The CCO's Guide to Generative Engine Optimization

Chief Communications Officers have always been responsible for how people understand the company.

Now they must also manage how AI systems understand it.

Customers, journalists, investors, employees, policymakers, partners, and other stakeholders increasingly use ChatGPT, Claude, Gemini, Perplexity, Grok, Microsoft Copilot, and Google’s AI search experiences to research companies, compare competitors, understand controversies, evaluate products, and form opinions.

These systems do more than find webpages.

They interpret information.

They compress years of company announcements, news coverage, research, product documentation, regulatory records, reviews, social discussion, and expert commentary into a direct answer.

That answer may describe the company as:

• A market leader

• An emerging challenger

• A legacy incumbent

• A category creator

• A trusted brand

• A controversial organization

• A product innovator

• A lower-cost alternative

• A company struggling to differentiate itself

• A business defined by an outdated narrative

This makes generative engine optimization, or GEO, a communications responsibility.

SEO teams remain critical because content must be accessible, crawlable, indexable, and technically understandable.

But the central questions of GEO are not merely technical:

• What does the company stand for?

• Which narratives define its reputation?

• Which claims are credible?

• Which sources have authority?

• What do independent observers believe?

• How is the company positioned against competitors?

• Which perceptions are becoming durable?

• What evidence would cause the interpretation to change?

Those are communications questions.

The CCO’s role is not to optimize isolated prompts or manufacture favorable answers.

It is to build a credible information environment in which the strongest available evidence supports an accurate, favorable, and strategically important perception of the company.

What is generative engine optimization?

Generative engine optimization is the practice of improving how a company, brand, product, executive, or issue is represented in AI-generated answers.

It combines:

• Narrative strategy

• Earned-media strategy

• Owned-content strategy

• Source authority

• Citation intelligence

• Technical SEO

• Entity consistency

• Message development

• Reputation management

• Competitive positioning

• AI perception measurement

• Evidence-based communications planning

GEO is sometimes described narrowly as the effort to get a website cited by ChatGPT or another AI system.

Citations matter, but they are only one part of the discipline.

A company can be cited frequently and still be portrayed negatively.

It can appear in many answers without being associated with its priority messages.

Its own website can be cited for basic facts while stronger independent sources define its broader reputation.

A complete GEO program therefore asks:

1. What do AI systems currently believe about the company?

2. Which narratives dominate that perception?

3. Which sources and claims support the interpretation?

4. How does perception differ across models?

5. How does current web retrieval change the answer?

6. Which messages are present, absent, or contradicted?

7. How is the company positioned against competitors?

8. Which inaccuracies or outdated beliefs persist?

9. What evidence is missing?

10. What communications actions could improve the outcome?

11. Is perception changing over time?

The objective is not merely to increase AI visibility.

The objective is to create a stronger and more durable brand perception.

Why GEO belongs with communications

Communications teams already manage the inputs that shape reputation.

They influence:

• Corporate narratives

• Executive positioning

• Earned media

• Issues and crisis response

• Product storytelling

• Research and thought leadership

• Stakeholder messaging

• Competitive framing

• Customer evidence

• Public claims

• Source relationships

• The consistency of company information

These inputs now affect two audiences:

1. People who read and interpret the information directly

2. AI systems that retrieve, summarize, compare, and reproduce it

The same article may influence a journalist, investor, customer, and AI-generated answer.

The same unclear product description may confuse both human stakeholders and retrieval systems.

The same authoritative third-party validation may strengthen both public credibility and AI perception.

The CCO is therefore no longer managing only earned-media outcomes.

The CCO is helping manage the evidence layer from which both human and machine perception develops.

The information environment has become the interface

Traditionally, stakeholders encountered a brand through several separate channels:

• News articles

• Search results

• Company webpages

• Analyst reports

• Social posts

• Reviews

• Investor materials

• Conversations with employees or customers

AI systems increasingly combine these channels into one interface.

A user can ask:

• What is this company known for?

• Is it trustworthy?

• What are its main strengths and weaknesses?

