article are estimates and do not guarantee time savings or revenue.

"Distributed and Switched in 4 Months"—The Substance of the Decision note Inc. Made in April 2026

First, let's look at the timeline in three lines.

・December 2025: Distributed Cursor to all job roles. In parallel, planned an in-house training program, "AIDX Bootcamp," targeting 15 teams and 72 people, primarily non-engineers.

・April 2026: Immediately after starting the training, announced that the company-wide AI tool would be switched to Claude

.・End of June 2026: Training completed for all 15 scheduled teams.

What is noteworthy is the speed of decision-making. When they identified the issues with switching, organized them one by one with "this can be solved like this," and brought them to the CEO, they got the OK in about 10 minutes. It is written that it took about one week from the start of full-scale consideration to the internal announcement. Changing a tool distributed to the entire company is usually a matter that would cause six months of debate.

And this company, in the first place, makes its AI policy public.

This was posted in July by Takayuki Fukatsu, CXO of note Inc., and the content is that they published the basic policy for note's services and AI as an "open internal newsletter" (it has received reactions with over 50,000 impressions). AI-pulse-Japan editorial note: What should be picked up here is not "they can publish it because they are a large company." It is the order of writing the policy down first and showing it to both inside and outside the company. Even in a one-person company, if you write a single A4 sheet on "how we use AI," the judgment when changing tools does not have to start from zero every time. The foundation for the switch being decided in one week lies here.

4 Reasons for Deciding to Switch | "4-Question Switch Judgment" You Can Use As-Is

There were four deciding factors for the switch cited by note Inc. The table below reconstructs these into 4 questions for you to judge when choosing tools again. ★Main Data ①.

| # | Point note Inc. Looked At | Question to Ask Yourself | What Happens If Not Met |

| 1 | Form of Cost (Usage-based → Fixed) | Is it in a form where "the more you use it, the higher it gets"? | Users hesitate to try it = utilization stops |

| 2 | Learning Cost | Is the person you are distributing to an engineer? Is it becoming their tool? | The person teaching bears the entire burden of relearning |

| 3 | Are you on the side of setting the standard? | Is your company deciding the rules for connection (MCP = common standard for connecting AI and external services) yourself? | You end up on the side where peripheral tools are not available in a few years |

| 4 | Does it connect with current tools? | Does it connect to Gmail, Google Drive, and Slack by default? | Man-hours for in-house development of connections occur |

The first point was the most practical. With usage-based billing (paying only for what you use), people use it while thinking, "How much will this cost if I do this?" so they won't try it with confidence—that's why they switched to a fixed rate, the story goes. They choose based on "whether you can touch it without worrying" rather than cost fluctuations. Since the number of times you touch AI is directly linked to proficiency, this is effective.

The fourth point also looks like a matter of efficiency, but it is actually a matter of development costs. While Slack and Notion provided official ways to connect, the Google Workspace system had limited connection ranges, and note Inc. was developing it themselves. If that becomes standard support via a connector (connection function), that development becomes entirely unnecessary.

Which Tasks to Hand Over First | Deciding with AI Replacement 3-Axis × 5-Level Scale

Even if the tool is decided, everyone stops at the next step. "So, which task first?" Here, we combine the AI replacement 3-axis (Frequency × Time per instance × Error tolerance) that this media usually uses with the 5-level definition adopted by note Inc. ★Main Data ②.

The level definition is one that note Inc. explicitly stated they referred to from what DeNA has published, and it consists of the following 5 levels.

