How to Build an AI Brand Perception Strategy

An AI brand perception strategy determines how a company wants to be understood by AI systems, measures how it is currently perceived, identifies the evidence shaping that perception, and strengthens the narratives and sources most likely to improve the outcome.

It is not simply a list of prompts.

It is not a content calendar.

It is not a technical SEO project.

It is not a dashboard showing whether the brand appeared in ChatGPT.

A complete strategy connects:

• Business priorities

• Corporate reputation

• Brand narratives

• Earned media

• Owned content

• Source authority

• Citation intelligence

• Competitive positioning

• Web retrieval

• AI perception measurement

• Communications action

The objective is to make the company’s intended perception the clearest, strongest, most accurate, and most consistently supported interpretation available.

That requires understanding three connected layers:

1. The broader information environment, including earned media and other sources shaping the narrative

2. AI perception without visible web retrieval, which can reveal persistent or established brand associations

3. AI perception with web retrieval, which shows how current accessible evidence and visible citations affect the answer

These layers can be analyzed together and synthesized into an LLM Perception Score.

The score provides an executive signal.

The underlying evidence explains why the score exists and what the company should do next.

What is AI brand perception?

AI brand perception is the interpretation of a company, product, executive, competitor, or issue expressed through AI-generated answers.

It includes:

• The narratives associated with the brand

• The facts used to describe it

• The qualities attributed to it

• The comparisons made with competitors

• The strengths and weaknesses emphasized

• The sources cited

• The recommendations produced

• The risks or controversies surfaced

• The degree of confidence expressed

• The conclusions that persist across models and repeated tests

An AI system may describe a company as:

• A category leader

• An emerging challenger

• A legacy incumbent

• A trusted institutional brand

• An innovative company

• A lower-cost alternative

• A product designed for small businesses

• An enterprise platform

• A company facing regulatory pressure

• A business struggling to differentiate itself

• A brand still defined by a past controversy

These are not simple mentions.

They are compressed interpretations.

A stakeholder may use those interpretations to decide:

• Which company to buy from

• Which product to recommend

• Which executive to trust

• Which company leads a market

• Whether a brand is innovative

• Whether an organization is financially stable

• Whether a controversy is serious

• Whether a company is suitable for a partnership

• Whether a stock, employer, or product deserves further consideration

An AI brand perception strategy helps the company understand and influence those conclusions.

Why companies need an AI brand perception strategy

AI systems increasingly act as an interface between stakeholders and the public information environment.

A customer may ask:

Which company offers the strongest enterprise platform?

An investor may ask:

What are the greatest risks facing this company?

A journalist may ask:

What evidence supports the company’s claims about artificial intelligence?

A policymaker may ask:

Has this company faced regulatory scrutiny?

A potential employee may ask:

Is this organization considered a good employer?

The AI system may synthesize:

• News coverage

• Company webpages

• Product documentation

• Research

• Public records

• Reviews

• Customer evidence

• Expert commentary

• Social discussion

• Competitor content

• Historical information

The stakeholder may never visit the company’s website.

They may never read the full article.

They may interact only with the system’s interpretation.

This changes the communications problem.

It is no longer enough to ask:

• Did we earn coverage?

• Did our message appear?

• Did the page rank?

• Was our website cited?

• Did the brand appear in the prompt?

The company must also ask:

• What conclusion did the evidence create?

• Was that conclusion accurate?

• Was it favorable?

• Was it strategically important?

• Did it persist across models?

• Did current retrieval strengthen or weaken it?

• Which sources shaped it?

• Is the perception changing over time?

Start with business strategy, not AI prompts

The first step is not generating hundreds of questions.

It is identifying the business and reputation outcomes that matter.

A company’s priority narratives may relate to:

• Corporate reputation

• Product leadership

• Artificial intelligence

• Innovation

• Trust

• Security

• Customer value

• Market expansion

• Financial stability

• Executive leadership

• Workplace culture

• Regulatory issues

• Sustainability

• Scientific leadership

• A major product launch

• A merger or acquisition

• A strategic repositioning

• A crisis or controversy

The strategy should focus on narratives that can materially affect:

• Customer consideration

• Competitive position

• Revenue

• Investor confidence

• Employee reputation

• Regulatory risk

• Executive credibility

• Market leadership

• Product adoption

• Corporate valuation

Do not begin with every possible thing someone might ask.