• How is it using AI?

• What controversies has it faced?

• How does it compare with competitors?

• Is it a good employer?

• Is its product appropriate for my company?

• Which sources support its claims?

• What has changed recently?

The system may produce a synthesized answer in seconds.

This creates a new reputational dynamic.

The stakeholder may never visit the company’s homepage.

They may never read the full news article.

They may never see the press release.

They may interact only with the AI system’s compressed interpretation of those sources.

Communications teams must therefore understand not only whether the company’s messages were published, but whether those messages survive synthesis.

From media coverage to machine interpretation

Traditional media measurement focuses on outputs such as:

• Mention volume

• Reach

• Impressions

• Share of voice

• Sentiment

• Publication tier

• Message pull-through

• Social engagement

• Spokesperson inclusion

These remain useful, but they do not fully reveal how coverage contributes to AI perception.

For example, a company may earn extensive positive coverage around a product launch.

The communications team may report:

• 400 articles

• 90% positive sentiment

• Strong Tier 1 coverage

• High message pull-through

• Significant social engagement

But AI systems may still describe the company using an older narrative because:

• The new coverage did not make the brand central enough

• Most articles repeated the same press release

• The strongest claims lacked independent evidence

• The company’s permanent product pages were not updated

• Authoritative publications framed the launch differently

• Competitor evidence remained stronger

• The coverage did not clearly connect the product to the broader corporate narrative

• The newer information had not yet become stable across models

GEO extends communications measurement from:

How much coverage did we earn?

To:

What perception did the evidence create for people and AI systems?

The core responsibility: control the narrative

A company cannot directly edit every AI-generated answer.

It can control the strength of the narrative and evidence environment from which those answers are constructed.

A narrative becomes more likely to appear when it is supported by:

• Clear first-party information

• Authoritative earned media

• Independent expert validation

• Strong brand prominence

• Specific facts

• Consistent claims

• Current evidence

• Customer proof

• Credible research

• Relevant social and community signals

• Accessible canonical sources

The lever is not repetition alone.

Publishing the same message across numerous low-authority channels does not create the same result as independent, mutually reinforcing evidence from sources with real authority.

A company gains control when the desired interpretation becomes the most credible conclusion available.

That is narrative control in the age of AI.

Start with the company’s priority narratives

A GEO program should not begin with a list of thousands of prompts.

It should begin with the narratives most important to the business.

Examples may include:

• The company is an AI leader.

• The company serves more than its legacy product category.

• The brand is trusted in moments of crisis.

• The business is expanding into new markets.

• The company has the strongest enterprise offering.

• The organization is committed to responsible innovation.

• The company is addressing a major societal challenge.

• The brand is a category creator rather than a follower.

• The company is financially stable.

• The organization is responding effectively to a reputational issue.

For each narrative, communications leaders should define:

Desired perception

What should stakeholders and AI systems conclude?

Undesired perception

What interpretation would create strategic or reputational risk?

Supporting claims

Which facts establish the desired perception?

Evidence

Which owned, earned, expert, customer, regulatory, or research sources support those claims?

Competitors

Which companies are competing to own the same narrative?

Stakeholders

Who is most likely to ask questions about the issue?

Measurement criteria

What would demonstrate that the perception is strengthening or weakening?

The narrative becomes the unit of strategy and measurement.

Prompts are used to test it.

The three layers a CCO should measure

A complete GEO program should examine three connected layers.

1. LLM perception without web retrieval

Testing without visible live web retrieval helps reveal the brand perception expressed by the model under the tested conditions.

This may identify:

• Durable brand associations

• Established narratives

• Persistent misconceptions

• Outdated beliefs

• Cross-model disagreement

• Competitive positions that recur without current retrieval

• Messages that appear to have become broadly associated with the company

This should be treated as an observed output, not a complete view into the model’s training data or internal reasoning.

AI companies do not publish a comprehensive explanation of every factor that contributes to each response.

The communications value lies in observing which perceptions persist without current retrieval.

