AI Is Answering Questions About Your Industry Every Day. Why Are Your Competitors Getting the Credit?

AI-driven search experiences are increasingly answering industry questions directly inside the interface, which means buyers can form opinions and make decisions without ever visiting your website. This article explains why that shift is creating a new competitive metric I call Share of Model: how often AI systems rely on your brand (not your competitors) when explaining your category. If you are not being cited or referenced in these answers, you are losing authority and demand invisibly. You will learn what is changing, why “wait and see” is risky, and how to use Answer Engine Optimization (AEO) to earn credit and reduce hallucination risk.

---

Key Takeaways

• In a March 2025 dataset of 68,879 Google searches, users clicked a traditional result 8% of the time when an AI summary appeared vs 15% when it did not. (Pew Research Center, July 22, 2025)

• Those same users clicked a source link inside the AI summary only 1% of the time (March 2025 behavior). (Pew Research Center, July 22, 2025)

• For informational queries with AI Overviews, organic CTR dropped from 1.76% to 0.61% from mid-2024 to 2025, according to reporting on Seer’s dataset. (Search Engine Land, Nov 4, 2025)

• Being cited matters: Seer found that when you are cited in an AI Overview, you see 35% more organic clicks and 91% more paid clicks vs. not being cited (as of Nov 2025). (Seer Interactive, Nov 4, 2025)

• NIST defines “confabulation” (hallucinations) as confidently stated but false output, which is why AEO is now a brand risk issue, not just a growth tactic. (NIST AI 600-1, July 2024)

The Conversations You Cannot See Anymore

The old loop: search, click, evaluate

For years, your visibility was measurable because discovery happened on your assets.

Even if you did not rank #1, you could still win attention because users opened tabs, compared sources, and browsed.

The new loop: ask, get an answer, move on

Now, a growing share of “research” ends inside the answer.

Pew Research Center’s March 2025 analysis (based on browsing data from 900 U.S. adults and a dataset of 68,879 Google searches) found AI summaries appeared on 18% of searches. (Pew Research Center, July 22, 2025)

When the AI summary was present, users clicked a traditional result only 8% of the time, versus 15% without a summary. (Pew Research Center, July 22, 2025)

That is the “silent conversation” problem: intent is expressed, resolved, and disappears off-site.

Credit Is Being Assigned in the Answer Layer

AI summaries are short, which makes attribution scarce

Pew reported the median AI summary length was 67 words in their March 2025 snapshot. (Pew Research Center, July 22, 2025)

Sixty-seven words does not leave room for nuance. It leaves room for a narrative.

So the question becomes brutally simple:

When AI explains your category, whose narrative is it repeating?

Links still exist, but users barely touch them

Pew found users clicked a link inside the AI summary only 1% of the time (March 2025 behavior). (Pew Research Center, July 22, 2025)

This creates a new kind of competition:

• You are competing for credit, not only traffic.
• You are competing for framing, not only rankings.
• You are competing for being the cited source even when the click never comes.

Google has made this mainstream

Google began rolling out AI Overviews broadly in the U.S. in May 2024, with a stated expectation to bring them to over a billion people by the end of that year. (Google, May 14, 2024)

If your strategy still assumes the blue link is the primary interface, you are optimising for yesterday’s surface area.

”Share of Model” Is the Metric Your Dashboard Does Not Show

Here is the simplest way to define it:

Share of Model = how frequently an AI system relies on your brand to construct answers about your category.

This replaces what Share of Voice used to represent, because voice is no longer distributed across ten results. It is concentrated inside one answer.

Why this turns into a competitor problem fast

If AI consistently references a rival when answering “best [category] for [use case]” questions, that rival becomes the default authority.

You do not just lose a click. You lose the recommendation.

And because this happens outside your analytics, it can look like nothing is wrong until pipeline starts feeling “off.”

Old SEO metrics vs AEO metrics

What you tracked	Why it breaks in AI answers	What to track with AEO
Keyword rank	Rank does not guarantee inclusion in summaries	Citation rate by topic and prompt
Organic sessions	Intent can be satisfied with no visit	Share of Model vs competitors
CTR	CTR is structurally suppressed	”Cited vs not cited” performance split
Share of Voice	Voice is not the primary output anymore	Share of Model (brand reliance)
Reputation monitoring	Brand perception now forms inside AI answers	ChatGPT brand monitoring + citation trails

This is the shift behind the surge in “AI narrative control” and “control AI search results” searches. People are realising the conversation moved.

The Numbers Are Ugly, and They Get Uglier When You Are Not Cited

Seer Interactive’s analysis (as of November 2025) found that citation status changes outcomes.

