×
Written by
Published on

Klarna recently revealed that during their first month of deployment, its AI-powered customer service agents, built using OpenAI’s technology, handled the equivalent workload of 700 full-time human agents. This accounted for two-thirds of all customer support chats. The AI agents resolved tasks in under 2 minutes, compared to the 11 minutes typically required by human agents, while achieving comparable customer satisfaction scores.

Our estimates suggest that Klarna’s 700 human agents handled 2.3 million conversations per month at a cost of $3.6 million, or $1.58 per conversation. In contrast, an OpenAI-powered agent addressing the same volume at 2 minutes per chat would cost no more than approximately $9,000 per month, or $0.004 per conversation, based on GPT-4’s current pricing. This switch from human to AI agents could potentially save Klarna nearly 100% in labor costs.

Although companies like Klarna will likely maintain human-in-the-loop systems to handle customer support cases that AI cannot resolve, the efficiency gains from AI are expected to significantly reduce the number of humans in customer support roles. Moreover, we anticipate that all language-centric knowledge work related to enterprise sales, technical support, and customer success will face substantial disruption.

Sebastian Siemiatkowski, co-founder and CEO of Klarna. “We are incredibly excited about this launch, but it also underscores the profound impact on society that AI will have. We want to reemphasize and encourage society and politicians to consider this carefully and believe a considerate, informed and steady stewardship will be critical to navigate through this transformation of our societies.

Recent Articles

Pika 1.5: A Leap Forward in AI Video Generation

Pika 1.5: A Leap Forward in AI Video Generation

Move over, Hollywood – this AI can create blockbuster-quality videos with a few clicks
Guide to Google’s NotebookLM

Guide to Google’s NotebookLM

A very meta in depth guide and audio example of how NotebookLM makes a guide on using Perplexity.