20.7 Billion Yen Spent, Yet Slides Remain Unchanged

I want to ask.

What exactly did a company spend 20.7 billion yen (approximately 200 million dollars) on when its employees’ slide decks still aren’t decent?

A major corporation in the United States invested a huge sum in a company-wide AI implementation. Generative AI, internal chatbots, data analytics infrastructure—a lineup that sounds impressive when translated into katakana. However, the feedback from the field was that “the quality of the slides hasn’t changed” and “we’re still creating them the same way as before.”

This is no laughing matter. It’s a structural issue.

Imagine the breakdown of that 20.7 billion yen. Licensing fees, infrastructure development, consulting, personnel costs for internal promotion teams. All of these are costs associated with “implementing AI,” not costs associated with “what to change with AI.”

The moment implementation becomes the goal, the investment amount and results no longer correlate. In fact, they could even be inversely correlated. The larger the organization, the greater the adjustment costs, leading to the mass production of tools that no one uses under the banner of “company-wide implementation.”

An AI Calendar That Eliminated Five Apps for 1,500 Yen a Month

On the other hand, there is a completely different scenario.

The AI calendar “AllTime” costs about 1,500 yen per month. What it does is simple. Calendar, task management, reminders, scheduling, prioritization—all the tasks that previously required switching between five apps are now integrated into one interface.

The key point is that “AI automatically optimizes the schedule.” When you input the required time and deadlines for tasks, it automatically blocks out available time slots. If a meeting changes, the tasks adjust accordingly. Reminders are sent automatically.

For someone who was using five apps, switching to just one leads to significant changes:

• No more transferring information between apps
• No more wondering, “Where did I write that task?”
• No more back-and-forth for scheduling adjustments

Suppose you were spending 30 minutes a day on this “app switching and transferring.” That amounts to 10 hours over 20 business days in a month. For someone earning 2,000 yen per hour, that’s 20,000 yen worth of labor. With an investment of 1,500 yen a month, you save 20,000 yen in time. The ROI is approximately 13 times.

Consider this figure in comparison to the 20.7 billion yen investment.

The One Question That Distinguishes Success from Failure

What separates successful AI investments from unsuccessful ones is neither the amount of money nor the sophistication of the technology.

“Whose work is being reduced, and by how much?”

Whether you can answer this question concretely is all that matters.

The 20.7 billion yen company aimed to “implement AI across the organization.” AllTime aimed to “reduce the hassle of schedule management by consolidating five apps into one.”

The former has made the means the end. The latter has worked backward from the problem.

This structure can be replicated regardless of company size. Even small businesses that stop at “let’s just get a corporate contract for ChatGPT” will make the same mistake. Conversely, large companies that narrow down to “automating this process in this department” will see results.

The Concept of “Intelligence Impact Quotient”

Recently, a metric called “Intelligence Impact Quotient (IIQ)” has begun to be proposed for measuring the effectiveness of AI investments.

Simply put, it’s an attempt to quantify “how much AI is integrated into operations.”

There are three components:

1. Usage Frequency — How many times per week and how many people are using it
2. Task Complexity — Is it simple search assistance or support for decision-making?
3. Organizational Leverage — How much does one person’s usage affect the work of others?

For example, if one accountant automates invoice processing with AI, reducing a monthly workload of 40 hours to 5 hours, the usage frequency is daily, the task complexity is moderate, and the organizational leverage extends to the entire accounting department—resulting in a high IIQ.

Conversely, if an AI chatbot is distributed to all employees but only used once a month, and even then just to ask about the weather—its IIQ is nearly zero.

A company with numerous departments where the IIQ is close to zero after a 20.7 billion yen investment, versus a sole proprietor whose IIQ skyrocketed with an investment of 1,500 yen a month. It’s clear which one is “succeeding in AI utilization.”

The Real Opportunity for Small and Medium Enterprises

Now, let’s get to the main point.

This structure presents an overwhelming advantage for small and medium enterprises.

Why? There are three reasons.

1. Faster Decision-Making

While it takes a large corporation six months to approve a 20.7 billion yen budget, a small business can start testing a 1,500 yen tool today. If it doesn’t work, they can stop next month. The speed of this “trial → judgment → adjustment” cycle becomes a decisive advantage in AI utilization.

2. Specific Challenges

Rather than abstract themes like “company-wide DX,” challenges are specific: “It takes three days to process invoices at the end of the month,” “We create estimates from scratch every time,” “We manually compile daily reports.” Because the challenges are concrete, the application of AI becomes clear.

3. Visible Effects

In a company with 30 employees, if one person’s workload is reduced by 10 hours a month, everyone will notice. “That person is going home early lately,” “That task is no longer being done.” Because the effects are visible, horizontal deployment is quick. In a large corporation, if one person’s workload improves, no one notices.

“So, what should we do?”

There are three specific actions to take.

① First, create a “List of Tedious Tasks”

Ask your team to list five tasks they find tedious every time. This will become the candidate list for AI implementation. Technical discussions can come later. First, articulate the challenges.

② Start with tools that can be tested for under 5,000 yen

Calendar tools like AllTime, the Team plan for ChatGPT (approximately 3,000 yen per person per month), automated meeting minutes tools—there are plenty of options that can be tested for a few thousand yen a month. There’s no need to make a large investment from the start.

③ Measure only “how many hours were reduced”

You don’t need to complicate the effectiveness measurement. “How many hours did this task take before?” “How long does it take now?” Just record that difference. As it accumulates, it will provide the basis for future investment decisions.

AI Investment Is Determined by the “Quality of the Questions,” Not the Amount

20.7 billion yen versus 1,500 yen. The difference in amount is about 13.8 million times. However, the difference in results does not correlate with the amount.

What made the difference was not the investment amount but the quality of the “questions.”

“How can we implement AI across the organization?”—this is a question about means.
“How can we complete this task in half the time?”—this is a question about challenges.

Only organizations that have the latter question will achieve results from AI investments. And those organizations are often small and medium enterprises where the distance between the field and management is close.

You don’t need 20.7 billion yen. What you need is a specific question about “what you want to eliminate” and a willingness to allow experiments costing a few thousand yen a month.

First, think of one task from today that made you wonder, “Isn’t this something that humans shouldn’t have to do anymore?”

That’s your starting point.