How Klarna’s AI Agent Strategy Backfired But Became A Useful Lesson

How Klarna’s AI Agent Strategy Evolved Is A Useful Lesson

Buy-now-pay-later financial services giant Klarna achieved impressive results when it rolled out agentic AI in customer service.

But it also learned a valuable lesson in the way AI strategies must adapt as businesses learn more about the impact of automation on their operations.

In early 2024, it deployed autonomous agents to assist with customer service inquiries. During their first month, they handled 2.3 million conversations. Of the two-thirds of tickets they played a part in resolving, the average resolution time dropped from 11 minutes to two minutes.

Over that year, the AI completed work equivalent to 700 human customer service agents, a figure which has now grown to around 850.

As the AI reduced the need for outsourced support, the number of external human agents working with Klarna fell from 3,000 to 2,300.

However, it found that human agents were still needed to handle more complex and sensitive issues, leading to the hiring of a relatively small team of around 100 highly-skilled human operators. Their job is to identify where the human touch can still provide the most value to its customers.

Klarna serves as a great example of a global business that, while achieving AI success, learned some important lessons that can benefit anyone working with AI in business.

Stories are now emerging from other companies, like Ford, that demonstrate how strategies have to evolve as businesses understand the impact of AI on workforces and customer experience.

So, let’s take a look at what Klarna learned and how they adapted in response.

What Do Klarna’s Customer Service Agents Do?

Klarna was an early partner of ChatGPT creator OpenAI and moved quickly to integrate natural language into its customer service. But rather than a simple chatbot, it built the offering around agentic AI.

This means it doesn’t just answer questions and generate information. It can work on complex, multi-stage tasks with minimal human involvement and interact with other systems.

In Klarna’s case, this means securely accessing customer data, monitoring the changing state of a ticket, issuing returns and managing payment plans.

These are exactly the types of tasks that agentic AI is good at: high-volume and extremely repetitive. Decisions follow straightforward logic, and clear guidelines can dictate when human intervention should happen.

The results were great. Driven by the headline figures mentioned above and a 25 percent reduction in repeat requests, Klarna estimated that the deployment added $40 million to its annual profit.

The 100-strong specialist team Klarna added to its workforce now works alongside its thousands of outsourced agents to help it understand and respond to more complex customer queries.

Human Intervention And Edge Cases

As AI took on many of the high-volume, routine inquiries, what was left were the more complex and ambiguous issues that typically need an experienced human capable of nuanced thought and empathy.

The role of these specialists is to identify which issues these are and how best to resolve them, with outsourced human help.

In an interview with CX Dive, Klarna spokesperson Clare Nordstrom said, "AI gives us speed. Talent gives us empathy. Together, we can deliver service that's fast when it should be, and empathic and personal when it needs to be."

While the decision to reduce human headcount was reported on as an AI failure, with Fast Company reporting that customer satisfaction fell sharply and service quality was inconsistent, this overlooks some important factors.

By their own metrics, Klarna's customer service AI rollout was a big success. However, by overlooking niche situations and problems where AI is less capable, its results were sub-optimal.

This was addressed by reassessing the need for human expertise and strategically applying it where it is likely to make a significant improvement when augmented by AI.

What Can Leaders And Professionals Learn?

The important lesson here is that AI is not a magic bullet. Even when the benefits are clear, we need to think about edge cases and where humans still need to be involved.

By identifying where performance was falling short, even when it meant walking back previous decisions, Klarna learned to adapt its strategy as its understanding increased.

Agents are built for routine, repetitive tasks where they can be given clear instructions and defined goals. But business in the real world is far too complex, nuanced and subjective for every workload to be handed to agents.

The reason agents fail in customer service edge cases is the same reason autonomous driving still isn’t commonplace for most of us, despite it being theoretically possible.

It also highlights that humans are still a critical element of AI infrastructure, even at the scale at which it's operated by Klarna.

Businesses have to chase efficiency, and sometimes this means reducing headcounts. But when it leads to an exodus of skill and talent, knock-on effects can be unpredictable.

This means thinking carefully about where human skills and experience need to be retained, even when AI is already significantly improving the performance of routine tasks.