・Lv1: Have touched it

・Lv2: Use it as a daily tool (brainstorming, editing, research)

・Lv3: Changing work outcomes ← The level note Inc. set as the goal for all employees

・Lv4: Can build systems

・Lv5: Can elevate others

The 3 axes represent "is it worth raising?" and the level represents "what stage are you at now?" When combined, it looks like this.

| Task Example | Frequency | Time per Task | Error Tolerance | Current Level | Next Step |

| Aggregating inquiries/requests | High | Medium | High (can be fixed) | Lv2 | To Lv3 = Top Priority. Provide aggregation and draft improvement proposals |

| Checking mentions/posts about the company | High | Small | High | Lv1-2 | To Lv4. Run automatically at set times and receive only the results |

| Summarizing minutes/meetings | High | Medium | High | Lv2 | To Lv3. Quality becomes consistent just by providing one sample |

| Creating estimates/invoices | Medium | Small | Low (mistakes are accidents) | Lv1 | Leave human verification andstop at Lv2 |

| Final decision on contracts | Low | Large | Low | Lv1 | Do not raise. AI is for preliminary research only |

The best way to use this table is the bottom two rows. Deciding not to raise the level is also a valid decision. If you force automation on tasks with low frequency or zero tolerance for error, the preparation effort will cost more than the benefit. "Start with just one task. You stop because you try to do everything" is what this means.

Even in note Inc.'s preliminary survey, almost everyone was already using AI every day while the usage was almost the same for everyone—brainstorming, text editing, and research. In other words, everyone was stuck at Lv2. Changing it from a consultant to a "task performer." This is the real turning point.

1 hour of efficiency per week is 500 hours a year for 10 people | The truth behind "3,600 hours"

"So, if we do that, how many hours and how much money are we talking about?" note Inc. brings this to management meetings in this format.

72 people × 1 hour/week × approx. 50 weeks = 3,600 hours. As a calculation formula, it is surprisingly simple. Replacing this with your scale is the table below. ★Main Data ③. The amounts are estimates converted based on our media's standard hourly wage (general employee 2,000 yen/h).

| Number of People | 1 Hour/Week Efficiency | 2 Hours/Week Efficiency |

| 1 person (individual/side job) | 50 hours/year (approx. 100,000 yen) | 100 hours/year (approx. 200,000 yen) |

| 3 people | 150 hours/year (approx. 300,000 yen) | 300 hours/year (approx. 600,000 yen) |

| 10 people | 500 hours/year (approx. 1 million yen) | 1,000 hours/year (approx. 2 million yen) |

| 72 people (note Inc. scale) | 3,600 hours/year | 7,200 hours/year |

(Basis for standard hourly wage: Part-time/Arubaito 1,177 yen = national weighted average of regional minimum wage for fiscal year 2026, announced by the Ministry of Health, Labour and Welfare on September 3, 2026, effective sequentially from October 1 / General employee 2,000 yen, Manager 3,600 yen are estimates converted from annual salary)

1 hour per week is about 4.3 hours per month. In our media's time-saving score, it is ★3. It is not flashy. Because it is not flashy, it gets approved. "We will halve our work with AI" is doubted in management meetings, but "1 hour per week per person" cannot be denied by anyone. Furthermore, when you multiply it by the number of people, it becomes a calculation of 1 million yen per year even for a 10-person company. As a technique for getting proposals approved, this way of presenting it is worth imitating.

Conversely, this table also presents a cold fact to individuals. If it is 1 hour per week for one person, it is 50 hours/year, worth about 100,000 yen. If you stop after being satisfied with just "implementing it," those 50 hours will simply disappear. It is a figure that only becomes meaningful when you turn the saved time into the next job or sales.

5 Steps to Introducing AI in the Workplace | The sequence that moved 72 people, scaled down for 1 to 30 people

We have organized the sequence that note Inc. actually followed into a format that is independent of scale. If you are a one-person company, please read "employees" as "yourself".

STEP 1: Listen to the current situation before distributing tools.

Conduct a survey of all employees (usage frequency, use cases, work bottlenecks) and individual interviews with managers. We decide the training themes based on the content that emerges here. It was revealed that there was another reason for the interviews, which was to convey in advance that "we are on your side." It is based on the premise that it is natural to feel anxious when someone you don't know well says, "Improve your work with AI." Companies that fail at implementation usually skip this step and just hand out tools.