Begin with the perceptions that matter most to the business.

Step 1: Select the priority narratives

Most companies should begin with a manageable number of narratives.

A practical starting point may be five to ten.

Examples include:

• The company is a leader in artificial intelligence.

• The company is more than its legacy product.

• The brand is trusted by a specific stakeholder group.

• The company has the strongest enterprise offering.

• The business is expanding successfully into a new market.

• The organization is responding effectively to a major issue.

• The product is differentiated from lower-cost competitors.

• The company is a credible category creator.

• The brand is financially stable.

• The organization is committed to responsible innovation.

Each narrative should be strategically meaningful.

Avoid broad corporate language such as:

• We are innovative.

• We put customers first.

• We are transforming the future.

• We deliver industry-leading solutions.

These statements are too generic to measure or substantiate.

A useful narrative should identify:

• The company

• The strategic association

• The relevant audience or market

• The basis for the claim

• The competitive implication

For example:

The company is the enterprise leader in privacy-preserving consumer AI because it combines on-device processing, integrated hardware and software, and clearly documented privacy controls.

This is specific enough to test.

Step 2: Define the desired perception

For each priority narrative, document what the company wants stakeholders and AI systems to conclude.

A desired perception should be:

• Specific

• Understandable

• Relevant to the business

• Supported by evidence

• Distinct from competitors

• Measurable

• Realistic

Weak desired perception:

The company is innovative.

Stronger desired perception:

The company is perceived as the leading provider of enterprise AI infrastructure for regulated industries, supported by real deployments, strong governance, and credible customer evidence.

The stronger version identifies:

• The category

• The audience

• The differentiator

• The evidence standard

This makes the perception easier to measure and manage.

Step 3: Define the undesired perception

The strategy should also document the interpretation the company wants to avoid.

Examples include:

• The company is a legacy incumbent adding superficial AI features.

• The brand is still only a single-product provider.

• The product is unsuitable for large enterprises.

• The company is losing market share to newer competitors.

• The organization’s sustainability claims are primarily promotional.

• The company’s leadership transition created instability.

• A resolved controversy remains the defining narrative.

• The product is cheaper but materially less capable.

• The company talks about innovation without demonstrating adoption.

Defining the undesired perception helps the team identify:

• Contradictory evidence

• Competitive vulnerabilities

• Reputational risks

• Missing messages

• Questions that should be tested

• Sources that may reinforce the wrong conclusion

The analysis should not be designed only to confirm the company’s preferred story.

It should deliberately test the strongest credible alternative.

Step 4: Define the priority messages

A narrative is broader than a message.

But each narrative should include several specific ideas the company wants to pull through.

For example:

Narrative: The company is more than its legacy product.

Priority messages:

• The company now offers an integrated platform.

• Customers use multiple products across the platform.

• The newer products contribute meaningful business value.

• The platform addresses a broader customer problem than the original offering.

• Independent sources recognize the company’s expansion.

Each message can later be classified as:

• Explicit

• Implied

• Absent

• Contradicted

This creates a measurable connection between communications strategy and AI perception.

Step 5: Identify the stakeholders

Different stakeholders ask different questions.

Relevant audiences may include:

• Customers

• Prospects

• Journalists

• Investors

• Analysts

• Employees

• Job candidates

• Policymakers

• Regulators

• Partners

• Suppliers

• Community leaders

• Industry experts

• Board members

For each narrative, identify:

• Who cares about it

• Why they care

• What decision they may be making

• Which evidence they are likely to trust

• Which competitors or alternatives they may consider

• Which risks they may investigate

For example, an investor may evaluate whether a company’s AI strategy is generating revenue.

A customer may care whether the AI product is secure and useful.

A journalist may examine whether the company’s leadership claim is supported by independent evidence.

The same narrative requires different prompt families and source analysis for each stakeholder.

Step 6: Define the competitor set

AI systems frequently interpret brands comparatively.

