In the 1980s, a gamer calls Nintendo with a problem: he can’t get the White Sword in The Legend of Zelda.
Sounds ridiculous today, doesn’t it? Imagine calling someone for help with a video game. Just Google it. Ask a friend. Start the game over…
But there’s something in that phone call that billions of dollars of AI investment still haven’t quite managed to recreate:
The moment the customer needs help, there’s someone on the other end who actually understands them.
The Nintendo employee doesn’t read from a script.
“Which quest are you on?”
“How many heart containers do you have?”
“Where exactly are you stuck?”
They understand the context. Diagnose the problem. Give the right answer.
What’s remarkable is that the kind of one-to-one attention a gamer could get 40 years ago is still considered a luxury at many large companies today.
We’re Using Technology to Solve the Wrong Problem
Whenever companies encounter a new technology, they tend to jump to the same question:
“How much money can this save us?”
Fewer people in the call center. Fewer tickets. Shorter calls. Higher automation rates.
Then the technology gets placed between the company and the customer.
IVR. Forms. Chatbots…
And now, AI.
Too often, the goal isn’t to serve the customer better. It’s to delay the moment they reach a real person for as long as possible.
I think that’s a fundamental mistake in how we think about technology.
Technology shouldn’t be used to reduce human connection.
It should be used to multiply it.
The right question isn’t:
“How can we do this with fewer people?”
It’s:
“How can we give every customer the same level of service we give our very best customers?”
The first is a cost-cutting strategy.
The second is a competitive strategy.
Great Service No Longer Has to Be a Scaling Problem
Until now, truly personalized service has been expensive.
Your most valuable customers get a dedicated relationship manager. A private banking customer calls and someone already knows who they are. A VIP customer doesn’t wait in line. They don’t have to explain their history again. They speak to someone who already understands what they need.
Giving that experience to millions of customers simply wasn’t economically realistic.
So companies rationed attention.
At the top: one-to-one service.
At the bottom: a queue.
One of the biggest things AI changes, in my opinion, is exactly this:
The economics of one-to-one service.
Built properly, an AI system doesn’t just answer questions.
It knows who the customer is.
It knows what they’ve done before.
It knows what they bought.
It knows where they are in the journey.
It knows what problems they’ve had in the past.
And it builds the current conversation on top of all that context.
So instead of giving the customer a bot that starts from zero every single time, you give them an assistant that actually knows them.
And you can do that for thousands, potentially millions of people at the same time.
You no longer need to hire thousands of additional employees to make that possible.
But you do need to put AI in the right place.
Customer Service Is Actually Part of Sales
On an org chart, sales, customer service, retention, and cross-sell all live in different boxes.
Customers don’t think in boxes.
They’re just trying to do something.
“Is this product right for me?”
“Which one should I choose?”
“How do I set this up?”
“Why didn’t this work?”
“What am I supposed to do now?”
“Is there a better option for me?”
The company may label some of these questions “sales”.
Others “customer support”.
Others “customer success”.
From the customer’s perspective, they’re all the same question:
Are you going to help me?
And every moment in which you help them has a commercial consequence.
Help them make a decision and conversion goes up.
Help them get more value from the product and retention improves.
Solve their problem quickly and churn goes down.
Understand what they actually need and the right upsell opportunity appears naturally.
That’s why treating customer service purely as a cost center feels increasingly strange to me.
What you actually have is an always-on growth channel.
The Biggest Opportunity Isn’t in Big Problems. It’s in Small Moments.
We usually think about customer experience when something goes wrong.
A complaint comes in. A ticket gets opened. Someone calls the support line.
But a lot of customer loss doesn’t happen during dramatic moments.
It happens through tiny moments of friction.
They don’t understand something. They can’t make a choice. They don’t want to spend another two minutes figuring it out. They can’t find an answer. They open another tab…
And then they never come back.
In many of these moments, the company doesn’t even know the customer needed help.
This is where conversational AI that can speak naturally across the phone, website, or mobile app becomes especially powerful.