2. LLM perception with web retrieval

Testing with retrieval shows how current accessible evidence changes the answer.

This can reveal:

• Whether recent developments appear

• Whether outdated facts are corrected

• Which sources are cited

• Which claims become more prominent

• Whether current coverage strengthens or weakens perception

• Whether competitors benefit from stronger retrievable evidence

• Whether the intended message survives current synthesis

• Whether the answer becomes more or less favorable

• Which URLs recur across repeated runs

The difference between web-on and web-off results is strategically important.

A new narrative may perform well with retrieval but remain absent without it, suggesting that the perception is emerging but not yet durable.

A brand may perform well without retrieval but worse with it, indicating that current coverage or accessible evidence is weakening an established perception.

3. Earned-media narrative analysis

The third layer examines the full relevant coverage environment.

It should evaluate:

• Dominant narratives

• Brand prominence

• Brand-centric favorability

• Source authority

• Message pull-through

• Factual specificity

• Independent corroboration

• Narrative momentum

• Competitive framing

• Conflicting evidence

• Which claims recur across authoritative sources

• Which narratives may become durable AI beliefs

This broader analysis matters because visible citations show only which sources appeared in the tested answers.

The earned-media corpus shows the larger body of evidence shaping public perception and available for future retrieval.

Together, these three layers connect:

• Current model perception

• Current retrievable perception

• The underlying news and evidence environment

What a CCO should measure

A useful GEO scorecard should go beyond mentions and citation counts.

Narrative presence

Is the company meaningfully associated with the strategic narrative?

A brand may appear without owning the intended idea.

For example, a company can appear in an answer about artificial intelligence while being described as a late adopter rather than a leader.

Brand prominence

Is the company central to the answer?

Prominence can include:

• Placement in the opening

• Position in a list

• Amount of substantive discussion

• Inclusion in the conclusion

• Whether the company defines the category

• Whether it is a primary or secondary example

Brand-centric favorability

How is the company itself positioned?

This differs from general sentiment.

An answer can discuss a negative industry environment while portraying the brand positively for responding effectively.

Favorability should classify the brand’s position as:

• Positive

• Neutral

• Mixed

• Negative

Message pull-through

Do the company’s priority messages appear?

Classify each message as:

• Explicit

• Implied

• Absent

• Contradicted

This reveals whether communications strategy is surviving AI synthesis.

Factual accuracy

Are the material claims:

• Accurate

• Outdated

• Incomplete

• Unsupported

• Incorrect

Accuracy should be evaluated at the claim level.

Competitive position

How is the company positioned relative to competitors?

Measure:

• Leadership

• Differentiation

• Recommendation

• Use-case ownership

• Strengths

• Weaknesses

• Category framing

• Comparative evidence

Citation quality

Which sources visibly support the answer?

Evaluate:

• Source authority

• Claim relevance

• Freshness

• Brand prominence

• Factual specificity

• Independence

• Whether the citation supports the associated statement

• Recurrence across models and runs

Cross-model consistency

Do ChatGPT, Claude, Gemini, Perplexity, and Grok express the same underlying perception?

The wording does not need to be identical.

The conclusion should be substantially consistent.

Cross-run stability

Does the perception persist across repeated tests?

A result that appears once is less meaningful than one that recurs across prompt variations and repeated runs.

Retrieval resilience

Does the perception hold when web retrieval is turned on and off?

This can reveal whether the narrative is:

• Durable

• Emerging

• Outdated

• Dependent on current retrieval

• Vulnerable to conflicting evidence

Earned-media evidence strength

How strongly does the broader coverage environment support the narrative?

This should account for:

• Authority

• Prominence

• Favorability

• Specificity

• Independence

• Corroboration

• Freshness

• Momentum

• Consistency

• Competitive framing

Narrative drift

Is the perception:

• Strengthening

• Weakening

• Correcting

• Fragmenting

• Hardening

• Fading

These dimensions can be combined into an LLM Perception Score.

Build an LLM Perception Score

Executives need a clear signal.

