AI is saying inaccurate things about our brand. How do you manage brand reputation in AI search?

You’re right to be concerned. AI reputation management is now essential.

Why this happens:

AI systems learn from:

1. Your website (current and cached versions)
2. Third-party content about you (reviews, comparisons, articles)
3. Historical web data (may be years old)
4. User-generated content (forums, Reddit, reviews)

They synthesize all this without distinguishing current from outdated, or accurate from inaccurate.

The correction framework:

Layer 1: Your own properties

• Update website with accurate, prominent information
• Use schema markup to explicitly state current facts
• Create clear FAQ pages addressing common misconceptions
• Add “last updated” dates prominently

Layer 2: Third-party sources

• Identify articles with outdated information, request updates
• Build new third-party content with accurate information
• Update Wikipedia with current facts (following guidelines)
• Engage in Reddit/forums to correct misinformation

Layer 3: Monitoring

• Regular AI query testing across platforms
• Set up alerts for brand mention changes
• Track sentiment and accuracy over time

The key: You’re not directly changing what AI says. You’re changing what AI learns from.

We had a similar issue with outdated pricing. Here’s what actually fixed it:

The problem: AI cited pricing from 3 years ago (we’d raised prices significantly).

What we tried first (didn’t work immediately):

• Updated pricing page on our site
• Added structured data
• Waited 3 months - no change

What actually worked:

1. Created multiple pages mentioning current pricing - Product page, FAQ page, comparison pages, blog posts
2. Got third-party content updated - Contacted review sites, asked them to update pricing
3. Published press release about pricing that got picked up
4. Updated Wikipedia pricing section (following notability guidelines)

Timeline: Changes started appearing in AI answers about 6 weeks after the multi-pronged approach.

Key insight: One page update isn’t enough. You need multiple corroborating sources to shift AI’s understanding.

The multi-source approach makes sense. AI is triangulating across sources, so we need consistent information everywhere.

Follow-up question: What about the deprecated feature that AI is still promoting? That’s actually misleading prospects into expecting something we don’t offer anymore.

And the 2-year-old bug being mentioned as current - that’s damaging.

How do you handle specifically NEGATIVE outdated information?

Negative outdated information is tricky but addressable:

The deprecated feature issue:

1. Create explicit “what’s changed” content on your site

   • “Feature X: Deprecated in 2024” with explanation
   • AI can learn that it’s no longer offered

2. Update comparison content that might reference the old feature

3. If feature is mentioned in reviews - you can’t change reviews, but you can:

   • Respond publicly noting the change
   • Create newer content that outranks old reviews
   • Seek updated reviews from current customers

The old bug issue:

1. Create a resolution announcement if you haven’t

   • “Bug X: Resolved in [Date]”
   • Explain the fix and current state

2. If it’s from a third-party source:

   • Contact the publisher, ask for update
   • If they won’t update, ask them to add a note about resolution

3. Generate positive current content that outweighs old negative

   • Recent case studies
   • Current customer testimonials
   • Updated reviews

The principle: You can’t delete old content, but you can:

• Create explicit correction content
• Generate fresher content that supersedes old
• Update third-party sources where possible

On contacting AI companies directly:

OpenAI (ChatGPT):

• Has feedback buttons on responses
• Won’t fix specific brand issues quickly
• Focus on factual errors with clear evidence

Perplexity:

• More responsive to factual corrections
• Can sometimes update quickly for clear errors
• Submit via help channels with evidence

Google AI Overview:

• Connected to Google Search, so standard webmaster feedback applies
• Fixing search ranking often fixes AI Overview

The reality: Don’t rely on AI company intervention. Your fastest path to correction is changing what they learn from - your web presence and third-party sources.

When to escalate:

• Clearly defamatory false statements
• Legal/safety misinformation
• Severe factual errors affecting customers

Even then, fix the source first - that’s more reliable than waiting for AI companies to act.

Prevention is easier than correction. Here’s our proactive approach:

Content hygiene:

1. Version everything - When you change pricing, features, policies, create clear dated announcements

2. Explicit deprecation - Don’t just remove old features; announce they’re removed

3. Regular content audits - Review all website content quarterly for accuracy

4. Schema markup - Use dateModified, priceValidUntil, and other structured data

Third-party management:

1. Update profiles quarterly - Review sites, directories, comparison tools

2. PR for major changes - Major updates get press releases

3. Wikipedia maintenance - If you have a page, keep it current

Monitoring:

1. Weekly AI brand queries - Catch issues early

2. Track what sources AI cites - These are your priority updates

3. Alert system - Am I Cited or manual tracking

The mindset: Treat AI brand perception as a continuous management task, not a one-time fix.

This gives me a clear action plan. Summary:

Immediate fixes (this week):

1. Create “What’s Changed in 2024-2025” content on our site
2. Update pricing page with explicit current pricing
3. Add deprecation notice for old feature
4. Create bug resolution announcement

Short-term (this month):

1. Contact top 5 review sites with outdated info
2. Request updates to comparison articles
3. Update Wikipedia if applicable
4. Generate fresh customer testimonials

Ongoing:

1. Set up Am I Cited monitoring
2. Weekly AI brand query testing
3. Quarterly content accuracy audit
4. Version and date all major changes going forward

Key realization: We didn’t communicate changes well externally. Internal we knew the feature was deprecated, but we never announced it publicly. That’s on us.

Going forward, every major change gets a public announcement that creates a citable source.

Legal perspective worth mentioning:

When AI statements are legally problematic:

• False claims about security/compliance
• Incorrect health/safety information
• Defamatory comparisons
• Trademark misuse

Your options:

1. Document the AI response (screenshots with dates)
2. Trace the likely source of misinformation
3. Address the source first (more effective than AI company contact)
4. For severe issues, legal letter to source publisher

The reality: Courts haven’t fully addressed AI liability for misinformation yet. Your best protection is prevention and quick correction, not litigation.

For regulated industries: Make absolutely sure your own content is compliance-reviewed. AI may cite it in contexts you didn’t anticipate.

Don’t forget the customer service angle.

If AI is telling prospects inaccurate information, your sales and support teams are dealing with the fallout.

What we did:

1. Briefed customer-facing teams on AI misinformation issues
2. Created response templates for when customers cite AI incorrectly
3. Tracked instances of customers mentioning AI research
4. Used customer feedback to identify AI issues we hadn’t caught

Example template: “Thank you for your research! Just to make sure you have the most current information - [corrected fact]. Our website at [link] has the latest details. AI systems sometimes reference older information, so we always recommend checking our official site for the most accurate, up-to-date info.”

The data: About 8% of our inbound inquiries now reference AI research. That number is growing. This is real customer impact.

Looking forward - this problem is only going to grow.

The trend: More people using AI for research, more decisions influenced by AI answers, more brand perception shaped by AI synthesis.

What forward-thinking brands are doing:

1. Dedicated AI reputation monitoring - Not just social listening, AI listening
2. Proactive content strategy - Creating citable content for key brand facts
3. Multi-source consistency - Ensuring all web properties say the same thing
4. Regular AI audits - Monthly systematic brand query testing

The competitive advantage: Brands that manage AI reputation well will have more accurate, positive AI representation. Brands that ignore it will lose control of their narrative.

This is the new battleground for brand reputation. Get ahead of it now.