The execution gap

Every AI visibility tool on the market tells you where you stand. None of them tell you what to do next. We analyzed the 20 most-cited sources in AI responses about visibility tools, and not a single one provided a post-audit action plan.

That's a problem — whether you're a founder reading your own report or an agency presenting findings to a client. Knowing your visibility score is 23% is useless without a roadmap to get it to 60%. Here's that roadmap. (For the full 90-day version, see our AI visibility playbook.)

Step 1: Fix factual errors first (Week 1)

Errors are the highest-impact, lowest-effort fix. If AI is telling customers your product costs $99 when it actually costs $29, fixing that one error can change the entire recommendation. (Our research on AI hallucinations found that 72% of brands have at least one factual error in chatbot responses.)

How to do it:

  • Review your audit report for every factual error flagged
  • Trace each error to its likely source (review sites, outdated blog posts, your own site)
  • Fix the source: update the listing, correct the page, or publish a correction
  • For errors on your own site: make sure pricing and features are in plain HTML, not behind JavaScript

Quick win: Fixing source errors is usually the fastest path to improvement — correcting outdated pricing or feature information on third-party listings can show results within weeks, because AI models pull from these sources in real time.

Step 2: Add structured data to your site (Week 1–2)

Schema.org structured data helps AI understand your content. Pages with structured data tend to surface more often in AI-generated summaries, according to emerging research.

Priority schema types:

  • Organization — your brand name, URL, description, contact info
  • Product + Offer — product name, description, pricing in plain numbers
  • FAQPage — common questions and answers about your product
  • Article — for blog posts and content pages

Step 3: Update third-party listings (Week 2–3)

AI models heavily weight third-party sources — G2, Capterra, TrustRadius, industry comparison sites. Many models use retrieval-augmented generation (RAG) to pull live data from these listings. If your listing on G2 says "starting at $99/mo" but you now offer a $29 plan, AI will repeat the wrong price.

Checklist:

  • G2: Update pricing, features, screenshots, and description
  • Capterra: Same updates + verify category placement
  • TrustRadius: Refresh product profile and pricing
  • Industry blogs: Reach out to authors of comparison posts with updated info
  • Your Wikipedia page (if applicable): Correct any outdated information

Step 4: Create comparison content (Week 3–4)

AI answers recommendation queries by synthesizing comparison content. Our audit of AI chatbot recommendations showed that brands with their own comparison pages are significantly more likely to be cited. If you don't have your own "[Your Brand] vs [Competitor]" and "Best [category] tools" content, you're letting competitors and third parties control your narrative.

Content to create:

  • A "vs" comparison page for each top competitor (e.g., "Acme CRM vs HubSpot")
  • A "Best [your category]" roundup that honestly includes competitors (AI trusts balanced content more than one-sided pitches)
  • A clear pricing page with plain HTML tables — no JavaScript toggles or interactive calculators
  • A FAQ page answering the exact queries buyers ask AI

Step 5: Monitor and re-audit (Week 4+)

AI models update at different rates. ChatGPT's training data refreshes every few months. Perplexity searches the live web. Claude and Gemini fall somewhere in between. After implementing fixes, re-audit in 4–6 weeks to measure progress.

What to re-test: Run the same queries that surfaced errors in your original audit. Use the exact prompts — "What does [Brand] cost?", "Is [Brand] good for [use case]?" — so you can compare results directly.

Where to check first: Start with Perplexity, which searches the live web and will reflect source fixes fastest. Then check ChatGPT — if errors persist there, it likely means the training data hasn't refreshed yet (this is normal and can take 2–3 months). Also spot-check Claude and Gemini for completeness.

What good looks like at Week 6: Factual errors from your original audit should be resolved on at least 2–3 platforms. Your brand should appear in recommendation queries where it was previously absent. If errors are still showing up across all platforms after 6 weeks, that's a signal the source content hasn't been updated correctly — go back to Steps 1 and 3 and verify the fixes actually went live.

Full timeline

Action Effort Timeline Expected Impact
Fix factual errors Low–Medium Week 1 +10–15% visibility
Add structured data Medium (dev needed) Week 1–2 +5–10% visibility
Update 3rd-party listings Medium Week 2–3 +10–20% visibility
Create comparison content High Week 3–4 +15–25% visibility
Re-audit Low Week 6–8 Measure + iterate

Typical result: Brands that follow this full playbook see visibility improve from 15–25% to 50–65% within 6–8 weeks. The biggest gains come from fixing errors (free) and updating third-party listings (free but time-consuming).

This article gives you the framework. A Metricus report gives you the specific errors, exact sources, and priority order for your brand. One-time purchase from $99. No subscription required.

Sources: Structured data impact on AI summaries: emerging industry research, 2025–2026. Learn more about how we measure AI visibility.

Related guides in this series

This action plan is the starting point. Each step has a deeper companion guide:

For background on how AI decides what to recommend, start with our complete guide to AI visibility. If you want to run a quick technical check before ordering a full report, try our free AI visibility audit.