The Real Challenges Facing Customer Support Teams
According to a survey conducted by Altam Ease Co., Ltd., a significant number of the 200 customer support staff surveyed clearly recognize which tasks they want to delegate to AI. This finding reveals not just a vague “desire to adopt AI,” but specific, on-the-ground needs.
How should executives and CTOs interpret this data? Having supported IT adoption for over 38 clients, I’ve seen that AI in customer support often becomes an “end in itself.” However, ignoring frontline voices can actually decrease efficiency.
The survey highlighted a clear need for delineation—not simply “let AI handle everything,” but “which tasks should AI handle, and which should humans manage?” This perspective is the core insight for leaders.
The Direction Shown by a New Service for Retail and EC
Equally noteworthy is the launch of “CommerceX AI,” an AI talent and process improvement service specialized for the retail, e-commerce, and reuse industries. Its key feature is its industry-specific focus.
Generic AI adoption services offer broad functionality. But retail and EC have unique workflows like inventory management, returns processing, and coupon handling. Generic AI takes time to grasp these industry-specific nuances.
From my own experience using AI for legal negotiations in Malaysia, I can say that AI becomes more effective the more it’s specialized for a specific domain. With my own company’s case starting at a monthly cost of around 21,000 yen (approx. $140), industry-specific services significantly lower the barrier to initial adoption.
Three Functions Frontline Teams Want from AI
The Altam Ease survey reveals three main functions frontline staff expect from AI.
First is “automated responses to standard questions.” Common queries like “What are your business hours?” or “What are the return conditions?” can be easily handled by an AI chatbot. In fact, one of my clients reduced inquiry volume by about 40% by automating this area.
Second is “data analysis and trend identification.” AI can automatically analyze data such as “which products generate the most inquiries” or “what times have the highest inquiry volume,” enabling optimized staffing.
Third is “initial handling of complex inquiries.” This is a hybrid model where AI handles the first response, and humans do the final check. This reduces staff burden while maintaining customer satisfaction.
The Importance of Seeing Adoption as a Means, Not an End
As the theme of the generative AI seminar “Adoption is Not the Goal! Generative AI Use and the Future You’ll Want to Use Across Your Organization” suggests, AI adoption is a means, not an end.
In my 93 use cases that achieved approximately 1,550 hours of annual work reduction, the most successful were those where “AI was chosen based on frontline challenges.” Conversely, there were many cases where jumping on the latest tech led to poor adoption.
As a leader, focus on these three points:
First, “listen to the frontline.” Like the Altam Ease survey, understanding what your staff actually struggles with is the first step.
Second, “start small.” Instead of aiming for company-wide adoption, pilot in a specific area to minimize risk.
Third, “create a system for measuring impact.” You need a framework to quantitatively track changes in inquiry volume, response time, and customer satisfaction after adoption.
A Concrete Adoption Process
Based on my experience, here’s a process for considering actual adoption.
First, analyze your company’s inquiry data. Visualize what types of questions come in, how many, and how long responses take. AI can even make this analysis more efficient.
Next, separate tasks that can be automated from those that should remain human. For example, complaints and high-level negotiations stay with humans, while FAQ-level questions go to AI.
Finally, choose the right tools. Industry-specific services like CommerceX AI offer low barriers and quick results. Alternatively, if you need customization for your workflow, you can combine general-purpose AI like GPTs or Claude.
Management Benefits of AI-Powered Customer Support
AI in customer support creates value beyond simple cost reduction.
First, reducing staff burden can lower turnover rates. As the survey shows, frontline staff want to distinguish between tasks for AI and tasks for humans. Meeting this need boosts employee satisfaction.
Second, it standardizes response quality. AI can provide consistent quality 24/7. For e-commerce sites, handling inquiries at night or on holidays is a common challenge that AI can solve.
Third, it enables data accumulation and utilization. All AI-handled inquiries become data that can inform product development and marketing—something difficult with traditional human-only support.
Cost Estimates and ROI
Let me give you a concrete sense of costs. Industry-specific services like CommerceX AI are often available for a few thousand yen per month (approx. tens of dollars). On the other hand, building a custom system with GPTs or Claude might require an initial development cost of several hundred thousand yen (approx. a few thousand dollars) and monthly operating costs of a few thousand to tens of thousands of yen (approx. tens to hundreds of dollars).
In my own company’s case, a monthly cost of about 21,000 yen (approx. $140) generates value equivalent to about 7,533,000 yen (approx. $50,000) annually. For customer support specifically, most cases see a return on investment within three months of adoption.
Summary: Frontline-Driven AI Adoption is the Key to Success
What emerges from this news is that the key to successful AI adoption lies in accurately capturing frontline needs. The Altam Ease survey provides concrete data on this.
My suggestion to executives is to start by interviewing your own customer support team. Simply asking “What tasks are you struggling with?” and “What would you like AI to handle?” will clarify the direction for AI adoption.
Then, I recommend starting small with industry-specific services or a combination of general-purpose AI. Don’t aim for a perfect system; start by solving frontline problems. That’s the first step toward sustainable AI use.
AI adoption is not the goal. It’s a means to solve frontline challenges and grow your business. Keep that perspective, and take it one step at a time.
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