A composite LLM Perception Score can summarize the strength and quality of the company’s AI perception, provided the underlying rubric is transparent.

A defensible score may combine:

The output may include:

• Overall LLM Perception Score

• Narrative-level scores

• Model-level scores

• Web-on score

• Web-off score

• Citation intelligence score

• Earned-evidence score

• Competitive-position score

• Message-pull-through score

• Confidence rating

The score is the synthesis.

The component evidence explains what the score means and what is driving it.

Add a confidence rating

The same score can represent very different levels of certainty.

A score based on five models, multiple prompt families, repeated runs, web-on and web-off testing, recurring citations, and a substantial earned-media corpus should carry greater confidence than a score based on three prompts and one response per model.

A confidence rating can account for:

• Number of models

• Number of prompt families

• Number of repeated runs

• Cross-model agreement

• Cross-run stability

• Citation recurrence

• Source quality

• Evidence volume

• Evidence freshness

• Availability of retrieval testing

• Strength of the earned-media corpus

Confidence should remain separate from performance.

A favorable but low-confidence score may indicate an emerging perception.

An unfavorable high-confidence score may indicate a durable reputational problem.

Measure narratives, not random prompts

Prompt monitoring remains useful, but prompt selection should follow narrative strategy.

For each priority narrative, use a prompt family containing several types of questions.

Factual prompts

• What does the company do?

• What products does it offer?

• Who leads the company?

• Which markets does it serve?

Category prompts

• Which companies lead this market?

• Which brands are most innovative in this category?

• What are the top platforms for this use case?

Comparative prompts

• How does the company compare with its main competitor?

• Which product is better for enterprise customers?

• What are the main differences among these brands?

Evaluative prompts

• Is the company trustworthy?

• What are its main strengths and weaknesses?

• Is the company considered innovative?

• What is the company best known for?

Narrative-specific prompts

• How is the company using AI?

• Is the company expanding beyond its traditional business?

• What role does it play in this strategic issue?

• What evidence supports its leadership position?

Adverse prompts

• What controversies has the company faced?

• What are the biggest risks associated with it?

• Why do customers criticize the product?

• What could weaken its market position?

Open-ended prompts

• Tell me about the company.

• What should I know about this brand?

• How is the company perceived?

• What defines its reputation?

The purpose is not to track every possible wording.

It is to determine whether the underlying narrative survives different ways of asking.

Repeated testing matters

AI answers can vary from one run to another.

A single answer should not become an executive conclusion.

Repeated testing helps determine:

• How consistently the company appears

• How often the desired narrative emerges

• Whether favorability is stable

• Whether competitors change

• Whether recommendations change

• Whether citations recur

• Whether factual errors persist

• Whether one response was an outlier

For high-priority citation analysis, repeated testing across 30 to 50 runs per narrative can provide a stronger view of which domains, URLs, and claims surface most often.

Every run should record:

• Model

• Product interface

• Date

• Prompt

• Retrieval condition

• Prior context

• Answer

• Citations

• Classification results

The goal is a transparent, repeatable methodology.

Visible citations are not the whole picture

Visible citations provide valuable evidence of which sources were presented in support of a tested answer.

They should not automatically be treated as a complete map of every factor involved in producing the response.

AI companies do not disclose a complete formula for every answer, and the relative role of retrieval, ranking, model behavior, conversation context, and other system components may vary.

A complete citation-intelligence program should distinguish among three layers.

Observed citation behavior

Which URLs and domains visibly appear across repeated answers?

Likely citation influence

Which sources are best positioned to shape future retrieval and citations based on:

• Authority

• Relevance

• Factual specificity

• Brand prominence

• Freshness

• Independence

• Accessibility

• Repetition

• Narrative alignment

The broader evidence environment

Which earned, owned, expert, social, regulatory, customer, and research sources collectively support the narrative?

The first is directly observable.

The second is an evidence-based assessment.

The third provides the wider context necessary to understand where perception may move next.

Why earned media matters so much

Owned content tells AI systems what the company says about itself.