A company should identify:

• Direct competitors

• Emerging challengers

• Adjacent alternatives

• Category incumbents

• Companies that own the desired narrative

• Companies stakeholders may compare even if they are not traditional competitors

The competitive analysis should ask:

• Which brands appear most often?

• Which brand is named first?

• Which company defines the category?

• Which company is recommended?

• Which strengths are assigned to each?

• Which weaknesses are emphasized?

• Which sources support the comparison?

• Which competitor has the strongest evidence?

• Which brand appears to own the narrative?

The competitor set may vary by narrative.

A company may compete with one group for product sales and another group for innovation leadership, talent, trust, or policy influence.

Step 7: Document the supporting claims

Every desired perception should be supported by factual claims.

For example:

Desired perception: The company is a leader in responsible AI.

Supporting claims might include:

• It has a formal governance framework.

• It publishes model-risk documentation.

• It conducts independent evaluations.

• It discloses safety limitations.

• It has deployed the framework across regulated products.

• Customers can audit important system decisions.

• Independent experts recognize the approach.

• The company publishes measurable progress.

Each claim should be classified by evidence type:

• Company assertion

• Product evidence

• Customer evidence

• Earned-media validation

• Expert validation

• Regulatory evidence

• Independent research

• Public record

• Competitor comparison

This reveals whether the desired perception is supported by substance or primarily by messaging.

Step 8: Establish the current evidence inventory

Before testing AI systems, map the current information environment.

Review:

Owned content

• Homepage

• Product pages

• Leadership pages

• Research

• Technical documentation

• Customer stories

• Policy pages

• Security documentation

• Investor materials

• Corporate history

• Press releases

• Issue pages

• FAQs

• Blog posts

• Structured data

Earned media

• News coverage

• Trade publications

• Broadcast coverage

• Investigations

• Interviews

• Profiles

• Reviews

• Analyst commentary

• Expert columns

• Syndicated coverage

Institutional and public sources

• Regulators

• Courts

• Government databases

• Industry associations

• Academic institutions

• Public filings

• Standards organizations

Customer and expert evidence

• Customer case studies

• Reviews

• Testimonials

• Expert commentary

• Technical forums

• Research partnerships

• Independent audits

Social and community sources

• Executive posts

• Professional communities

• Developer forums

• Customer discussions

• Relevant social platforms

Competitor evidence

• Competitor product pages

• Competitor research

• Comparison content

• Competitor earned media

• Analyst reports

• Customer evidence

• Category definitions

The objective is to understand what evidence already exists before interpreting AI outputs.

Step 9: Analyze the earned-media narrative environment

Earned media should not be treated as a separate measurement system.

It is a foundational layer of AI brand perception.

For each priority narrative, analyze the complete relevant coverage corpus.

Measure:

• Coverage volume

• Brand prominence

• Brand-centric favorability

• Message pull-through

• Source authority

• Original reporting

• Factual specificity

• Independent corroboration

• Narrative momentum

• Competitive framing

• Conflicting evidence

• Narrative formation

• Narrative durability

The analysis should determine:

• Which narratives dominate

• Which sources have the greatest authority

• Whether the brand is central or incidental

• Which claims recur

• Whether independent sources support the desired perception

• Whether a competitor’s narrative is stronger

• Which claims may become durable AI beliefs

• Which sources are likely to affect future retrieval

• Which sources already appear in observed AI citations

Raw coverage volume should not determine the outcome.

One substantive, authoritative article may contribute more than dozens of passing mentions or duplicated press-release stories.

Step 10: Build representative prompt families

Prompts should be organized around the priority narratives.

A prompt family contains several ways of testing the same underlying perception.

For example:

Narrative: The company is an AI leader.

Factual prompts

• What AI products does the company offer?

• How does the company use artificial intelligence?

• When did the company launch its AI strategy?

Category prompts

• Which companies lead AI innovation in this industry?

• Who are the most important AI providers in this market?

Comparative prompts

• How does the company’s AI strategy compare with its main competitors?

• Is the company ahead of or behind competitors in AI?

Evaluative prompts

• Is the company considered an AI leader?

• What are the strengths and weaknesses of its AI strategy?

