Real-World AI Utilization and DX for SMEs: A Scene-by-Scene Guide to Helping Those Wearing Multiple Hats

Table of Contents

1. Introduction: The "Wearing Many Hats" Problem and the Reality of DX for SMEs

DX (Digital Transformation) concept illustration

In small and medium-sized enterprises (SMEs), it's common for one person to handle multiple roles such as sales, general affairs, and accounting, constantly facing a shortage of time. While flashy examples of "dramatic business changes through DX (Digital Transformation) and AI utilization" or "work finishing with system overhauls" are often discussed, the reality is often, "I don't even have time to think of prompts to give to AI," or "setting up a new DX system is a hassle in the first place." Isn't that the truth?

Warning: DX and AI are not "magic wands"
Expecting 100% perfect results by fully delegating work to tools, or trying to introduce a company-wide DX system all at once, will only increase the burden on employees and the effort for corrections, leading to frustration. It's crucial to view these as merely "competent stationery."

This article introduces practical ways to use AI tools not for a dream-like full automation, but to "accelerate the initial phase of tedious tasks" and "drastically simplify manual data entry."

Even without multi-million dollar DX investments or dedicated IT teams like large corporations, SMEs can gradually create an environment where they can concentrate on core tasks by letting AI take over "daily tedious small tasks."

2. Why SMEs Need "Down-to-Earth AI Utilization and DX Promotion"

For SMEs, suddenly introducing advanced and expensive DX systems carries risks. "Down-to-earth AI utilization" that matches the company's scale is recommended for the following reasons:

Reason 1: To reduce "switching costs" for combined tasks

You might be creating sales materials, then suddenly find yourself responding to general affairs inquiries. AI can help reduce the "uh, what was I supposed to do first?" recall time and the psychological barrier of starting to write from scratch when switching tasks, by acting as a "sounding board."

Reason 2: Because a "70-point draft" is sufficient, not perfect automation

Instead of rewriting instructions multiple times to get 100% perfect results from AI, it's significantly faster in practice to have AI produce a 70-point draft in one minute and then have a human refine the remaining 30 points.

Reason 3: The agility to try small and stop if it doesn't fit

By using cloud-based AI tools, departmental test operations are possible for a few thousand yen per month or even for free (Reference: Current Status of DX Promotion in the White Paper on Small and Medium Enterprises). The ability to "stop if it doesn't work" is one of the greatest strengths of SMEs.

3. A Scene-by-Scene Guide to Truly Useful DX Tools on the Ground

Scene 1: Reducing "Where Is That?" with an Internal FAQ (e.g., Dify)

One of the challenges for SMEs is the abundance of person-dependent information that can only be found by asking the specific person, such as "How do I apply for paid leave?" or "Where are past similar quotes?" This is where RAG (Retrieval-Augmented Generation) tools like Dify excel.

Realistic Usage
It's impossible to make all internal documents perfectly searchable from the start. The secret to success is to first narrow down to specific areas like "employment regulations" or "frequently asked accounting questions (Q&A list)," upload PDFs or text, and create a simple chatbot.

For improving RAG accuracy, please refer to the following related article:
Reference article: Article 64: Alternative Approaches to Improving Dify's RAG Accuracy

Scene 2: Connecting "Copy-Pasting" Across Multiple Tools (e.g., Make / Zapier)

Copying and pasting the content of a website inquiry form into a spreadsheet, and then notifying the person in charge via chat. These simple daily tasks can be semi-automated with integration tools called iPaaS.

Example of a Realistic Workflow
1. Customer submits a Google Form
2. Make (or Zapier) detects the submission
3. Automatically adds a row to the specified Google Sheet
4. Automatically notifies Slack or Chatwork with "New inquiry received"

Open-source n8n is powerful and free, but has a somewhat higher learning curve for non-engineers. Starting with the free tiers of intuitive tools like Make or Zapier is a realistic option.

Scene 3: Research, Data Collection, and Creating Draft Documents (Various AI Chats)

When there's no time to research industry trends or outline a proposal for a new client from scratch, conversational AI tools like ChatGPT, Claude, and Genspark are highly effective.

Especially with AI tools linked to search engines, simply typing "Tell me the latest trends in the XX industry in bullet points" will organize information with links to the sources.

It is strictly forbidden to present AI-generated research results or document outlines directly to customers. Always perform fact-checking by a human and add your company's unique strengths and concrete figures, based on a "collaborative process."

For more on how to use various AI tools, please refer to the following articles:
Reference article: Article 45: Genspark Basic Features Complete Guide
Reference article: Article 05: Development Workflow Using Tools

Scene 4: Digitizing Handwritten Notes and Receipts Instantly with a Smartphone

In the field of SMEs, "paper" is still prevalent. The task of manually entering handwritten daily reports and receipts into Excel later is a significant burden. Building a system for "API integration and automation tools for DX..." is one approach, but many people find the setup "bothersome."

Ultra-Simple "Micro DX" Using Smartphones and Image Recognition AI
The easiest way is to take photos of receipts or handwritten notes with your smartphone and upload them directly to a multimodal AI chat (e.g., ChatGPT, Claude). Simply instruct, "Extract the date, amount, and purchased items from this receipt image and output them as comma-separated text (CSV format)," and the AI will instantly perform the tedious transcription.

Then, all you have to do is copy and paste the output text into Excel or a spreadsheet. Even without large-scale "system integration," simply using an AI at hand as a "high-performance data extraction assistant" can dramatically reduce manual entry time. This is truly DX you can start tomorrow.

4. 3 Steps for Successful Implementation

The following are 3 realistic steps for establishing DX and automation tools within your company:

  1. List your "tedious" tasks
    Instead of jumping straight into tools, first list "simple tasks that take more than 30 minutes each week" or "psychologically burdensome tasks."
  2. Try "just a part" using free tiers or trials
    Do not aim for company-wide implementation; instead, start with test operations using a free plan, either alone or with a small team (e.g., Article 20: Creating Common Automation Tools).
  3. Define "roles" between humans and AI
    Clearly draw the line: AI for "research, drafting, and data extraction," and humans for "final confirmation, decision-making, and building customer relationships."

5. Practical Examples (Realistic Success Stories)

Case Study: General Affairs Manager at a 15-employee Manufacturing Company
Before: Daily calls from the factory asking, "How many paid leave days do I have left?" or "Is this expense reimbursable?", constantly interrupting personal work. Also, several hours each month were spent transcribing handwritten supply purchase notes submitted from the factory.

Action: 1. Created a "General Affairs FAQ Bot" with Dify, making it accessible from factory smartphones.
2. Changed the process for large numbers of "handwritten receipts" and "notes" sent from the factory to uploading photos to an AI chat, converting them into tabular data in bulk, and then copying and pasting into Excel.

After: While inquiries didn't completely disappear, routine questions were reduced by roughly half, and the time spent on manual data entry for month-end transcription tasks was significantly reduced. The biggest gain was the reduction in psychological "interruption stress."
It's not about building magnificent systems, but about accumulating small successes like "one less tedious task" or "saved the hassle of transcription." This is truly the first step for DX and AI utilization in SMEs.

6. Conclusion: On-the-Ground Business Improvement is True DX

AI tools are not "magic for reducing staff" but "support tools for allowing existing talented personnel to concentrate on their core work." Instead of aiming for exaggerated full automation, try starting by letting AI take over the "small inconveniences" of daily tasks.

By using different tools for different scenarios, from digitizing handwritten documents to creating document drafts, you should be able to achieve rapid business improvements precisely because you have a small team. Take the first step with "on-the-ground DX" that doesn't overreach.