Discussion Reputation Management Brand Monitoring

How do you manage brand reputation when AI is saying things about you that you can't control?

BR
BrandManager_Rachel · Brand Reputation Manager
· · 79 upvotes · 9 comments
BR
BrandManager_Rachel
Brand Reputation Manager · January 5, 2026

We have a reputation problem we can’t fix the traditional way.

The situation:

  • ChatGPT is citing an outdated news article about a product issue we resolved 2 years ago
  • Perplexity positions us as “budget alternative” when we’re actually premium
  • Google AI Overviews mention a competitor lawsuit that was dismissed
  • We can’t “edit” what AI says about us

Questions:

  1. How do you manage reputation when AI synthesizes from sources you don’t control?
  2. What actually works to change AI outputs?
  3. How do you monitor what AI is saying at scale?
  4. Is this even fixable or do we just accept it?

Traditional reputation management playbooks don’t work here.

9 comments

9 Comments

AE
AIReputation_Expert Expert AI Reputation Consultant · January 5, 2026

This is the biggest unaddressed challenge in brand management right now. Here’s the reality:

Why traditional reputation management fails:

Traditional ApproachWhy It Fails for AI
Push down negative resultsAI synthesizes, doesn’t rank
Delete or delist contentAI trained on historical data
Respond to reviewsAI pulls from aggregated sources
PR crisis managementCan’t issue corrections to AI

What actually works:

  1. Source flooding - Create overwhelming positive content on authoritative sites that AI prioritizes
  2. Entity correction - Update Wikipedia, Wikidata, Google Knowledge Panel with accurate information
  3. Third-party validation - Get corrective coverage in publications AI trusts
  4. Structured data accuracy - Ensure your schema markup is current and comprehensive

The timeline reality:

  • Perplexity (live search): Changes can appear in hours/days
  • ChatGPT Search: Days to weeks (depends on Bing indexing)
  • Google AI Overviews: Days to weeks
  • ChatGPT base model: Months (until next training cycle)

The hard truth:

You can’t edit AI. You can only influence what it learns from.

BR
BrandManager_Rachel OP · January 5, 2026
Replying to AIReputation_Expert
So if ChatGPT’s base model has outdated info, we’re stuck until their next training update? That’s months of damage.
AE
AIReputation_Expert Expert · January 5, 2026
Replying to BrandManager_Rachel

Yes, but there’s nuance:

The multi-model reality:

Most AI platforms now use hybrid approaches:

  • Base model (training data) + Live search (RAG)

When ChatGPT uses web search, it can find current information. The key is ensuring:

  1. Your corrective content is well-indexed
  2. It appears on authoritative sources AI trusts
  3. It explicitly addresses the outdated information

Tactical approach for your situation:

  1. The resolved product issue:

    • Publish a detailed resolution case study on your site
    • Get coverage in industry publications
    • Update Wikipedia if applicable
    • Create FAQPage schema with the resolution

  2. The “budget alternative” positioning:

    • Audit why AI thinks this (price mentions? review positioning?)
    • Create comparative content showing premium positioning
    • Get analyst coverage positioning you correctly
    • Update third-party profiles (G2, Capterra) with accurate positioning

  3. The dismissed lawsuit:

    • Ensure the dismissal is prominently covered
    • Create press release with full outcome
    • Update any Wikipedia mention with current status
    • Monitor for outdated sources still appearing

The goal:

Make correct information so prevalent and authoritative that AI can’t ignore it.

PD
PRCrisis_Director Crisis Communications Director · January 5, 2026

Traditional PR perspective with AI adaptations:

What’s changed:

Old model: Control the narrative through media relationships New model: Influence the sources AI learns from

Our adapted crisis playbook:

  1. Monitor all AI platforms - Not just Google mentions
  2. Identify source origin - Where is AI getting bad info?
  3. Create authoritative counter-content - On sites AI trusts
  4. Leverage structured data - Make corrections machine-readable
  5. Build positive third-party coverage - Overwhelm negatives

Specific tactics that worked:

Client had similar outdated product issue appearing in ChatGPT:

Week 1: Identified sources AI was citing (old TechCrunch article) Week 2: Published resolution story on own site + Medium + LinkedIn Week 3: Pitched update story to TechCrunch (they updated original article) Week 4: Created comprehensive FAQ with schema markup Week 6: ChatGPT Search started showing corrected information

Key insight:

The original source matters most. If you can get the original negative source updated or corrected, that propagates faster than creating new competing content.

RP
ReputationMonitoring_Pro · January 4, 2026

Monitoring perspective - you can’t fix what you can’t see:

Our monitoring setup:

We track brand mentions across:

  • ChatGPT (multiple prompts daily)
  • Perplexity (multiple prompts daily)
  • Claude (weekly)
  • Google AI Overviews (daily)
  • Gemini (weekly)

What we monitor:

MetricWhy It Matters
Mention frequencyAre we appearing at all?
SentimentPositive, neutral, negative?
Citation accuracyIs info correct?
Source attributionWhere is AI pulling from?
Competitor mentionsHow are we positioned vs. them?

The prompts we use:

  • “[Brand name] review”
  • “Is [brand] good?”
  • “Best [category] companies”
  • “[Brand] vs [competitor]”
  • “Problems with [brand]”
  • “[Brand] controversy”

Tools we use:

Key finding:

AI responses vary significantly based on prompt phrasing. You need to test many variations to understand your full reputation landscape.