Evidence prompts

• What evidence supports the company’s AI leadership claims?

• Has the company demonstrated meaningful customer adoption?

Adverse prompts

• What concerns have been raised about the company’s AI strategy?

• Is the company adding AI features to a legacy platform?

Open-ended prompts

• What should I know about the company’s AI strategy?

• How is the company perceived in artificial intelligence?

The purpose is not to create the largest prompt list possible.

It is to test whether the narrative survives different questions, stakeholder perspectives, and assumptions.

Step 11: Avoid prompt-set bias

The prompt set can distort the result.

A credible strategy should avoid:

• Including the brand name in every prompt

• Using only the company’s preferred category language

• Asking only favorable questions

• Excluding major competitors

• Repeating minor wording variations as separate evidence

• Ignoring open-ended questions

• Ignoring adverse questions

• Overweighting one stakeholder

• Creating prompts that reveal the intended answer

• Testing only purchase recommendations

• Testing only one narrow product

The set should include:

• Neutral questions

• Comparative questions

• Adverse questions

• Factual questions

• Open-ended questions

• Recommendation questions

• Evidence questions

• Narrative-specific questions

The goal is an accurate measurement, not a flattering one.

Step 12: Test across relevant AI systems

A complete strategy may include:

• ChatGPT

• Claude

• Gemini

• Perplexity

• Grok

• Microsoft Copilot

• Google AI search experiences

• Other systems relevant to the company’s stakeholders

Not every system needs equal weight.

Prioritize based on:

• Stakeholder usage

• Market relevance

• Geographic relevance

• Product category

• Availability

• Search and retrieval behavior

• Strategic importance

Record:

• AI product

• Model when visible

• Date and time

• Prompt

• Retrieval condition

• User location when relevant

• Prior conversation context

• Personalization or memory conditions

• Answer

• Citations

• Classification results

The methodology should be transparent enough to repeat.

Step 13: Test with web retrieval turned off

Testing without visible current retrieval helps identify the perception expressed by the model under those conditions.

It can reveal:

• Persistent brand associations

• Established category positions

• Older narratives

• Durable strengths

• Durable weaknesses

• Outdated beliefs

• Cross-model differences

• Messages that recur without current citations

This should not be described as direct access to the model’s training data or internal reasoning.

It is an observed output under a defined test condition.

The analysis should ask:

• Which narratives persist?

• Which facts are outdated?

• Which competitors are associated with the category?

• Which messages appear consistently?

• Is the perception favorable?

• Is the company described accurately?

• How stable is the result across repeated runs?

Step 14: Test with web retrieval turned on

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

It can reveal:

• Recent developments

• Current news

• Updated product facts

• New competitive narratives

• Visible citations

• Current controversies

• Correction of outdated information

• Stronger or weaker favorability

• New message pull-through

• Different recommendations

The analysis should ask:

• Does retrieval improve the perception?

• Does retrieval introduce new risk?

• Which sources recur?

• Do the cited sources support the claims?

• Are current owned pages appearing?

• Are outdated pages still being cited?

• Does current earned media reinforce the desired narrative?

• Are competitors supported by stronger evidence?

Step 15: Measure retrieval resilience

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

A narrative may be:

Strong in both conditions

This suggests a more established and resilient perception.

Strong with retrieval, weak without it

The current evidence supports the desired narrative, but it may still be emerging.

Weak with retrieval, strong without it

The company may have a favorable established reputation that current news or accessible evidence is weakening.

Weak in both conditions

The desired perception may lack sufficient support across both current evidence and persistent model outputs.

Retrieval resilience should be measured across:

• Narrative presence

• Favorability

• Accuracy

• Message pull-through

• Competitive position

• Recommendation

• Cross-model consistency

Step 16: Use repeated runs

One response is not a reliable measure.

AI answers can vary across repeated runs.

Variability may affect:

• Brand inclusion

• Answer order

• Recommendations

• Claims

• Citations

• Favorability

• Competitive comparisons

• Message pull-through

Repeated testing helps distinguish:

• Durable patterns

• Emerging patterns

• Intermittent outputs

• Outliers

• Model-specific behavior

• Retrieval-specific behavior

For high-priority citation analysis, repeated testing across 30 to 50 runs per narrative can provide a stronger view of:

• Recurring URLs

• Recurring domains

• Citation frequency

• Citation-to-claim relationships

• Cross-model source patterns

• Changes in citation behavior

The number of runs should reflect the importance of the narrative and the confidence required.

