Customer Support Research & Analysis Business Operations

Sebastian Siemiatkowski's AI use case

Co-founder and CEO at Klarna

AI CEO hotline workflow where customers and merchants talk to an AI version of Klarna's CEO, then transcripts and summaries are analyzed into product feedback for internal teams.

The problem

What was broken before AI

Customer feedback often arrives in scattered, low-context formats: survey scores, open text boxes, app reviews, support conversations, merchant complaints, social posts, and one-off executive escalations. Teams then have to translate that material into themes, urgency, owner, and next step before anyone can act on it.

What changed

What the use case made possible

Klarna used a conversational AI interface as the front door for feedback, then used transcription, summarization, and LLM analysis to turn spoken customer input into a live internal feed for product and engineering review. The CEO-clone experience makes the channel feel unusually direct, while the operational value comes from shortening the path from raw customer language to triaged internal work.

Why this matters

Why this use case is worth studying

Most companies treat feedback collection and feedback interpretation as separate jobs. Klarna’s workflow combines them: the moment a customer speaks, the system can capture context, summarize the issue, classify it, and push it toward a dashboard. The AI personality gets attention, but the real lesson for builders is how much leverage comes from designing the handoff after the conversation ends.

Use this when

When this pattern applies

Use this when your company gets lots of customer feedback but struggles to convert it into structured, prioritized product work. It is especially relevant when feedback is scattered across support, surveys, sales calls, app reviews, and founder inboxes.

Exponential Builder analysis

Design the handoff before the interface

The hotline gets attention, but the durable system is the handoff from spoken feedback to internal action. AI products become more useful when they package messy input for the next human decision.

Conversation can raise feedback quality

A caller can explain context, frustration, and desired outcome in a way a five-star survey cannot capture. Voice is especially useful when the goal is to understand the story behind the complaint.

Executive access can be simulated, accountability cannot

An AI CEO can create a feeling of direct access, but the company still needs real owners, prioritization rules, and follow-through. The workflow only earns trust if customer input visibly reaches teams that can change the product.

Who this is for

Best fit

Product leaders who want richer customer feedback than survey scores

Support leaders trying to turn recurring issues into product fixes

Founders who want a direct line to customers without creating another inbox

Ops teams building voice-of-customer dashboards

AI builders designing customer-facing feedback loops

Companies with enough feedback volume to justify automated triage

What to avoid

Mistakes and warnings

Where this pattern can go wrong if you copy it too literally.

Do not let the novelty of an AI CEO distract from whether teams actually act on the feedback.

Do not use a synthetic executive voice without very clear disclosure.

Do not collect sensitive financial, personal, or account-specific data unless the system is designed and approved for that use.

Do not promise next-day fixes unless the organization can reliably deliver them.

Do not route raw AI summaries directly into engineering work without human review.

Do not measure success only by call volume; measure quality of insight, time to triage, and closed-loop actions.

Do not treat LLM classifications as ground truth when a complaint may involve compliance, fraud, payments, or vulnerable customers.

Public workflow preview

The shape of the workflow

A high-level look at how the use case works, with the reusable pattern made clear.

Invite the call

Customers and merchants are directed to a hotline where they can share product feedback, issues, and suggestions.

Use an AI front door

The caller speaks with an AI version of Sebastian Siemiatkowski trained on his voice, insights, and experiences, according to Klarna.

Capture the conversation

Each call generates a transcript and concise summary.

Analyze for action

An LLM reviews the call output and extracts insights for an internal live feed and innovation dashboard.

Route to owners

Product and engineering teams review the insights and assign priority tasks where appropriate.

Copy the pattern

The reusable idea

Pattern in one sentence

Turn customer conversations into structured product intelligence by combining a conversational AI front door with transcript, summary, LLM triage, dashboard routing, and human follow-up.

Reusable idea

You do not need a CEO voice clone to copy the best part of this. Start with a dedicated feedback channel that feels higher-signal than a form, then design the post-conversation workflow before you launch it. The key is to decide how every call becomes a structured object: customer type, product area, pain point, severity, evidence, suggested owner, and next action.

Steal this workflow

Voice-of-customer triage recipe:

Input: One recorded feedback call or transcript.

Weekly ritual

Caller type: consumer, merchant, prospect, partner, unknown

Product area: payments, onboarding, app, merchant dashboard, checkout, support, other

Main issue: one sentence

Evidence: short supporting excerpt or paraphrase

Severity: low, medium, high, urgent

Frequency signal: new, repeated, unknown

Suggested owner: product, engineering, support, compliance, growth, design

Recommended next action: investigate, create ticket, request follow-up, add to roadmap, close as non-actionable

Follow-up question: one question a PM should ask if they contact the caller

Review the top 20 highest-severity records.

Cluster them into themes.

Assign owners for the top 3 themes.

Publish a short internal note: what customers said, what changed, what needs more evidence.

Suggested prompt

"You are analyzing a customer feedback call for a product and engineering team. Read the transcript and produce a structured triage record with: 1) one-sentence summary, 2) caller type, 3) product area, 4) core user problem, 5) stated request, 6) severity from low/medium/high/urgent with rationale, 7) evidence from the transcript, 8) likely owner team, 9) recommended next action, 10) follow-up question for the customer, and 11) any risk flags such as privacy, payments, fraud, legal, safety, or account-specific issues. Be conservative. If the transcript does not support a field, write 'unknown.'"

Field notes

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