STEP 2: Decide on a single goal level.

note Inc.'s goal is clear: to raise all employees to Level 3 (where work results change). Instead of saying "Let's use AI," specify the level. Goals that cannot be measured will not be achieved.

STEP 3: Ask management for only three things.

1. Announce to the entire company in the management's own words that "we are starting this to achieve 100x results." 2. Issue an order to secure time to attend training. 3. Management themselves should also take the training and lead by example. Management is actually included in the target audience. If you are a one-person company, this is equivalent to "blocking out time on your own calendar."

STEP 4: Stop company-wide classroom lectures and do them by team.

We did basic training when introducing Cursor, but "people would listen to the explanation and think it was good, but return to their usual methods the next day." If it isn't tied to their own work, the priority drops the moment they return to their daily routine. That is why we remade the materials for each team, and initially, two people from within the company completed 10 sessions in two weeks, with each team having four 2-hour sessions. We didn't consider external instructors from the start—because if someone who doesn't know the company's work comes in, it will only be a superficial discussion.

STEP 5: Rebuild work flows with AI as the premise.

We position the time after the training as "this is where the real work begins." Instead of just carving out parts of the work to delegate, we have entered a stage of rebuilding the work itself into a form that is easy for AI to handle, and then separating it into full automation and semi-automation. The tasks that actually started moving at note Inc. included: aggregating requests from inquiries and writing them out in the form of improvement proposals (CS) / automatically checking for media coverage at set times on weekdays and delivering the results to Slack (PR) / and having Claude on Slack write correction code for UI improvement suggestions collected every other Friday (Design). In all cases, "human judgment" remains, and the manual work before and after that is eliminated.

Today's Step: A prompt to adapt these 5 steps for yourself

These are instructions to complete Steps 1 and 2 in one go. You can copy and use them as is.

You are in charge of AI promotion within the company. Read the following and output in the specified format.

【My Premise】

・Structure: (e.g., One person / Team of ◯ people / Company of ◯ people)

・Industry/Assigned Tasks:

・AI currently in use:

【My Task List】

1. (Task name / How many times per month / Approx. ◯ minutes per time)

【What I want you to output】

1. Score each task on a 5-point scale based on three criteria: "frequency, time required per session, and tolerance for errors"

2. Determine the current level of each task

Lv1 Have touched it / Lv2 Daily tool / Lv3 Work results have changed /

Lv4 Can build systems / Lv5 Can elevate others

3. The task that should be chosen as the "first one" and the reason why

4. Three materials I should provide to AI to raise that task by one level

(① Passing example ② Criteria for judging rework ③ Scope of what can be touched)

5. Conversely, "tasks that should not be raised" and the reason why

The goal is not the scoring itself, but to verbalize the "three materials to provide" in number 4. Once this is filled in, all that's left is to provide them.

This prompt will handle the scoring of tasks, but the answer to "which task should be raised in what order" to get the most relief will change depending on whether you are running it alone or have people, and whether it is contract work or in-house work. With one tap to add us on LINE, we will deliver your type assessment + a 7-day roadmap for replacement order (including an estimate of monthly time savings) for free (you can receive it in 30 seconds):

"I distributed it, but the numbers didn't change"—what happens after implementation

There is a continuation to this story. It is the sentiment that implementation is only the entrance.

In a July post by Kento Kajitani, who shares insights in the AI field, he writes: “I often receive consultations from executives saying, ‘We distributed Claude Code to our employees and productivity increased, but the business numbers haven’t changed. What should we do next?’” AI-pulse-Japan Editorial Note: This points to the exact same spot as note Inc.’s Step 5. Even if each individual becomes faster, if the handoffs between tasks remain human-driven, business numbers will not move. For an individual, this is the same phenomenon as “work got faster, but sales didn’t change.” Distribute → Enable use → Change the structure of the work. There are currently a massive number of companies that haven’t touched that third stage.