Earned media provides independent evidence about whether others accept, validate, challenge, or reinterpret that position.

Authoritative journalism can:

• Confirm company claims

• Establish market significance

• Explain a product’s importance

• Define a category

• Compare competitors

• Document adoption

• Strengthen executive credibility

• Surface controversy

• Correct company messaging

• Create durable associations

Not all coverage contributes equally.

A passing mention in a broad article may have less value than one substantive story in which the brand is central.

A syndicated press release does not provide the same independent corroboration as original reporting.

A high-authority source that clearly connects the brand to a strategic narrative can be more influential than a large volume of low-quality mentions.

CCOs should therefore measure earned-media quality through:

• Source authority

• Brand prominence

• Brand-centric favorability

• Narrative relevance

• Independent reporting

• Factual specificity

• Message pull-through

• Competitive framing

• Citation recurrence

• Narrative momentum

The goal is not only more coverage.

It is stronger evidence.

Why owned content still matters

First-party content provides canonical evidence for facts the company controls.

This may include:

• Product descriptions

• Executive roles

• Research methodology

• Pricing

• Policies

• Security practices

• Corporate history

• Official announcements

• Technical documentation

• Investor information

• Customer resources

For GEO, owned content should be:

• Accurate

• Current

• Publicly accessible

• Crawlable

• Indexable

• Clearly written

• Factually specific

• Internally consistent

• Organized around a clear purpose

• Supported by evidence

• Linked from related pages

• Maintained at a permanent URL

OpenAI says public websites can potentially appear in ChatGPT Search and advises publishers that want their content considered for summaries and snippets not to block OAI-SearchBot.

Google says established SEO practices remain foundational for AI Overviews and AI Mode. Pages must meet standard Search requirements, including crawlability, indexability, and eligibility to appear with a snippet.

Technical access does not guarantee that a page will be cited.

It makes the page eligible to be considered.

The information still needs to be useful.

Communications and SEO must work together

GEO requires a partnership between communications and SEO.

SEO teams should help ensure:

• Crawler access

• Indexability

• Canonical URLs

• Internal links

• Logical site architecture

• Structured data

• Correct metadata

• Server-rendered content

• Page performance

• Search visibility

• Duplicate-content management

Communications teams should ensure:

• Narrative clarity

• Message consistency

• Source authority

• Accurate claims

• Earned-media validation

• Executive credibility

• Factual specificity

• Competitive differentiation

• Reputation context

• Alignment across earned and owned content

Neither function can succeed alone.

A strong narrative hidden in inaccessible content will have limited effect.

A perfectly optimized page containing weak, vague, or unsupported claims will also have limited effect.

Technical accessibility and evidence quality must converge.

GEO is not an AI-content strategy

Publishing large volumes of AI-generated content is not GEO.

Google’s current guidance continues to emphasize useful, original, expert-led content and warns against using generative AI primarily to manipulate search rankings.

The problem is not the use of AI in content production.

The problem is producing large amounts of low-value, duplicative, inaccurate, or interchangeable material.

Communications teams should prioritize:

• Original research

• Firsthand expertise

• Transparent methodology

• Executive insight

• Customer evidence

• Technical documentation

• Clear definitions

• Primary-source material

• Defensible analysis

• Useful synthesis

The standard is not whether a human typed every word.

The standard is whether the content adds credible evidence to the information environment.

GEO is not a collection of hacks

CCOs will encounter claims that AI perception can be solved through:

• An llms.txt file

• Special AI schema

• Exact prompt pages

• Artificial content chunking

• Mass-produced FAQs

• Secret keywords

• Paid citation networks

• Inauthentic mentions

• Guaranteed ChatGPT rankings

• Automated content volume

No single technical mechanism guarantees citations or favorable perception.

OpenAI does not promise top placement in ChatGPT Search.

Google says sites do not need special AI markup or artificially small content chunks to appear in its generative AI search experiences.

The durable work remains:

• Clear narratives

• Strong technical foundations

• Original evidence

• Independent validation

• Source authority

• Factual specificity

• Brand prominence

• Consistent information

• Accurate measurement

GEO is not about gaming AI systems.

