Klarna: the X-ray deck

The full X-ray of Klarna · keys ← → · F fullscreen · A all slides. The plain-text version is this page's markdown twin (index.md).

X-rayed by RiskMandate

Klarna

A payments and shopping company whose OpenAI-powered assistant answers most of its customer-service chats, with humans brought back as a choice.

Sources read on 2026-11-07 · web-summit.sgit.ai/xray/klarna

What it does

Klarna

Klarna is a payments and shopping company (it describes itself as a global digital bank and flexible payments provider) whose app lets consumers pay later, manage payments and shop. Since February 2024 its OpenAI-powered AI assistant has answered customer-service chats in the app, handling refunds, returns, payment issues, cancellations, disputes and invoice inaccuracies; Klarna says it handled 80% of customer-service chats in 2025. Customers can still ask for a human, and in May 2025 the CEO said Klarna would recruit human agents so that a person is always available.

Sources: s1, s6, s3, s8, s9

Its AI agents

What acts on its own

AI assistant (service)The customer-service chatbot in the Klarna app, launched globally in February 2024 with OpenAI, available 24/7 in 23 markets and more than 35 languages. Klarna says it had 31 million conversations since launch and handled 80% of customer-service chats in 2025, the equivalent of over 850 full-time agents. It also gives customers their balances, payment schedules and spending limits. LangChain's customer story says it is built on LangGraph and tested with LangSmith.
  • Answers customer-service chats in the app, in more than 35 languages
  • Handles refunds, returns, payment-related issues, cancellations, disputes and invoice inaccuracies
  • Shows outstanding balances, upcoming payment schedules and purchase power, and explains spending limits
  • Customers can choose a human agent instead; Klarna tells users they are talking to a chatbot
AI assistant (shopping)Chat-based shopping features added to the same assistant in September 2024: product search, personalised recommendations, comparisons and pricing, integrated with Klarna's price database of 5.6 million products from thousands of stores (Pricerunner). Users can buy directly in the app with Klarna's flexible payment methods.
  • Searches products and brands, with reviews and price history
  • Recommends products from the user's shopping preferences and habits
  • Compares products with an AI-generated pros-and-cons summary
  • Reads price, stock, delivery and cashback information across merchants
  • Leads to a purchase in the app with Klarna's payment methods
Kiki (internal)Klarna's internal knowledge chatbot on OpenAI models, launched June 2023. Klarna said in May 2024 it had answered over 250,000 employee questions (about 2,000 a day) and that 85% of employees used it; the 2025 annual report says it helps employees find information across internal systems.
  • Answers employee questions in 1 to 5 seconds
  • Personalises answers to the asker (for example, what my team is working on)
  • Draws on information across Klarna's internal systems

Use case · AI assistant (service)

2024 to 2025: AI-first customer service, then humans back

Reach
  • Customer-service chats in the Klarna app: two-thirds of chats in the first month (2.3 million conversations), 80% of chats in 2025, Klarna says people
  • Refunds, returns, payment-related issues, cancellations, disputes and invoice inaccuracies money
  • Each customer's outstanding balances, upcoming payment schedules, purchase power and spending limits data
  • The hand-off to a person: customers can choose a live agent, and the US help page offers human support or a dedicated agent people
  • The customer-service workforce: about 3,000 agents, mostly at outsourcing partners, which a spokesperson said would fall to about 2,300 people
  • Klarna's public and regulatory statements about the assistant: 700 then 853 agents' equivalent work, $39 million (2024) and about $59 million (2025) in cost savings, satisfaction on par with humans, all from Klarna's own measurements outside
Mandate (illustrative)
  • Klarna's stated policy: every user is told they are talking to a chatbot and can choose to speak with a human instead (Klarna, December 2024)
  • As a Klarna spokesperson put it in March 2024: more experienced and senior staff are still needed, with specialised training for complex or sensitive cases
  • Illustrative: the assistant resolves routine errands end to end; a change to a customer's money or payment terms is recorded with who decided it
Gap
  • Disputes, refunds and invoice corrections sit in the same chat reach as routine questions; nothing public shows where the line to senior human staff for complex or sensitive cases was drawn.
  • The human path that contains the agent (the choice of a person) depended on a workforce that was shrinking while the assistant took more chats.
  • The evidence of success is Klarna's own: share of chats, resolution time and satisfaction from its chat logs and internal surveys, with no independent measurement in the public record.

