The 'Lack of Experts' Problem Hindering AI Adoption: 2 Steps SMEs Can Take Right Now

As of March 2026, the percentage of small and medium-sized enterprises (SMEs) in Japan incorporating AI into their operations is only about 5–10% (data from the Ministry of Internal Affairs and Communications). In contrast, about half of large corporations have already implemented it. When I first saw these figures, I was honestly surprised at how low they were.

In my daily conversations with various business owners, I often hear things like, 'AI is ultimately for big companies, isn't it?' or 'We don't have a dedicated IT person...' However, I believe that the idea that 'it's impossible because we don't have an expert' is likely a major misconception. Today, I would like to write frankly about that.

Why can't SMEs adopt AI? The 'real reason' shown by the data

According to a survey conducted by Tokyo Shoko Research (covering 6,645 companies), the percentage of companies that responded they are 'promoting the use of generative AI (technology that automatically generates text and images)' as a company is 25.2% for large corporations, whereas for SMEs it is only 12.7%. The difference is 12.5 percentage points.

Furthermore, the percentage of SMEs that answered they 'have not decided on a policy' reaches as high as 52.4%. In other words, more than half of SMEs are in a state where they haven't even made the decision of whether to 'do it or not.'

[Top 3 Reasons SMEs Do Not Adopt AI]
・ 'There is no expert personnel to promote it'… 55.1% (most common)
・ 'Cannot evaluate the advantages and disadvantages of usage'… 43.8%
・ 'Do not know where to start'… (multiple answers)

Source: Tokyo Shoko Research 'Corporate Survey on Generative AI Usage'. Looking at the numbers, 'no expert personnel' is the runaway number one. However, I feel a bit of discomfort here. AI tools, especially generative AI like ChatGPT, can be used by anyone on a smartphone or computer without programming knowledge. Why, then, is the perception that 'you can't use it without an expert' so widespread?

The answer is simple. It is because 'there is no opportunity to be taught how to use it'.

Creating a 'state where it can be used without experts' through AI training

Let's look at an example where productivity actually improved.

At a service company with 35 employees, they incorporated AI into their internal chat tool, and AI became able to automatically handle over 60% of daily inquiries from employees. This was the result of employees receiving training on how to use AI and expanding the scope of its application while actually testing it in the field (Source: Smilion IT Blog).

At another SME, by learning how to have ChatGPT create draft structures based on past proposal materials, they reduced the time to create proposal materials by about one-third. The number of business negotiations also increased by 20% per month.

What these cases have in common is that they did not 'hire high-level IT experts.' Existing employees learned how to use it through training and incorporated it into their work.

[Benefits of Introducing AI Training]
・ Can make existing staff immediately effective through upskilling
・ Costs are overwhelmingly lower than hiring external talent
・ Develops 'talent' within the company who can master AI
・ Faster speed of integration into operations (training → practice can be done immediately)

Under the Ministry of Health, Labour and Welfare's reskilling support project for SMEs, there is also a system where up to 75% of AI training costs can be covered by the Human Resources Development Support Grant.

'Organizing work first' is the first step in AI utilization

Here, I would like to share something I strongly feel in the field.

Even after receiving AI training and thinking, 'I know how to use it!', many companies actually hit a wall, asking, 'So, which tasks should we use AI for?' The reason for this is clear: the company's own operations are not organized.

What kind of work is being done by whom and how much time is it taking? Which tasks are routine tasks that occur repeatedly? If such 'visualization of work' is not done, even if you introduce AI, you will end up in a state of 'not knowing what to use it for.'

In your company, are there tasks that anyone could do, yet only the person in charge understands how to perform them?

In reality, this is not just a barrier to AI adoption; it is also a barrier to talent development and recruitment. When tasks become personalized, new employees cannot be trained, and if a veteran leaves, the organization stops functioning. Furthermore, when you try to automate these personalized tasks with AI, a structural problem arises: you cannot give instructions to the AI because the content of the work has not been organized.

That is why the first thing you should do before adopting AI isto inventory and organize your business processes. Clarify who is doing what work and where efficiency can be improved. After that, provide AI training and apply it to actual tasks. This order is crucial.

Conversely, by proceeding with business organization and AI training as a set, the effectiveness of AI adoption will increase significantly. The goal is not to introduce tools, but to achieve results such as 'making work easier, faster, and reducing errors.'

The difference in AI adoption is made starting from this very moment.

The fact that the AI adoption rate for SMEs is 5-10% also means that 'if you start now, you can still get ahead.' Now, while many competitors are stuck without having decided on a policy, is it not the perfect time to make a move by starting with training?

It is not a matter of 'it's impossible without experts,' but rather 'you can start once employees become proficient through training.' And 'to make full use of AI, first organize your business processes.' I believe these two steps are the realistic path to AI adoption.

Do not overthink it; start by choosing one 'simple, repetitive task within your current operations' and try to see if AI can be used for it.

ShakeHands LLC

A business partner that organizes labor shortages from the structure up.
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