It is about making the correct interpretation better supported than competing interpretations.

Diagnose the evidence gap

When the desired perception does not appear, communications leaders should identify why.

Common gaps include:

Narrative gap

The company has not clearly defined the perception it wants to own.

Owned-content gap

There is no canonical page establishing the facts or message.

Authority gap

The claim appears primarily in company-authored content without credible independent validation.

Prominence gap

The brand is mentioned but not central to relevant articles or pages.

Specificity gap

The company uses broad marketing language without concrete evidence.

Freshness gap

Outdated information remains more visible or authoritative than current information.

Consistency gap

Different company pages, executives, departments, or external sources make conflicting claims.

Competitive gap

A competitor has stronger evidence, clearer positioning, or greater category authority.

Retrieval gap

Strong content exists but is difficult for search and AI systems to access.

Reputation gap

A negative narrative is supported by stronger or more credible evidence than the company’s preferred interpretation.

Corroboration gap

The narrative lacks independent, mutually reinforcing support.

The action should match the gap.

Publishing more content will not solve every problem.

Turn analysis into communications action

A GEO program should lead to a clear set of evidence-based actions.

Amplify

Increase the prominence of a favorable narrative that is already well supported.

This may involve:

• Extending earned coverage

• Elevating authoritative sources

• Reusing research

• Connecting the narrative to executive commentary

• Strengthening internal links

• Making successful customer proof more visible

Clarify

Improve an important message that is present but misunderstood.

This may involve:

• Rewriting product descriptions

• Defining terminology

• Publishing a clear comparison

• Updating executive messaging

• Explaining the company’s role in a narrative

• Replacing vague claims with specific facts

Counter

Address an inaccurate, incomplete, or misleading narrative.

This may involve:

• Publishing stronger evidence

• Responding publicly

• Correcting a factual record

• Providing missing context

• Earning independent validation

• Making current information easier to retrieve

Canonicalize

Create a permanent source for an important fact or narrative.

Examples include:

• A category-definition page

• A methodology page

• A security page

• An executive biography

• A product explainer

• A research hub

• A corporate-history page

• A frequently updated issue page

Create

Develop evidence that does not yet exist.

This may include:

• Original research

• Customer studies

• Expert analysis

• Product documentation

• Technical validation

• Data reports

• Executive thought leadership

• Independent audits

Validate

Build credible third-party support for a company claim.

This may involve:

• Earned media

• Expert commentary

• Customer evidence

• Research partnerships

• Industry recognition

• Regulatory confirmation

• Independent evaluation

Correct

Update outdated or inaccurate information.

This may involve:

• Owned pages

• Public databases

• Business directories

• Executive profiles

• Partner pages

• Media corrections

• Structured information sources

Consolidate

Reduce fragmentation across duplicative or conflicting pages.

Monitor

Continue observing a narrative that is still developing.

The actions should emerge from the evidence, not from a generic GEO checklist.

A CCO operating model for GEO

A practical GEO program can be organized into seven stages.

1. Define priority narratives

Select the narratives most important to the business and reputation.

Document:

• Desired perception

• Undesired perception

• Priority messages

• Supporting claims

• Competitors

• Stakeholders

• Known evidence

• Known risks

2. Establish the baseline

Test prompt families across relevant models.

Use:

• Multiple models

• Repeated runs

• Web retrieval on

• Web retrieval off

• Neutral questions

• Comparative questions

• Adverse questions

• Open-ended questions

Measure the initial LLM Perception Score and its components.

3. Analyze the evidence environment

Review:

• Earned media

• Owned content

• Expert sources

• Customer evidence

• Social and community sources

• Regulatory records

• Research

• Competitor sources

Assess:

• Authority

• Relevance

• Prominence

• Specificity

• Freshness

• Independence

• Consistency

• Accessibility

• Narrative alignment

4. Diagnose the gap

Determine why the desired perception is not emerging.