Furthermore, if you don’t decide on the boundaries for internal AI use (what is okay to input and what is not) beforehand, you will inevitably get stuck at Step 4. Digging deeper: Confidential Information Guidelines for Internal AI Use | Defining What Is Okay to Input

3 Landmines That Always Occur in the Field

From note Inc.’s article, I will extract three “stumbling blocks” that are effective to know when replicating their success.

① Resistance: “We finally got used to it.”

It is revealed that this naturally occurred during the switch. The countermeasure wasn’t just empty words, but creating migration tools and providing careful support. And what ultimately worked wasn’t persuasion, but the emergence of success stories in the field, which then created a chain reaction. They describe it as the names of the tools people mentioned changing naturally.

② The burden of relearning for those who teach.

The reason Cursor was difficult is that, in addition to the difficulty level being one step higher for non-engineers, since many internal engineers were already using Claude, they had to go out of their way to relearn it in order to teach others. When choosing tools, are you accounting for the cost of the people who teach, not just the people who use them?

③ People who can only give vague instructions cannot delegate to AI either.

“Garbage in, garbage out”—the point is that people who can break down work, organize it, and make requests with criteria attached are the ones using AI effectively. Managers who are used to delegating work to people are, as expected, good at this. In other words, half of AI training is training in organizing work. If you skip this and only teach how to operate the tool, you won’t rise above Level 2.

Summary: What you should change is not the tool, but how you hand off work

I will narrow down what to take away from this case study to three points. ① Even for tools distributed company-wide, it is fine to re-evaluate them based on four criteria (cost structure, learning cost, whether it is the standard, and whether it integrates). ② Set goals by stage (Lv3 = results change), not by “utilization.” ③ If one person saves one hour a week, that’s 500 hours/about 1 million yen per year for 10 people—it’s not a flashy number, which is why it gets approved.

And the most effective thing is the order. Listen to the current situation → Decide the stage → Secure time → Do it by team/task unit → Reassemble the work structure. Even if you change the tools, if you don’t follow this order, you will end up with “we distributed it, but the numbers didn’t change.” The idea that 30 minutes spent writing down three items to hand off is more effective than 3 hours spent trying out three new AI tools—this holds true whether the scale is 1 person or 72 people.

Which task should you start those 30 minutes with? This is where the most people get stuck.

With one tap to add us on LINE, we will deliver your type assessment and a 7-day roadmap for replacement for free (30 seconds, 1 tap):

If you found this article helpful, please give it a like and follow, as it encourages future updates. I continue to publish articles that translate public case studies from other companies into “how would this work at my scale?”

Next time, I will make Step 5, “Reassembling the Work Flow,” tangible—I plan to provide a boundary table for three tasks: inquiry response, meeting minutes, and invoice processing, showing what to automate and where human confirmation begins.

※ The facts and figures in this article (chronology, 15 teams/72 people, approval in about 10 minutes, estimates of approximately 3,600/7,200 hours per year, 5-level definitions, changes in the field) were verified on September 20, 2026, from the official note published by note Inc. on September 18, 2026, titled “Changing the Cursor Distributed Company-wide to Claude in 4 Months: note’s Company-wide AI Promotion Led by 2 Engineers.” It is stated that the level definitions refer to those published by DeNA in the same article. The “Questions to Ask Yourself,” “Next Move,” annual time/monetary conversions by number of people, 3-axis AI replacement, and time-saving scores in the tables are organized by this media as a guide and are not the views of note Inc. The base hourly wage of 1,177 yen is the national weighted average of the 2026 regional minimum wage announced by the Ministry of Health, Labour and Welfare on September 3, 2026, which will come into effect sequentially from October 1, 2026, depending on the prefecture. All estimates are guidelines and do not guarantee reduced time, sales, or profits. The content of the X posts is the personal opinion of the poster and has not been verified by this media. We deliver free content from this media via LINE.