What to Consider Before Arguing Over “ChatGPT vs. Claude”
“Should we go with ChatGPT or Claude?” I’ve been getting this question more often from business owners and CTOs lately. Both companies are releasing high-performance AI models one after another, so it’s natural to feel overwhelmed by the options.
However, as shown in a case where a major broadcasting company implemented generative AI and RAG consulting, successful companies start the discussion not with “which AI to use,” but with “what problem do we want to solve?” In this article, I’ll explain five key points for SMEs to avoid failure in AI investment, based on insights gained from my own experience with 93 AI use cases.
The Common Misunderstanding About “AI Selection” That Leads to Failure
The Danger of Getting Stuck in Tool Comparisons
You’ll find plenty of information out there comparing tools: “ChatGPT is good at Japanese,” “Claude excels at long-form text processing.” But companies that get stuck in these kinds of discussions almost always fail in their AI investments. Why? Because the pros and cons of an AI tool only matter once you’ve decided “what you’re going to use it for.”
For example, if your work is specialized in contract review, Claude, with its strength in long-form text, would be a good fit. On the other hand, for first-line customer support, ChatGPT, which offers easy API integration, might be more advantageous. In other words, comparing tools without considering business requirements is like choosing a car model without knowing your destination.
The Negative Spiral Created by “Just Implementing It”
Among the companies I’ve visited for consulting, quite a few had implemented ChatGPT company-wide on a whim, driven by a top executive’s decision. What was the result? Employee usage rates were under 10%, and the tool became a “dead asset” that only generated monthly license fees.
The root cause of this failure was the lack of a design phase before implementation—specifically, deciding “which tasks to delegate to AI.” AI is not a magic box. Without clear use cases, even the most powerful tool is a waste.
Five Practices of Successful Companies
1. Work Backwards from Your “Pain Points”
The first step is to take stock of your company’s business processes and identify challenges like “inefficient for humans to do,” “over-reliance on specific individuals,” or “prone to errors.”
When I introduced AI to streamline legal work, I first listed specific issues: “It takes two hours to review each contract,” and “Manual background checks are prone to oversights.” Only after that did I consider “which AI tool is suitable,” ultimately deciding to use both Claude and ChatGPT together.
2. Start Small and Visualize the Results
There’s no need to roll out AI company-wide all at once. Start with a pilot in one department or for one task, and measure the results quantitatively.
In my case, I began with a single task: automated social media posting. After confirming that monthly posting time was reduced from 15 hours to 2 hours, I gradually expanded to WordPress article generation, contract review, and FX trading. This “accumulation of small successes” is the fastest way to gain internal understanding and cooperation.
3. Calculate Cost-Effectiveness Rigorously
The monthly cost of AI tools is generally around $15–$25 per user. However, it’s important to evaluate the benefits not only in terms of “time saved” but also “quality improvement” and “risk reduction.”
In my case, with a monthly AI-related cost of about $150, I’m generating value equivalent to approximately $53,000 per year. This figure includes not just time savings but also qualitative effects like reducing reliance on specific individuals and decreasing errors. Before implementation, conduct an “ROI simulation” to clarify the basis for your investment decision.
4. Data Preparation is 80% of the Work
AI performance heavily depends on the quality of the data you feed it. Especially when using RAG (Retrieval-Augmented Generation), it’s essential to organize your internal knowledge base and past case data.
In the aforementioned broadcasting company case, a significant amount of time was spent in the early stages of RAG consulting on designing “which data to train the AI on.” Neglecting this preparation can lead to “hallucinations,” where the AI generates incorrect answers, actually hindering work.
5. Redefine the Role of Humans
What’s most often overlooked in AI implementation is the change in the human role. While AI takes over routine tasks, humans are expected to take on higher-level roles like “verifying AI output,” “handling exceptions,” and “making strategic decisions.”
In my team, we have a rule that a human must always do the final check on AI-generated contract review results. The understanding that AI is merely an “assistant” and that humans bear ultimate responsibility is the foundation for safe and effective AI use.
Concrete Steps to Overcome Implementation Hurdles
The Real Cost Picture
What SMEs worry about is, of course, cost. In my experience, if you want to keep initial investment low, I recommend starting with a free plan first. Even the free version of ChatGPT can adequately handle basic tasks like writing and summarization.
After that, while confirming the effects, it’s realistic to move to a paid plan costing $15–$25 per month. The annual license fee per person is about $150–$250. If this can save tens of hours of work per year, the return on investment is more than sufficient.
Preparing Your Skill Set
You don’t need special engineering skills to implement AI. What’s important is the ability to design “what to delegate to AI” and the judgment to “evaluate AI output.”
I myself don’t have specialized programming knowledge, but I’ve been able to build an automated system using AI APIs in-house. Recent AI tools can generate code from natural language instructions, making them fully usable even for non-engineers.
Summary: Business Design Matters More Than AI Selection
The debate over “ChatGPT vs. Claude” distracts from the essence of AI implementation. What truly matters is identifying your company’s business challenges and designing the optimal way to use AI to address them.
From my 93 AI use cases, I can say that the key to success lies not in tool selection, but in preparation before implementation and operational design after implementation. By putting the five points in this article into practice, even SMEs can maximize the effectiveness of their AI investments.
Start by writing down your company’s “pain points” on paper. That’s where the first step of AI utilization begins.
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