Classify the problem as:

• Technical

• Evidentiary

• Narrative

• Competitive

• Editorial

• Reputational

• Temporal

• Source-related

5. Build the communications plan

Prioritize actions across:

• Earned media

• Owned content

• Executive positioning

• Research

• Customer evidence

• Technical SEO

• Corrections

• Competitive messaging

• Stakeholder engagement

6. Execute and measure

Track changes in:

• Overall LLM Perception Score

• Narrative-level scores

• Web-on perception

• Web-off perception

• Citation behavior

• Earned-media evidence

• Message pull-through

• Competitive position

• Narrative drift

7. Report to leadership

Translate complex data into:

• What AI systems currently believe

• Why that perception exists

• What changed

• What it means for the business

• Which risks or opportunities require attention

• Which actions are underway

• Whether perception is improving

GEO should become a continuous reputation-management capability, not a one-time project.

What the CCO dashboard should show

A CCO does not need a dashboard filled with every prompt and citation.

The executive view should answer the most important questions.

Overall perception

• LLM Perception Score

• Confidence rating

• Direction of change

• Web-on versus web-off difference

• Cross-model consistency

Priority narratives

For each narrative:

• Current interpretation

• Narrative score

• Favorability

• Message pull-through

• Competitive position

• Direction of change

• Evidence strength

Citation intelligence

• Most frequently observed sources

• Highest-authority sources

• Sources reinforcing desired perception

• Sources reinforcing risk

• Outdated sources

• Likely citation influence

• Citation changes over time

Earned-media evidence

• Dominant narratives

• Brand prominence

• Brand-centric sentiment

• Source authority

• Independent corroboration

• Narrative momentum

• Competitive framing

Accuracy and risk

• Incorrect claims

• Outdated claims

• Missing context

• Emerging negative narratives

• Cross-model disagreement

• High-confidence reputation risks

Actions

• Amplify

• Clarify

• Counter

• Canonicalize

• Create

• Validate

• Correct

• Monitor

The dashboard should produce understanding, not more work.

How to report GEO to the CEO and board

A board or CEO does not need a detailed explanation of every prompt variation.

A strong executive update may follow this structure.

Current AI perception

State the dominant interpretation of the company in direct language.

What changed

Explain whether the perception strengthened, weakened, fragmented, or remained stable.

Why it changed

Identify the narratives, sources, claims, and current events driving the movement.

Business significance

Connect perception to:

• Customer consideration

• Market leadership

• Investor confidence

• Employee reputation

• Policy risk

• Product adoption

• Competitive position

• Crisis exposure

Evidence quality

Explain how confident the company should be in the conclusion.

Next actions

Identify the specific communications priorities.

The CCO should be able to explain AI perception with the same confidence used to explain a major media narrative or reputation risk.

When GEO matters most

GEO should be continuously measured, but some moments require particular attention.

Product launches

Does AI understand what launched, why it matters, and how it differs from competitors?

Category creation

Is the company associated with the category, or are competitors defining it?

Repositioning

Has the brand moved beyond its legacy perception?

Executive transitions

Are leadership facts current and consistently represented?

Mergers and acquisitions

Do AI systems understand the transaction, combined company, strategy, and implications?

Issues and crises

Which negative claims are becoming dominant, and which sources are reinforcing them?

Regulatory developments

Are formal actions being described accurately and in context?

Research and thought leadership

Is the company’s evidence being cited and connected to its expertise?

Competitive announcements

Has a competitor strengthened its narrative or weakened the company’s comparative position?

Major earned-media campaigns

Did the coverage change perception, or only increase volume?

These moments should include a before-and-after perception baseline.

How quickly can perception change?

The answer depends on the narrative.

Breaking events can alter web-grounded answers quickly.

Longstanding corporate perceptions may change more slowly.

The rate of change depends on:

• Authority of the new evidence

• Volume of credible independent support

• Brand prominence

• Factual specificity

• Consistency across sources

• Crawl and index refresh cycles

• Model behavior

• Retrieval conditions

• Strength of the prior narrative

• Competitor evidence

A single article can matter if it is highly authoritative and directly relevant.