Use case · AI assistant (shopping)

Shopping assistant: from recommendation to purchase

Reach
  • Klarna's price database of 5.6 million products from thousands of stores, with stock, delivery and cashback information data
  • The user's shopping preferences and habits (what they buy and when, brands they prefer), used for recommendations data
  • A purchase made directly in the app with Klarna's flexible payment methods (Pay Later and Fair Financing are consumer credit) money
  • Merchants' listings and customer reviews, summarised into an 'unbiased, AI-generated' pros and cons outside
Mandate (illustrative)
  • Illustrative: help the user find and compare products with a neutral summary
  • Illustrative: the purchase, and any credit used for it, is the user's own decision, made outside the recommendation
Gap
  • Recommendations built from the user's habits run straight into a purchase, possibly on credit, in the same chat flow.
  • Klarna calls the summaries unbiased, but nothing public shows how the ranking is evidenced to be independent of Klarna's merchant relationships.

Use case · Kiki (internal)

Kiki: internal answers across internal systems

Reach
  • Information across Klarna's internal systems data
  • Who is asking: answers are personalised to the person and their team people
  • Over 250,000 employee questions and Kiki's answers (about 2,000 a day) data
  • Third-party AI tools employees use, where Klarna's own risk factors say confidential information could be disclosed outside
Mandate (illustrative)
  • Illustrative: answer an employee from information that employee is already allowed to see
  • Illustrative: no confidential information leaves Klarna's systems through an AI tool
Gap
  • Kiki answers across internal systems and personalises by asker; nothing public shows how its answers follow each employee's own access.

The simulation · 2024 to 2025: AI-first customer service, then humans back

Each action, placed against the mandate

  1. December 2023: Klarna announces a hiring freeze except for engineering roles as it turns to AI in the mandate
  2. February 2024: the assistant, live globally for a month, has had 2.3 million conversations, two-thirds of all customer-service chats; Klarna says that is the work of 700 full-time agents in the mandate
  3. A spokesperson says the outsourced customer-service department of about 3,000 will shrink to about 2,300 because of the assistant in the mandate
  4. (illustrative) Handles a customer's dispute over an invoice inaccuracy inside the chat, as the launch release says it can, without a senior human looking at it in the gap
    The record: RiskMandate's record shows disputes and refunds (money) inside the same reach as routine questions, against a stated mandate that complex or sensitive cases need experienced staff, and no public evidence of where that line is enforced.
  5. Customers complain of generic answers and of questions too complicated for it, a Forrester analyst later tells CX Dive; an engineer who tested it said it recites the docs and passes him to human support fast in the gap
    The record: RiskMandate's record shows the hand-off to a person as the control that contains the agent, and the human capacity behind that hand-off being reduced at the same time. Klarna's own record says satisfaction did not drop.
  6. 8 May 2025: the CEO tells Bloomberg the cost-cutting in customer service has gone too far, that cost was 'a too predominant evaluation factor' and the result 'lower quality', and starts recruiting human agents so customers always have a person in the mandate
  7. 16 July 2025: answering an SEC comment, Klarna says the remark was about the quality of its human customer service, not AI-driven service, was misreported, and adds its 'dual-track' disclosure in the mandate

What RiskMandate gives

RiskMandate is never in the request path: read-only by design.

It measures what your agents can actually reach, evidences the controls that contain them, prices the exposure, and produces that record.

riskmandate.ai/llms.txt

Sources

Every fact, with its source and the date read

And 11 more on the X-ray page.

The questions we would ask

We have not asked them yet

  1. Which errands can the AI assistant complete on its own (refunds, dispute outcomes, invoice corrections, payment changes), and which must go to a person before money or payment terms change?
  2. How is a dispute or other complex or sensitive case detected in the chat and handed to experienced staff, and is that hand-off recorded?
  3. The satisfaction and accuracy figures come from your own chat logs and surveys: are they broken down by case type, and has anyone outside Klarna measured them?
  4. When the shopping assistant leads to a purchase on credit, what separates the recommendation from the credit offer, and how is the 'unbiased' summary evidenced?
  5. How do Kiki's answers respect each employee's own access to internal systems?

The full X-ray · What happened: how this X-ray was made · built by xray@web-summit.dinis

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