Wrong AI Answer? An Incident-Response Playbook for Marketing Teams
· 6 min read · By Perciva Team
When ChatGPT tells buyers your product costs twice its real price, lacks an integration you shipped last year, or "is best suited for hobbyists," you have an incident — not a curiosity. The teams that handle wrong AI answers well borrow the discipline engineers use for outages: detect fast, triage by severity, contain the damage, remediate the root cause, verify the fix, and write down what you learned. This playbook adapts that loop for marketing teams.
The core mindset shift: a wrong AI answer is not a one-off embarrassment to screenshot and forget. It is a live, repeating misstatement served to every buyer who asks that question, and it stays live until the sources feeding it change.
Step 1: Detect — You Can't Respond to What You Don't See
Most wrong answers are discovered by accident: a prospect mentions it on a call, a founder tries a prompt at midnight. Accidental detection means the answer has typically been wrong for weeks already.
Systematic detection means running your buyer questions on a schedule and storing full answers — verbatim answer capture — so changes surface as diffs instead of surprises. At minimum, monitor pricing questions, capability questions ("does X support..."), and your top comparison questions weekly.
And when a wrong answer does arrive through the accidental channel — a prospect, a colleague, a screenshot in Slack — feed it into the same process rather than treating it as a one-off: reproduce it yourself in a clean session, capture it properly, and add the question to the monitoring set. Accidental detection is a gift; wasting it on an untracked ad-hoc fix is how the same claim resurfaces in three months.
Step 2: Triage — Assign a Severity
Not every inaccuracy deserves the same response. Triage each wrong claim with a severity level:
| Level | Definition | Examples | Target response |
|---|
| SEV-1 | Deal-killing falsehood on a high-intent question | Wrong pricing by a large margin; "doesn't support SSO / SOC 2" when you do; recommends rival due to a false claim | Start remediation within 48 hours |
| SEV-2 | Materially wrong, plausibly influencing shortlists | Missing flagship feature; outdated plan names; stale positioning ("small teams only") | Within 1 week |
| SEV-3 | Wrong but low-stakes | Minor feature detail; slightly old founding facts | Batch into monthly content updates |
| SEV-4 | Imprecise or hedged, not false | Vague descriptions; missing recent launches | Track; fix opportunistically |
Two factors drive severity: how wrong the claim is, and how commercially important the question is. A tiny error on "compare X vs us" outranks a big error on a question no buyer asks.
Step 3: Contain — Limit the Damage While You Fix
- Brief sales immediately for SEV-1s. Reps should know buyers may arrive believing the false claim, and have a one-line correction ready with proof.
- Publish the truth prominently on your own site if it isn't already unambiguous — a clear pricing page, a security page, an integrations directory. You can't correct an engine that can't find the correct fact.
- Record the evidence: engine, date, exact question, full answer, and cited sources. You'll need it for attribution and for the verification step.
Step 4: Remediate — Fix the Sources, Not the Symptom
AI engines synthesize answers from what they retrieve and what they were trained on. Remediation means changing the inputs:
- Find the origin. Check the answer's citations first. Wrong claims usually trace to an outdated third-party article, an old review-site listing, a stale pricing roundup — or your own outdated page.
- Fix what you own. Update your pricing, feature, and comparison pages so the correct fact is stated plainly, in text (not only in images or tables engines parse poorly), with a visible last-updated date.
- Request corrections on what you don't own. Reach out to the cited third parties with the correct information. Review platforms and comparison sites update more often than teams expect — they want accuracy too.
- Publish the missing authoritative page if the engine had nothing good to retrieve. Many hallucinations are gap-filling: the model invents a detail because no source states the real one. Claim extraction across all your monitored answers shows which facts engines consistently get wrong or omit — that's your content gap list.
For the content-side tactics in depth, see how to fix AI misinformation about your brand.
Step 5: Verify — The Incident Isn't Closed Until the Answer Changes
This is the step most teams skip. Re-run the exact question on the same engine weekly after remediation. Expect lag: retrieval-augmented engines (Perplexity, Copilot, ChatGPT with browsing) often pick up corrected pages within days to a few weeks; claims baked into training data can persist until a model refresh. Log the date the answer finally corrects — that close-the-loop receipt is also how you prove the work mattered.
If the answer hasn't moved after several weeks, escalate: the engine is likely leaning on a source you haven't fixed yet. Go back to step 4 with the current citation list.
Who Does What: Roles Without the Bureaucracy
An incident process with no owners is a document, not a process. You don't need an on-call rotation — you need three named hats, which in a small team may sit on two heads:
- Incident owner (usually the marketer who runs monitoring): triages severity, drives the timeline, and is the one person who can declare the incident closed — which only happens after verification, not after the fix ships.
- Fixer (content or product marketing): updates owned pages, drafts third-party correction requests, fills content gaps. Works from the incident owner's source attribution, not from guesses.
- Field channel (sales or CS lead): pushes the correction one-liner to anyone talking to buyers, and routes back what prospects are actually repeating — often your best detection signal for the next incident.
Special Case: Security and Compliance Claims
One class of wrong answer deserves an automatic SEV-1 regardless of the question's traffic: false statements about security, compliance, or data handling. "X is not SOC 2 compliant" or "X stores data in [wrong region]" disqualifies you in enterprise procurement before a human ever reads your security page — and the buyer who believed it never tells you why they went quiet. If you sell into enterprise, monitor these questions explicitly, keep your trust/security page unambiguous and machine-readable, and treat any false negative here as a same-week incident even when the affected question seems obscure. The asymmetry justifies the paranoia: a false positive about a feature costs a conversation; a false negative about compliance costs the shortlist.
Step 6: Postmortem — Make the Next Incident Cheaper
- What was wrong, on which engine, for how long (first-seen to verified-fixed)?
- Which source fed it, and why did that source have bad data?
- Was the question in your monitoring set before the incident? If not, add it.
- Does this class of claim (pricing, integrations, compliance) need a standing owner?
The One-Page Runbook
- Detect: scheduled scans over buyer questions, verbatim capture, diffs reviewed weekly.
- Triage: severity by wrongness × question importance (matrix above).
- Contain: brief sales, publish the truth, save the evidence.
- Remediate: fix owned pages, correct third-party sources, fill content gaps.
- Verify: re-scan weekly until the answer corrects; escalate if stuck.
- Postmortem: log duration, origin, and monitoring gaps.
A special case worth its own playbook: when the "wrong answer" is a rival's name in your recommendation slot — that's displacement, and the response differs; see what to do when ChatGPT recommends a competitor. And if you want the detect-diff-verify loop to run without anyone remembering to do it, that is exactly the workflow Perciva automates — our methodology page shows how capture, claim extraction, and verification fit together.