Sebastian Siemiatkowski

Now, because we don't hire these people ourselves, they work for customer service companies, they just shifted and started working on their own.

So fortunately, in that situation, nobody lost their job, but it was still like an eye opener for us, like, wow.

And then the point is, what you realize when you're early on that journey is, again, for customer service agents, whether it's AI or humans for that sake, for them to be able to answer questions really well, they need as much context as possible.

Where is that context?

It's in the source code of your software.

How does Klana calculate interest?

Well, we can have a documentation of that, but the truth is in our source code, is somewhere deep in our source code where that interest calculation is actually explained, right?

And documentation may be inaccurate.

So what you realize when you pursue this is that like customer service isn't just like, hey, I need an agent that answers questions.

Sooner or later, you wanted to read the source code and explain to the customer how it works.

You want them to provide as much context as possible.

to be able to give the right answers.

And that's when you start realizing that it's not something you, in our case at least, we come to the conclusion we cannot buy it off the shelf because it actually becomes part of our tech stack.

Will every large technology-first company build their own customer support system?

I'm not sure.

I think there will be, no, I think obviously

I believe that for Klarna, the right thing was to be early, to try to find what we can do with this technology and where it can bring us.

And I think it's going to be a competitive advantage over time to incumbents that haven't done that.

But a lot of incumbents will obviously procure fantastic AI customer service solutions in order to try to reduce the gap between what we're doing and they're doing.

So, you know, who knows?