They reported that when you are cited in an AI Overview, you get 35% more organic clicks and 91% more paid clicks compared with when you are not cited. (Seer Interactive, Nov 4, 2025)

They also published CTR ranges showing that, in Q3 2025, queries where AI Overviews were present but you were not cited had an organic CTR of 0.52%, while queries where you were cited had an organic CTR of 0.70% (reported as of Nov 2025). (Seer Interactive, Nov 4, 2025)

Separately, Search Engine Land’s reporting on Seer’s data noted organic CTR for AI Overview queries dropped from 1.76% to 0.61% since mid-2024, and paid CTR fell from 19.7% to 6.34%. (Search Engine Land, Nov 4, 2025)

This is why “recovering traffic from AI Overviews” is becoming a category of its own.

Brand Reputation in AI Is Now an Operational Risk

If you are not present, models still answer.

Sometimes they guess.

NIST’s Generative AI Profile defines confabulation as “confidently stated but erroneous or false content,” commonly called hallucinations. (NIST AI 600-1, July 2024)

Now apply that to your business:

• “What does [Brand] charge?”
• “Does [Brand] integrate with [X]?”
• “Is [Brand] compliant with [Y]?”
• “Is [Brand] legit or a scam?”

If the model is fuzzy on your facts, you have an LLM hallucination management problem.

AEO is the proactive version of fixing incorrect information in AI answers: you publish structured, authoritative truth so the system has less room to invent.

”Wait and See” Is How Competitors Lock In Default Authority

Gartner predicted in February 2024 that by 2026, traditional search engine volume will drop 25%, with search marketing losing share to AI chatbots and virtual agents. (Gartner, Feb 19, 2024)

Whether it is 25% or 15% is not the point.

The point is that AI answers are becoming a primary interface for discovery.

And AEO is not a one-time fix. It is cumulative:

• Every clean, citable page you publish increases the chance you become the referenced source.
• Every month you ignore it increases the chance your competitor becomes the default explanation.

You can claw back rankings.

It is harder to claw back the narrative once buyers start hearing the same competitor name in every answer.

AEO: A Defensive Playbook to Win the Credit

This is the part most teams skip. They accept invisibility because they cannot see it.

Here is a practical AEO workflow you can run without overthinking it.

Step 1: Map the questions that shape buying decisions

List 25–50 questions prospects ask before they buy (sales calls, demos, onboarding, support).

Group them into three buckets: problem-aware, solution-aware, vendor-selection.

Prioritize the 10 questions most tied to revenue.

This is your AEO prompt universe.

Step 2: Build “citation-ready” answers (not blog posts)

For each priority question, create a page that contains:

• a direct 2–3 sentence answer at the top
• a table or bullets that clarify trade-offs
• dated facts with sources
• a visible author and last updated date

Your job is to make the page easy for an answer engine to extract and trust.

Step 3: Publish a single canonical “About” truth source

If you care about corporate identity in large language models, you need one page that states your facts plainly:

• what you do
• who you do it for
• what you do not do
• product constraints and pricing anchors (if you can share)
• links to official docs
• last updated date

This is how you reduce guesswork.

Step 4: Produce one piece of original data per quarter

Answer engines summarise opinions.

They cite data.

If you want to be referenced, you need assets that look like sources:

• benchmarks
• survey results
• pricing analysis
• category definitions with evidence

Treat this as brand defense, not content marketing.

What “ChatGPT Brand Monitoring” Should Look Like in Practice

Most teams do this in an ad hoc way: they test a prompt once, react emotionally, then move on.

If you want control, you need a repeatable system.

A weekly AEO visibility routine

1. Enter your top 50 prompts into a tracker.
2. Run them weekly across the main answer surfaces your buyers use.
3. Record three outputs for each prompt: (a) who is mentioned, (b) who is cited, (c) the framing.
4. Flag any incorrect claims about your pricing, features, or reputation.
5. Update the pages that should have been cited, then rerun the same prompt set.

This is how you stop flying blind.

It is also how you start measuring Share of Model instead of guessing it.

Where Genrank Fits

AEO is not only “write better content.”

It is oversight.

The problem is that most teams have no tooling to answer basic questions like:

• Which prompts is AI using to recommend my competitor?
• Where am I missing from the citation list?
• Which pages are actually earning references?
• Where is the model repeating outdated or incorrect information about us?

Genrank is built to turn those questions into a dashboard:

• track the questions being asked
• surface who is getting cited
• quantify your Share of Model against competitors
• show where you are losing credit, and why

If you are serious about AI narrative control, you need measurement that matches the new interface.

Because AI will answer the question either way.

The only variable you can control is whether it builds the answer with you, or with your competitor.

---

Join the waitlist to get your first Share of Model baseline and an AEO-focused visibility audit.