WE
Wikipedia_Editor Expert · January 4, 2026

Wikipedia perspective - this matters more than most realize:

Why Wikipedia is critical:

AI systems heavily weight Wikipedia content. It’s in their training data, and many use it for entity verification. If your Wikipedia page is wrong or outdated, AI will perpetuate those inaccuracies.

What you can and can’t do:

Can do:

  • Update factual inaccuracies with citations
  • Add recent information (funding, acquisitions, leadership)
  • Correct outdated product descriptions
  • Add resolution to past controversies

Can’t do:

  • Remove negative but accurate information
  • Add promotional language
  • Create page if not notable enough
  • Edit without disclosing conflicts of interest

Best practice:

  1. Request updates through Wikipedia’s edit request process
  2. Disclose any affiliation with the company
  3. Provide reliable, third-party sources for all changes
  4. Focus on factual corrections, not reputation spinning

The impact:

We’ve seen Wikipedia updates propagate to AI responses within 2-4 weeks for live-search AI and 3-6 months for base model updates.

Also update:

  • Wikidata (structured data Wikipedia uses)
  • Google Knowledge Panel
  • Crunchbase, LinkedIn company page
  • Industry directories
CV
ContentStrategy_VP VP Content Strategy · January 4, 2026

Content strategy approach to reputation:

The fundamental shift:

Traditional: Create content for customers AI era: Create content for customers AND for AI systems to cite

Content that influences AI reputation:

  1. Definitive brand page - Comprehensive “About Us” with current, accurate information
  2. FAQ schema - Address common questions (including reputation concerns)
  3. Case studies - Demonstrate positive outcomes
  4. Leadership content - Expert perspectives on industry topics
  5. Third-party coverage - Earn mentions on authoritative sites

For specific reputation issues:

Create content that directly addresses the concern:

  • “How We Resolved [Issue]” - Full transparency
  • “[Brand] Product Quality Standards” - Counter “budget” perception
  • “Company Updates: [Lawsuit Outcome]” - Clear resolution

The structure matters:

AI extracts clear, direct statements. Write content with extractable quotes:

Good: “In 2024, [Brand] resolved the product quality issue by implementing a new QC process, resulting in 99.8% customer satisfaction.”

Bad: “We’ve made many improvements over the years and continue to focus on quality.”

The first version can be quoted by AI. The second is too vague to cite.

BR
BrandManager_Rachel OP Brand Reputation Manager · January 4, 2026

This is exactly what I needed. Here’s my action plan:

Immediate actions (Week 1-2):

  1. Set up systematic monitoring

    • Am I Cited for daily tracking
    • Manual checks with varied prompts
    • Document current AI representations

  2. Identify sources

    • Where is AI getting wrong information?
    • Which sources are most influential?

Short-term fixes (Week 3-8):

  1. Update authoritative sources

    • Wikipedia edits (with proper disclosure)
    • Google Knowledge Panel corrections
    • Third-party profile updates

  2. Create corrective content

    • Resolution case study for product issue
    • Premium positioning content
    • Lawsuit dismissal documentation

  3. Pitch corrections to original sources

    • Contact TechCrunch about outdated article
    • Reach out to other citing publications

Ongoing strategy:

  1. Build positive third-party presence

    • Analyst coverage
    • Industry publication features
    • Review platform optimization

  2. Monitor and iterate

    • Weekly AI response tracking
    • Adjust strategy based on changes

Success metrics:

  • AI sentiment shift from negative to neutral/positive
  • Reduction in outdated information appearing
  • Improved positioning vs. competitors
  • Citation accuracy improvement

The mindset shift:

AI reputation is influence, not control. We can’t edit what AI says, but we can overwhelm it with accurate, authoritative information.

Thanks everyone for the practical strategies!

Have a Question About This Topic?

Get personalized help from our team. We'll respond within 24 hours.

Frequently Asked Questions

How do you manage reputation in AI search?
AI reputation management involves monitoring what AI platforms say about your brand, ensuring accurate information across authoritative sources, building positive third-party presence, and creating content that counters inaccurate AI representations. Unlike traditional reputation management, you can’t directly control AI outputs.
Can you fix inaccurate AI information about your brand?
You can influence but not directly fix AI outputs. Strategies include: updating Wikipedia and knowledge panels, publishing corrections on authoritative sites, building positive third-party mentions, ensuring structured data accuracy, and creating comprehensive content that AI systems can cite as authoritative.
How do AI platforms form opinions about brands?
AI systems synthesize information from training data and live web sources. They pull from Wikipedia, news sites, review platforms, industry publications, and your own content. Negative reviews, outdated articles, or misinformation can appear in AI responses, making proactive reputation management essential.
What metrics should you track for AI reputation?
Track: brand mention frequency, sentiment analysis (positive/neutral/negative), citation accuracy, competitive positioning, share of AI voice, and specific inaccuracies in AI responses. Regular monitoring across ChatGPT, Perplexity, Claude, and Google AI Overviews is essential.

Monitor Your Brand Reputation in AI

Track how AI platforms represent your brand. Monitor mentions, sentiment, and citations across ChatGPT, Perplexity, and Google AI Overviews.

Start Monitoring Learn More

Learn more