AI Support Transcript Quality Checklist: Weekly QA for Bots and Humans

AI Support Transcript Quality Checklist: Weekly QA for Bots and Humans

Building a support assistant is only half the job. The half that keeps customers safe is reviewing real conversations: Did the bot answer from an approved source? Did it invent a policy? Did the handoff include enough context for a human?

This tutorial gives you a weekly transcript QA loop you can run with a spreadsheet and exported chats—or with meeting/transcript tools if your team reviews voice support. It extends the AI customer support workflow tutorial with an explicit quality scorecard.

AiToMake content is for education and research. Use these examples with your own policies, tools, and review requirements. See the Earnings Disclaimer for tool-cost caveats.

What you will build

By the end you will have:

• a 10-item transcript scorecard
• a sampling plan (which chats to review)
• escalation and handoff checks
• a weekly knowledge update checklist
• a short metrics set that avoids “deflection-only” blind spots

Sources checked

Primary and official pages used for this guide (verify live details before you buy or change process):

• OpenAI Klarna customer story — first-month volume and service metrics for Klarna’s AI assistant
• Zendesk: Evaluating AI agents with Zendesk QA
• Zendesk: Creating scorecards
• Intercom: AI–human collaboration and handoffs
• Intercom: Building Fin Procedures
• Otter pricing, Fireflies pricing, Fathom pricing
• Fireflies Agent Scorecard skill
• Moffatt v. Air Canada (B.C. Civil Resolution Tribunal)

Secondary reporting on Klarna’s later quality discussion: Silicon Republic summary of May 2025 comments. Treat media narratives carefully; Klarna’s public story includes both strong early automation metrics and later emphasis on human quality.

Why transcript QA matters

Case A: Volume metrics can hide quality problems

Klarna’s AI assistant, described in OpenAI’s case study, handled 2.3 million conversations in its first month—about two-thirds of Klarna’s customer service chats—with claimed workload equivalent to roughly 700 full-time agents, resolution under two minutes versus about eleven previously, and customer satisfaction described as on par with human agents.

Those numbers are useful for understanding scale. They are not a substitute for reading hard conversations. Later public discussion (including CEO comments reported in May 2025) emphasized that organizing support with cost as too dominant a factor can produce lower quality, and Klarna described investing again in human support capacity. The practical lesson for smaller teams is simple: track quality samples, not only automation rate.

Case B: Wrong policy answers create real liability

In Moffatt v. Air Canada (2024 BCCRT 149), a customer relied on a website chatbot that described a retroactive bereavement-fare process. Air Canada’s actual policy did not allow that approach. The tribunal found the airline responsible for the inaccurate representation and ordered compensation. Coverage such as The Guardian’s report and the tribunal decision make the operational point clear: policy answers need source checks, and “there was also a correct page elsewhere on the site” is a weak defense.

Case C: Platforms treat bot QA as a first-class workflow

Zendesk documents how to evaluate AI agents with the same QA scorecard machinery used for humans: filter bot conversations, apply a scorecard, and use Reviews / BotQA views. Intercom’s handoff guidance stresses structured handoff notes and feedback loops when Procedures escalate. You do not need their exact stack—you need the same loop: sample → score → fix sources → retest.

Step 1: Define the review sample

Do not try to read every chat on day one. Pick a weekly sample:

If you use Zendesk-style tooling, filter for bot participants as described in Zendesk’s AI QA article. If you export CSV from a helpdesk, filter by assignee = bot / channel tags.

Step 2: Use a 10-item transcript scorecard

Score each sampled conversation Pass / Fail / N/A. Failures need a one-line root cause (mirroring Zendesk’s scorecard root-cause idea).

Critical fails (auto-fail the conversation): invented policy, unsafe advice, or a promise the system cannot keep—especially refunds, account closure, fraud, or hardship cases.

Step 3: Run the review meeting (30–45 minutes)

Weekly agenda:

1. Score the sample with two reviewers when possible (spot disagreements).
2. Cluster fails by root cause: missing doc, stale doc, bad prompt, missing escalation rule, tool bug.
3. Assign owners: knowledge update vs workflow change vs training.
4. Retest: ask the same customer question in a staging bot after the fix.

Intercom’s handoff article is useful here: agents need a path to flag bad escalations so Procedure quality improves over time—not only so individual tickets close.

Step 4: Fix the operating loop, not only the prompt

Most wrong answers come from knowledge and routing, not from “the model being dumb.”

A practical confidence pattern used across support-AI vendors: high → auto-reply, medium → draft for human, low → escalate. Tune thresholds after you have a few weeks of scorecard data—not before.

Step 5: Optional tooling for transcripts and coaching

You can run the scorecard in a spreadsheet. Tools help when you already record calls or want timestamped coaching.

For chat bots, prefer helpdesk export + Zendesk-style scorecards. For voice, pair your meeting notes tool comparison with this checklist. Always confirm consent, retention, and sharing rules before recording customers.

Step 6: Metrics that catch silent failures

Track weekly:

If automation rises while reopen and critical fails rise, pause expansion and repair sources.

Copy-paste: handoff note template

Use this when the bot escalates:

Customer intent:
Order / account ID:
What the customer already tried:
What the bot answered (quote):
Source doc the bot used (URL or title):
Why escalated (loop / low confidence / policy / customer request / risk topic):
Suggested next human action:
Risk flags (refund, fraud, legal, hardship): yes/no

Copy-paste: weekly QA log columns

date | conversation_id | channel | topic | score_pass_count | critical_fail | root_cause | owner | fix_due | retest_result

Limitations

• This checklist does not replace legal review for regulated industries.
• Vendor AutoQA categories (tone, empathy, spelling) help triage; humans still need to check policy truth.
• Public enterprise cases (Klarna, airlines) are not identical to a 5-person shop—adapt sample sizes.
• Pricing and plan features for Otter, Fireflies, and Fathom change; re-check official pricing pages.

Related guides

• Build an AI Customer Support Workflow from Real Cases
• AI Meeting Notes Tool Test: Otter vs Fireflies vs Fathom
• Real-World AI Practice Case Library
• Internal AI Knowledge Assistant Playbook

Next action

This week: export 15 AI-handled chats, score them with the 10-item card, and fix the single highest-frequency root cause in your knowledge base. Do not expand bot scope until critical fails are near zero on policy topics.