Step 17: Measure narrative presence

Narrative presence determines whether the brand is meaningfully associated with the priority idea.

Classify the result as:

Primary

The brand is presented as a leading or defining example.

Substantive

The brand is meaningfully connected to the narrative.

Incidental

The brand appears without a meaningful narrative association.

Absent

The brand does not appear.

This prevents passing mentions from receiving the same value as category ownership.

Step 18: Measure brand prominence

Prominence determines how central the brand is to the answer.

Relevant signals include:

• Appearance in the opening

• Position within a list

• Amount of substantive discussion

• Inclusion in a heading

• Use as the main example

• Presence in the conclusion

• Share of answer space

• Whether the company defines the category

A brand mentioned at the end of a list does not have the same perception strength as a brand used to frame the entire answer.

Step 19: Measure brand-centric favorability

General sentiment does not reliably measure brand perception.

An answer can discuss a negative subject while positioning the brand positively.

Brand-centric favorability asks:

Does this answer strengthen or weaken confidence in the brand?

A practical classification may include:

• Strongly positive

• Positive

• Neutral

• Mixed

• Negative

• Strongly negative

The analysis should explain why the classification was assigned.

Relevant factors include:

• Leadership

• Trust

• Innovation

• Reliability

• Product quality

• Risk

• Controversy

• Customer outcomes

• Competitive weakness

• Effective response to a difficult issue

Step 20: Measure message pull-through

For each priority message, classify whether it is:

• Explicit

• Implied

• Absent

• Contradicted

For example:

Priority message: The company offers more than its original product.

An answer may:

• Clearly describe the broader platform

• Mention several products without connecting them

• Describe only the original product

• Explicitly call the company a single-product provider

These represent different degrees of pull-through.

Message measurement connects communications strategy to AI perception.

Step 21: Measure factual accuracy

Accuracy should be assessed at the claim level.

Classify each material claim as:

• Accurate

• Incomplete

• Outdated

• Unsupported

• Incorrect

Review:

• Executive roles

• Product availability

• Pricing

• Corporate ownership

• Regulatory status

• Transaction status

• Financial information

• Product capabilities

• Market position

• Corporate history

• Controversies

• Security events

• Customer claims

An inaccurate but highly visible answer should not receive a strong perception score.

Step 22: Measure competitive position

AI systems often answer through comparison.

Measure:

• Which competitors appear

• Which company appears first

• Which company is framed as the leader

• Which differentiators are assigned

• Which weaknesses are emphasized

• Which use cases each company owns

• Which company is recommended

• Whether recommendations are qualified

• Which evidence supports the comparison

• Whether the category definition favors a competitor

A company may have favorable standalone perception but weak comparative positioning.

That distinction should remain visible.

Step 23: Measure recommendation quality

Recommendation is more nuanced than whether the brand appears in a list.

Classify the result as:

• Recommended without qualification

• Recommended for a specific use case

• Included as one option

• Mentioned but not recommended

• Recommended against

• Absent

Also capture the reason:

• Product breadth

• Price

• Security

• Enterprise readiness

• Ease of use

• Innovation

• Customer support

• Market leadership

• Geographic availability

• Reputation

This reveals the criteria AI systems use when converting perception into a decision.

Step 24: Record observed citation behavior

Visible citations are a direct source signal.

Track:

• URL citation frequency

• Domain citation frequency

• Citation position

• Model

• Prompt family

• Retrieval condition

• Claim supported

• Brand prominence in the source

• Source freshness

• Cross-run recurrence

• Cross-model recurrence

Observed citation behavior answers:

Which sources visibly surfaced when AI systems answered questions about this narrative?

It should not be treated as a complete account of everything involved in producing the answer.

Step 25: Assess citation quality

A citation should be evaluated in relation to the claim.