It doesn’t have to be a system that simply responds when something goes wrong.
It can become a layer that stays with the customer throughout their journey.
It can step in at exactly the moment help is needed.
And it can make that help deeply personal.
This Is the Biggest Difference We See in Enterprise Projects
At Next Big App, our work with large organizations in Türkiye keeps revealing the same divide.
When AI is treated as little more than a smarter FAQ bot, the outcome is limited.
Call volume drops a little. Costs come down a little. But the customer experience doesn’t fundamentally change.
Once you bring the customer’s history, transaction context, current stage, and intent into the conversation, something very different happens.
The system stops merely answering questions.
It starts helping. It makes the customer feel looked after.
Let me give you a real example.
At one large company we work with, customers who need support fill out a form on the website. It could be a complaint, a suggestion, a request, anything.
What happens next looks roughly like this:
The form lands in an internal system and waits for an employee to categorize it. Manually.
Then it gets assigned first to a department and then to an individual employee. Again, manually.
That employee has to find time in the middle of an already busy day to get back to the customer. Usually by phone.
They listen to the customer, understand the issue, and genuinely try to help.
After the call, they take whatever internal actions are necessary: call another employee, update a system, fix whatever caused the problem, and so on.
Then they call the customer again with an update. If the customer is happy, great. If not, the whole cycle begins again.
Finally, the employee has to document everything. They sit down and write a long email summarizing the entire case from beginning to end.
You can see what’s happening here.
This is the kind of process only a company that genuinely cares about its customers would be willing to take on.
The amount of effort being spent to make one customer happy is genuinely worth applauding.
So what did we do for them?
We built a system that uses AI to understand the request, the context, and the relevant information from a much larger pool of company data.
It can automatically call the customer, listen to the entire issue, resolve it immediately when possible, or generate a report and route the case to the right teams automatically.
The goal isn’t just efficiency.
It’s to improve the customer’s experience while also making much better use of employees’ time.
At the beginning, the concern was predictable:
“What if the AI makes a mistake while talking to a customer on the phone?”
Over time, that changed into something closer to:
“The AI is actually more patient, more knowledgeable, and better at finding solutions than I am when I’m trying to squeeze these calls into the middle of a hectic day.”
Now both customers and employees are happier.
That, in a nutshell, is how we think about conversational AI.
Not as “replace the human with a bot.”
Almost the opposite.
We see it as a way to take the kind of high-quality attention you could previously afford to give only a small number of customers — and make it available to everyone.
So that every customer, at any moment, can feel like there’s someone beside them who already knows who they are.
To me, that’s the real promise of AI in customer experience.
Same Technology. Two Completely Different Outcomes.
You can have the same model. The same integrations. The same data…
But the objective you give the system changes everything.
If your goal is:
“Let’s cut call center costs by 20%.”
You’ll probably end up putting a slightly smarter barrier between the customer and your company.
If your goal is:
“Let’s help every customer as well as the best employee in our company would.”
You’ll design a completely different experience.
In one, technology gets between you and the customer.
In the other, it brings you closer.
Over the next few years, I don’t think competitive advantage will come from answering the question:
“Which companies are using AI?”
Everyone will be using it.
The real divide will be somewhere else:
Between companies that use AI to replace human connection, and companies that use AI to scale it.
That Nintendo player was only trying to get the White Sword.
But what they actually received was something more valuable:
The feeling that, at the exact moment they needed help, someone was there who genuinely wanted to help them.
Today, we have the technology to give that feeling to millions of people at once.
Using it only to cut costs would mean choosing the smallest part of the opportunity.
The irony is that the better AI gets, the more valuable genuinely human connection becomes. Technology can remove friction, but trust, chemistry and belonging are much harder to engineer. The biggest opportunity may be using AI to bring people together, not replace the need for each other.
I agree, but I don't think people value it. I launch podcasts for B2B SaaS founders to connect directly with their ideal customer. While you think that they would understand the power of that human connection, people are mostly interested when they find out that we repurpose the podcast for AI.