AI Operators Are More Than Just a Tool for Reducing Call Center Labor Costs

When people hear "AI operator," many think of it as something that automates phone support.

That is not wrong. However, viewing it only in that way is a bit shallow.

The essence lies not in replacing phone support, but in redesigning the customer touchpoint itself.

Traditional call centers have been plagued by issues such as labor shortages, wait times, limited operating hours, training costs, and inconsistent service quality.

Calls don't get through.
Support is unavailable outside business hours.
Customers are forced to explain their inquiries repeatedly.
The quality of answers varies depending on the representative.
In the end, they are just transferred to another department.

These experiences are highly stressful for customers.

On the other hand, companies are also struggling.

Hiring operators is difficult.
Training takes time.
The number of inquiries does not decrease.
As products and services increase, there is more to learn.
Staff are tied up with simple inquiries and cannot focus on the complex cases that actually require their attention.

This is where the significance of an AI operator comes in.

What makes it different from traditional chatbots?

Traditional chatbots and voice bots were basically scenario-based.

If asked A, answer B.
If asked C, answer D.

They function as long as the interaction follows a pre-built flow.

However, customer questions are not that neat.

They misspeak.
They ask different questions in the middle of a conversation.
They leave out essential background information.
They are struggling while looking at a screen.
They haven't even organized what they are having trouble with themselves.

In these situations, traditional bots are weak.

"I don't understand."
"Please select the relevant question."
"I will connect you to an operator."

In the end, it goes back to a human.

Rather than automation, this just adds extra effort for the customer.

On the other hand, an AI operator using generative AI can converse while understanding the customer's context.

They can handle changes in questions mid-conversation.
They can ask for clarification if information was missed.
They can provide more specific guidance by integrating with the screens the customer is viewing and error information.
They can hand over to a human if necessary.

It is not just a simple Q&A, but a functional conversation.

This is a major difference.

AI is not just inferior to humans; there are situations where it is superior.

The term 'AI operator' gives the impression of being a replacement for humans.

However, in reality, there are areas where it excels over humans.

For example, in cases where guidance is provided for procedures while looking at a web screen.

Human operators cannot directly see which screen the customer is currently looking at or what they are entering and where.

Therefore, they have no choice but to ask questions like these.

"Which screen are you on now?"
"How far have you progressed?"
"What kind of error is appearing?"
"Could you please check your input?"

This takes time.

However, if AI can integrate with web behavior data and error information, it can grasp where the customer is stuck.

For example, an error occurs because the address number was entered in half-width characters on the address change screen.

If the AI can see this information, it can guide the user by saying,
"Isn't the address number field in half-width? Please enter it in full-width characters."
and provide guidance.

This becomes support that exceeds that of a human.

Humans are good at empathy and judgment.
AI is good at processing large amounts of information, referencing logs, grasping screen states, and providing 24-hour support.

This division of roles becomes important.

The value of introducing AI operators lies in 24-hour support

The easily understood value of an AI operator is the ability to provide support 24 hours a day, 365 days a year.

Current corporate service desks have short operating hours.

Phones are only answered during weekday business hours.
You cannot call while at work.
Lines are busy when you call during your lunch break.
Reception is already closed by the evening.

At this point, the customer experience is quite poor.

With an AI operator, you can provide support even outside of business hours.

Address changes.
Setting changes.
Checking maturity settings for fixed deposits.
Product explanations.
Frequently asked questions.
Guidance on procedures.

There is a high possibility that these can be handled without human intervention.

From the customer's perspective, they can ask whenever it comes to mind.

From the company's perspective, missed opportunities are reduced.

This is not just cost reduction, but also the reduction of opportunity loss.

AI contact centers are not finished once they are built.

However, there is a major pitfall with AI operators.

That is, if you build them and leave them, they will deteriorate.

Generative AI cannot answer perfectly from the start.

Knowledge is insufficient.
Manuals are outdated.
Prompts become complex.
Fixing one part causes the accuracy of other answers to drop.
Operations become dependent on specific individuals.
No one can improve it.

When this happens, the AI operator becomes unusable in the field.

It looks good in a demo.
However, in production, there are too many types of inquiries.
There are many exceptions.
Customers express themselves in various ways.
Laws and internal rules are also involved.

Therefore, what an AI contact center needs is a mechanism for self-improvement.

Collect conversation logs that were not answered well.
The AI evaluates the reasons for failure.
Determine whether it is a lack of knowledge or a prompt issue.
Create improvement proposals.
Check if the accuracy of other answers has dropped after the improvements.
If there are no issues, implement the changes.

Without this improvement loop, it cannot withstand production operations.

Do not separate knowledge for humans and knowledge for AI.

A troublesome aspect of transitioning to an AI contact center is knowledge management.

Contact centers have manuals, FAQs, and talk scripts for operators.

However, simply having an AI read them does not mean they can be used effectively.

Knowledge intended for humans often contains images, tables, supplementary information, exceptions, and implicit assumptions. Even if it is easy for humans to understand, it can be difficult for AI to interpret.

Therefore, separate knowledge is created for AI.

This seems good at first glance, but it leads to problems.

Knowledge for humans. Knowledge for AI. Knowledge for FAQs. Knowledge for chatbots.

When these are managed separately, update costs explode.

Every time there is a product change, multiple knowledge bases must be updated. It becomes unclear which one is the latest. Human responses and AI responses diverge. Operations become burdensome.

That is why centralized knowledge management is essential.