Relevant dimensions include:

• Authority

• Direct relevance

• Factual specificity

• Freshness

• Independence

• Brand prominence

• Original reporting

• Transparency

• Whether the source supports the associated claim

• Whether it reinforces the desired or undesired narrative

A company page may be authoritative for product specifications.

A regulator may be authoritative for an approval.

A specialist publication may be authoritative for a technical comparison.

A customer-review site may be relevant to usability.

Authority is contextual.

Step 26: Assess likely citation influence

Observed citations show what visibly happened in tested answers.

Likely citation influence evaluates which sources are best positioned to shape future retrieval and citation behavior.

Relevant signals include:

• Source authority

• Direct narrative relevance

• Factual specificity

• Brand prominence

• Freshness

• Independence

• Accessibility

• Repetition across credible sources

• Narrative alignment

This is an evidence-based assessment.

It should not be described as direct access to an AI system’s internal weighting.

The distinction is:

• Observed citation behavior: What visibly surfaced

• Likely citation influence: Which sources appear best positioned to shape future answers

Both matter.

Step 27: Measure cross-model consistency

Different AI systems may express different perceptions.

Classify the result as:

• Highly consistent

• Generally consistent

• Mixed

• Contradictory

• Insufficient evidence

The goal is not identical wording.

It is consistency in the underlying interpretation.

Cross-model disagreement may indicate:

• Different source retrieval

• Conflicting evidence

• An emerging narrative

• Weak category definition

• Model-specific behavior

• Greater sensitivity to prompt wording

• A perception that has not become durable

Step 28: Measure cross-run stability

Cross-run stability determines whether the perception persists.

Track consistency in:

• Brand inclusion

• Narrative classification

• Favorability

• Message pull-through

• Competitive position

• Recommendation

• Factual claims

• Citations

A favorable result that appears once should not receive the same weight as a favorable perception that recurs across repeated tests.

Stability should affect both performance and confidence.

Step 29: Measure narrative drift

Narrative drift shows how perception changes over time.

Classify the trajectory as:

• Strengthening

• Weakening

• Correcting

• Fragmenting

• Hardening

• Fading

• Stable

For example, a company may shift from being described as:

• A product company to a platform

• A challenger to a leader

• An innovator to a legacy incumbent

• A controversial company to a trusted one

• A consumer brand to an enterprise provider

Narrative drift should be tied to dated evidence and compared with a fixed baseline.

Step 30: Build the LLM Perception Score

The underlying dimensions can be combined into a transparent composite score.

A defensible LLM Perception Score may include:

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

• Accuracy score

• Confidence rating

The score is the synthesis.

The component evidence explains the result.

Step 31: Define the scoring rubric

The scoring methodology should document:

• Each component

• The scoring scale

• The evidence required

• The weighting

• How missing data is handled

• How models are weighted

• How repeated runs affect the result

• How web-on and web-off conditions are combined

• How citations are evaluated

• How earned media contributes

• How negative narratives affect the score

• How score changes are calculated

• How confidence is determined

A composite score is not the problem.

An opaque score is.

Leadership should be able to trace the overall number back to:

• Individual narratives

• Answer patterns

• Source evidence

• Citation behavior

• Earned-media findings

• Model differences

• Scoring decisions

Step 32: Add a confidence rating

Performance and confidence should remain separate.

A score of 82 based on five models, several prompt families, repeated runs, web-on and web-off testing, recurring citations, and strong earned-media evidence should carry more weight than an 82 based on three prompts and one model.

Confidence may consider:

• Number of models

• Number of prompt families

• Number of repeated runs

• Cross-model agreement

• Cross-run stability

• Citation recurrence

• Source quality

• Evidence freshness

• Earned-media corpus size

• Retrieval-condition coverage

• Longitudinal consistency

A high score with low confidence may indicate a favorable but emerging perception.

A low score with high confidence may indicate a durable reputation problem.

Step 33: Diagnose the evidence gap

The score should lead to diagnosis.

Common gaps include:

Presence gap

The brand is absent from relevant answers.

Prominence gap

The brand appears but is not central.

Favorability gap

The brand is visible but framed negatively or with material qualifications.

Message gap

Priority ideas do not survive synthesis.

