Japanese Enterprise AI Agent Cases: Learning from Success and Failure
Analyzing AI adoption reality and challenges at major chains like Skylark, Saizeriya, and Sushiro.
"After we let AI handle phone calls, customer complaints skyrocketed."
This is what we heard from a representative of a major family restaurant chain in 2024. They had implemented the latest AI technology—so why did it fail?
We thoroughly investigated the cause. What was different from companies that were succeeding during the same period?
Success Story: Skylark's "Gradual Challenge"
Skylark Group deployed the serving robot "BellaBot" to 2,100 stores nationwide. But they didn't do it all at once.
First, they tested in just a few stores. They carefully observed staff reactions and customer reactions. "Kids love it when the robot comes." "Staff walking distance reduced by 30%."—Only after accumulating positive data did they roll out nationwide.
Failure Story: The Price of Neglecting "Language"
On the other hand, the chain that had to shut down its AI chatbot had a problem with "Japanese quality."
"Thank you for visiting us today."—Polite language that seems fine at first glance. But this expression is used after a visit. When the AI responded this way to a reservation inquiry, customers felt something was off.
Japanese keigo needs to be used differently depending on the situation. When using foreign-made AI as-is, these subtle nuances can be missed.
What We Learned
From this investigation, we gained three lessons.
First is "phased implementation." Don't roll out all features at once—expand while confirming effectiveness.
Second is "human collaboration." Have mechanisms ready to smoothly hand off to humans in situations AI can't handle.
Third is "commitment to language quality." Japanese keigo and dialect support in particular directly impacts customer satisfaction.
Successful companies treated AI as "a tool to nurture" rather than "a magic wand." That difference made the difference in results.