Create a state where both humans and AI can refer to the same correct information and convert it into the necessary format.

If this is not designed properly, the operational burden will actually increase despite the shift to AI.

Japanese 'omotenashi' (hospitality) is a collection of tacit knowledge.

Japanese contact centers have high quality requirements.

Especially in industries with high public interest such as finance, insurance, telecommunications, and healthcare, accuracy in responses alone is not enough.

Politeness. Choice of words. How to confirm information. How to alleviate anxiety. Consideration for the elderly. Tone during complaints. Legal accountability.

These are the things that are required.

This is not just a simple FAQ.

It is the tacit knowledge that veteran operators and supervisors have been teaching newcomers on the front lines.

'Phrasing it this way makes the customer anxious.' 'In this case, express gratitude first.' 'It is better to guide elderly people in this order.' 'Connect this inquiry to a person quickly.' 'Do not be definitive here; use confirming expressions.'

This kind of detailed wisdom is what creates service quality.

The truly difficult part of an AI contact center is how to turn this tacit knowledge into explicit knowledge.

AI has become quite capable of simply returning the correct answer.

However, to meet the quality standards of Japanese customer service, it is necessary to cultivate the way those answers are delivered.

Cultivating AI and humans in the same operational loop

The ideal form of an AI contact center is not one where humans and AI are operated separately.

Human operators handle inquiries.
AI also handles inquiries.
Each conversation log is accumulated.
Successful responses become knowledge.
Failed responses become material for improvement.
Supervisor guidance is also reflected in the AI.
Human manuals are also improved based on AI failures.

It is important for humans and AI to be in the same learning loop in this way.

It is not just the AI that evolves.
It is not just the humans who are trained.
The entire contact center learns.

Once you reach this point, an AI contact center is no longer just an automated response system.

It becomes a mechanism where corporate knowledge is accumulated through customer interactions.

Making VOC usable for business growth

The true value of an AI contact center is its ability to turn the voice of the customer into data.

Until now, conversations with customers were lost within individual operators.

What are they struggling with regarding this product?
Where are they dropping off?
What are they dissatisfied with?
What kind of language conveys the message effectively?
What are the signs before cancellation?
Where are the questions concentrated regarding new products?

This type of information is inherently of very high value.

However, it has not been sufficiently structured in the past.

When you build a contact center centered around AI operators, conversation logs are accumulated.

If you analyze the accumulated conversations, you can see the customers' pain points.

Feed inquiries about new products back to product development.
Feed reasons for cancellation back to sales and customer success.
Feed common complaints back to UI improvements.
Feed procedures with high inquiry volumes back to web improvements.
Utilize customer language in marketing.

This is what becomes possible.

In other words, an AI contact center is not a cost center, but a source of business improvement information.

Customer support is moving closer to the center of business growth.

Until now, customer support has often been viewed as a defensive department.

Handling inquiries.
Receiving complaints.
Reducing customer dissatisfaction.
Lowering costs.

However, as the transition to AI contact centers progresses, their role will change.

Increasing customer touchpoints.
Gathering honest customer feedback.
Providing feedback for product improvement.
Detecting signs of churn.
Identifying opportunities for upselling and cross-selling.
Passing information to marketing and sales.

In short, CS will become involved in business growth.

This is a significant change.

For a company, the voice of the customer is the most important data.

The contact center may actually hold the largest amount of that data.

Companies that do not adopt it will lose customer touchpoints.

Companies that do not implement AI operators or AI contact centers will not just miss out on cost-cutting opportunities.

They will lose customer touchpoints.

Phones not connecting.
Limited hours for inquiries.
Inability to resolve issues via FAQ.
Cumbersome procedures.
Being kept waiting.
Being forced to explain things repeatedly.

If these experiences continue, customers will leave.

Conversely, if companies emerge that offer 24/7 support and can resolve issues on the spot to some extent, the standard for customer experience will change.

Until now, it was accepted that "it can't be helped if the phone doesn't connect."

From now on, it will be "why can't you respond immediately?"

Customer expectations will change.

Companies that cannot adapt to that change will quietly find themselves at a disadvantage.

AI operators should be thought of in terms of expansion, not reduction

AI operators are not just for reducing headcount.

Of course, there is a cost-reduction effect.

If AI handles general inquiries, the number of cases handled by humans will decrease.
Support outside of business hours will also become possible.
The accuracy of inquiry routing will improve.
The burden of operator training will also decrease.

However, that alone is a waste.

The essence of an AI operator lies in the expansion of customer touchpoints.

Capturing voices that could not be picked up before.
Capturing needs outside of business hours.
Capturing consultations from people who were hesitant to call.
Capturing small signs of dissatisfaction before cancellation.
Capturing voices that lead to product improvements.

When this is possible, an AI contact center is no longer just an efficiency tool.

It becomes infrastructure for growing the business.

Future call centers will become the intelligence of the company

As AI contact centers advance, the meaning of a call center will change.

It will become a place to deepen customer understanding, not just a place to process inquiries.

Conversations become data.
Data becomes knowledge.
Knowledge nurtures AI.
AI improves response quality.
Response quality improves customer experience.
Customer voices return to products and the business.

Companies that can keep this cycle running are strong.

Conversely, companies that continue to treat inquiries merely as costs will miss out on the true intentions of their customers.

AI operators are not just a technology for automating phone responses.

They are a technology that changes how a company faces its customers.

Future contact centers will become the intelligence of the company.

AI operators are the gateway to that.