Accuracy gap

Answers contain incorrect or outdated claims.

Competitive gap

A competitor owns the category or preferred use case.

Recommendation gap

The brand appears but is not recommended.

Citation gap

The brand lacks recurring source support.

Authority gap

The supporting evidence is primarily company-authored or low quality.

Earned-evidence gap

The desired perception lacks independent corroboration.

Retrieval gap

Strong information exists but is difficult to access.

Durability gap

The narrative appears only in isolated prompts, one model, or web-on conditions.

Consistency gap

Owned, earned, expert, and public sources support conflicting conclusions.

Different gaps require different communications actions.

Step 34: Prioritize the right action

A practical strategy should classify actions into several categories.

Amplify

Use when the desired perception already exists but lacks sufficient prominence.

Actions may include:

• Extending authoritative earned coverage

• Elevating strong third-party sources

• Increasing executive participation

• Connecting research to the broader narrative

• Strengthening internal links

• Making successful customer evidence more visible

• Reinforcing the narrative with current examples

Clarify

Use when the perception is directionally correct but misunderstood.

Actions may include:

• Defining the category

• Rewriting product descriptions

• Explaining the company’s broader platform

• Clarifying the timeline of a change

• Publishing a comparison

• Replacing vague messaging with specific claims

• Aligning executives around consistent language

Counter

Use when an inaccurate or incomplete narrative is gaining strength.

Actions may include:

• Publishing current facts

• Providing missing context

• Correcting canonical pages

• Seeking legitimate external corrections

• Addressing a claim directly

• Earning independent reporting

• Increasing the authority of current evidence

Canonicalize

Use when an important fact or narrative lacks a permanent source.

Canonical pages may include:

• Category definitions

• Product architecture

• Research methodology

• Security documentation

• Executive biographies

• Corporate history

• Issue-response pages

• Transaction status

• Responsible-AI frameworks

• Frequently updated policy pages

Create

Use when the necessary evidence does not yet exist.

The company may need:

• Original research

• Customer data

• Technical documentation

• Independent audits

• Product evidence

• Expert analysis

• Transparent methodology

• Executive thought leadership

• Customer case studies

Validate

Use when the company’s claim lacks independent support.

Validation may come from:

• Earned media

• Customers

• Experts

• Industry organizations

• Research partners

• Regulators

• Independent evaluators

• Auditors

Correct

Use when the information environment contains inaccurate or outdated facts.

Actions may include:

• Updating owned pages

• Updating structured data

• Correcting directories

• Updating partner descriptions

• Seeking publisher corrections

• Clarifying dates

• Distinguishing announced and completed actions

Consolidate

Use when fragmented content creates conflicting interpretations.

Actions may include:

• Redirecting duplicates

• Updating regional pages

• Retiring obsolete pages

• Aligning biographies

• Consolidating product descriptions

• Clarifying historical content

• Establishing one source of truth

Monitor

Use when the narrative is emerging but not yet stable.

Monitoring should track:

• Model agreement

• Run stability

• Web-on and web-off differences

• Citation recurrence

• Earned-media momentum

• Competitive activity

• Narrative drift

Step 35: Build an owned-content plan

Owned content should provide the canonical foundation for important facts and narratives.

Review whether the company has clear permanent pages for:

• What the company does

• Product capabilities

• Category definitions

• Executive leadership

• Research

• Methodology

• Security

• Policies

• Corporate history

• Transactions

• Customer evidence

• Strategic initiatives

• Current issues

Each page should be:

• Accurate

• Current

• Specific

• Publicly accessible

• Crawlable

• Indexable

• Written in readable text

• Internally linked

• Supported by evidence

• Maintained at a stable URL

• Consistent with related pages

OpenAI advises publishers that want content considered for ChatGPT Search summaries and citations not to block OAI-SearchBot.

Google says foundational SEO practices remain relevant to its generative AI experiences and discourages creating separate content for every possible query variation primarily to manipulate generative responses.

Technical access does not guarantee citation or favorable perception.

It ensures the evidence can be considered.

Step 36: Build an earned-media plan

Earned media provides independent evidence that a company’s preferred interpretation is credible.