A broader perception may require sustained convergence across earned, owned, expert, customer, and other credible sources.

Common mistakes CCOs should avoid

Treating GEO as an SEO-only project

Technical SEO is necessary but cannot establish reputation by itself.

Treating prompt visibility as perception

A brand can appear frequently and still be positioned poorly.

Measuring only citations

Citations do not reveal whether the resulting perception is favorable, accurate, or strategically useful.

Relying only on owned content

Company claims become stronger when they receive credible independent validation.

Generating excessive low-value content

Volume does not create authority.

Ignoring web-off perception

Current retrieval may improve an answer while older perceptions remain durable without it.

Ignoring earned-media quality

Not all mentions provide equal evidence.

Treating all sources as equally authoritative

Authority depends on the source’s relationship to the claim.

Failing to establish a baseline

Without a baseline, teams cannot demonstrate change.

Producing a score without evidence

An executive score must remain traceable to its components, sources, and methodology.

Separating human and AI perception

The same information environment increasingly shapes both.

Questions a CCO should ask the team

1. What are our five most important narratives?

2. What do AI systems currently believe about each one?

3. Where do models agree and disagree?

4. How does web retrieval change the perception?

5. Which priority messages are absent or contradicted?

6. Which competitors have stronger narratives?

7. Which sources are most frequently cited?

8. Which sources have the greatest likely citation influence?

9. How strong is the underlying earned-media evidence?

10. Which facts are inaccurate or outdated?

11. What is our current LLM Perception Score?

12. How confident are we in that score?

13. What changed since the last measurement?

14. Which narratives are hardening or fading?

15. What should we amplify, clarify, counter, canonicalize, create, or validate?

16. Can we show that communications activity changed the outcome?

These questions move GEO from experimentation to an operating discipline.

The strategic opportunity for communications

GEO creates an opportunity for communications teams to demonstrate strategic impact more clearly than traditional media measurement often allows.

A communications campaign may produce:

• New authoritative coverage

• Stronger message pull-through

• Improved source authority

• Greater brand prominence

• Better competitive framing

• More consistent AI perception

• Improved citations

• Correction of outdated claims

• A stronger LLM Perception Score

• Greater alignment between human and machine perception

This creates a direct line from communications activity to the company’s information environment and stakeholder interpretation.

The CCO can move beyond reporting what the team published or how many articles appeared.

The team can report:

• Which narratives changed

• Which beliefs became more durable

• Which sources drove the change

• Which risks weakened

• Which competitive advantages strengthened

• How AI systems now describe the company

• Whether the intended perception became the dominant outcome

That is a more strategic measure of communications performance.

The central lesson

Generative engine optimization is not simply the process of getting cited by ChatGPT.

It is the discipline of shaping the narratives, evidence, sources, and claims from which AI systems construct their understanding of a company.

For the CCO, the goal is not to control the wording of every answer.

The goal is to shape the information environment strongly enough that the intended perception becomes the most credible, consistent, and well-supported conclusion available.

That requires:

• Clear narrative strategy

• Authoritative owned content

• Independent earned-media validation

• Strong source intelligence

• Accurate citation analysis

• Technical accessibility

• Competitive understanding

• Repeated cross-model measurement

• Web-on and web-off testing

• Transparent scoring

• Evidence-based action

AI systems increasingly mediate how stakeholders understand companies.

Managing that perception is now part of managing reputation.

And reputation belongs with communications.

Frequently asked questions

Sources

• OpenAI: Publishers and Developers FAQ

• OpenAI: ChatGPT Search

• Google: AI Features and Your Website

• Google: Optimizing Your Website for Generative AI Features on Google Search

• Google: Guidance on Using Generative AI Content on Your Website

• Google: Introducing Search Generative AI Performance Reports in Search Console

• Microsoft: Introducing AI Performance in Bing Webmaster Tools

• Microsoft: Elevating the Role of Grounding on the AI Web