Resolve routine support requests without an agent

Last updated 11 August 2026

Automated first-line support lets customer service teams resolve repetitive requests — order status, returns, refunds — without a human agent, by answering from policy documents and order systems and handing anything unresolved to a person, typically absorbing a large share of contact volume while the team keeps the complex cases.

Retail & E-commerceFinancial ServicesCustomer SupportTime savings

What problem this solves

Support queues fill with the same questions. Where is my order, how do I return this, why was I charged twice. Each one is quick, but there are thousands, they arrive at all hours, and they crowd out the cases that genuinely need judgement.

Hiring to cover peaks is expensive and seasonal demand makes it worse. Agents burn out answering the same question for the fortieth time that shift, and the customer still waits.

How it works

  1. Group historical tickets by intent to find the handful of request types that make up most of the volume.
  2. Connect the assistant to the systems that hold the answer — order status, returns policy, account records — so responses come from live data rather than a general-purpose model.
  3. Answer the customer directly for the intents you have chosen, in their language, at any hour.
  4. Hand off to a human the moment a request falls outside those intents, or the customer asks for a person.
  5. Keep the route to a human agent visible at every step rather than buried behind a menu.
  6. Review transcripts weekly for wrong answers and narrow the scope wherever the assistant is unreliable.

What you need to start

  • Ticket history labelled by intent, to establish which request types actually dominate volume
  • API access to the systems of record — orders, returns, billing — so answers reflect the customer’s real account
  • An escalation path to a human that is always visible, not hidden behind a menu
  • Retained support headcount for complex and sensitive cases: Klarna resumed hiring agents in 2025 after judging that an AI-only model had cost it service quality

Expected outcomes

Typical ranges, with sources

MetricTypical rangeSource
Share of chats handled without an agentAround two-thirds at KlarnaView source
Average resolution time11 minutes to under 2 at KlarnaView source
Support headcount replacedNone — Klarna rehired agentsView source

Real-world signal

Emerging evidence · 2 publishers

  • Klarna, a payments company serving e-commerce merchants, reported that in its first month its AI assistant handled two-thirds of customer service chats — 2.3 million conversations — cutting average resolution time from 11 minutes to under 2.

    klarna.com · 2024 ↗

  • Klarna resumed hiring human support agents in 2025 after its chief executive said cost had become too dominant a factor and quality suffered, while the assistant continued to handle around two-thirds of inquiries.

    customerexperiencedive.com · 2025 ↗

Common questions

How much data do you need to start?

A year of tickets is enough to see which intents dominate. You do not need to cover everything: the top five request types usually account for most of the queue, and scoping tightly is what keeps answer quality high.

What happens when the assistant gets it wrong?

It should hand over to a person, and the customer should always be able to ask for one. The failure mode to avoid is a system that keeps trying instead of escalating. Review transcripts weekly and pull back scope where accuracy is poor.

Does this let us reduce the support team?

Treat that as a separate decision, and be careful. Klarna moved to an AI-first model and then resumed hiring agents in 2025 after judging that quality had slipped. The reliable gain is absorbing routine volume so the team can handle harder cases.

How long does it take to get running?

A narrow deployment covering two or three intents takes a few weeks. Connecting to order and billing systems so answers use live account data is usually the longest part.

What these scores mean

How this use case was rated, and against what

Scoring rubric for this use case
DimensionRatingWhat that means
ImpactHigh4/5Material gain across a function
Implementation effortModerate3/5Custom workflow, 3–8 weeks
Data requirementLow2/5Needs one tidy export
Company size51–200 · 201–1000 · 1000+

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