The plan should identify:

• Which narratives require validation

• Which claims need third-party evidence

• Which sources have authority

• Which publications influence the category

• Which reporters understand the issue

• Which experts can evaluate the claims

• Which customer outcomes are newsworthy

• Which research could create original evidence

• Which competitor narratives need to be reframed

• Which issues require public context

The objective is not maximum coverage volume.

It is authoritative, substantive, independently reported evidence in which the brand is central to the narrative.

Step 37: Build a source strategy

For each narrative, identify:

Canonical owned sources

The best first-party pages for current facts.

Authoritative earned sources

The publications and articles most capable of validating the narrative.

Expert sources

The individuals and institutions with subject-matter credibility.

Customer sources

The evidence demonstrating real-world outcomes.

Regulatory and public sources

The formal records supporting key facts.

Community sources

The credible forums or communities relevant to specific questions.

Risk sources

The sources reinforcing negative or outdated narratives.

Then classify each source by:

• Authority

• Relevance

• Specificity

• Prominence

• Freshness

• Independence

• Accessibility

• Narrative alignment

• Observed citation frequency

• Likely citation influence

This turns citation work into source intelligence.

Step 38: Establish technical eligibility

Communications cannot solve technical access problems alone.

SEO and digital teams should confirm:

• robots.txt access

• Search crawler access

• Indexability

• Canonical tags

• HTTP status codes

• Server-rendered content

• Internal links

• XML sitemaps

• Structured data

• Page titles

• Metadata

• Duplicate handling

• Stable URLs

• Page performance

Technical eligibility is necessary for web-grounded discovery.

It does not replace authority, relevance, or evidence quality.

Step 39: Establish the measurement cadence

Different narratives require different schedules.

Ongoing corporate narratives

Measure monthly or quarterly.

Examples:

• Innovation

• Trust

• Leadership

• Employer reputation

• Market position

Active campaigns

Measure:

• Before launch

• During the campaign

• After major coverage moments

• At the end of the campaign

Product launches

Measure:

• Before announcement

• Immediately after launch

• After major reviews or customer evidence

• After the narrative has had time to stabilize

Issues and crises

Measure more frequently while facts and coverage are changing.

Leadership changes

Measure at:

• Announcement

• Effective date

• After owned and external sources are updated

Category creation and repositioning

Measure longitudinally because durable narrative change may require sustained evidence.

The cadence should reflect how quickly the information environment can change.

Step 40: Establish a baseline

A baseline should exist before:

• Product launches

• Rebrands

• Executive transitions

• Category campaigns

• Major research releases

• Mergers and acquisitions

• Investor events

• Crisis responses

• Strategic repositioning

• Significant earned-media campaigns

The baseline should include:

• Overall LLM Perception Score

• Narrative-level scores

• Web-on perception

• Web-off perception

• Citation behavior

• Earned-media evidence

• Competitive position

• Message pull-through

• Accuracy

• Confidence

Without a baseline, the company cannot demonstrate change.

Step 41: Connect communications activity to perception change

The strategy should test whether specific actions changed the information environment.

Examples include:

• Did a research release improve authority?

• Did earned coverage increase message pull-through?

• Did a canonical page correct an outdated claim?

• Did customer evidence strengthen recommendation?

• Did executive thought leadership improve category association?

• Did a product launch alter competitive positioning?

• Did crisis communications reduce inaccurate narratives?

• Did current retrieval begin reinforcing the desired perception?

• Did the narrative begin appearing without retrieval?

• Did authoritative citations replace weaker sources?

Use cautious language when interpreting causality.

Appropriate language includes:

• Appears associated with

• Likely contributed to

• Coincided with

• May have strengthened

• Was followed by

• Is consistent with

The company should separate observed change from causal certainty.

Step 42: Build the executive scorecard

Leadership should not have to review every prompt.

A useful executive scorecard may include:

Overall perception

• LLM Perception Score

• Confidence rating

• Direction of change

• Web-on score

• Web-off score

• Cross-model consistency

Priority narratives

For each narrative:

• Current perception

• Narrative score

• Favorability

• Message pull-through

• Competitive position

• Direction of change

• Durability

• 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 favorability

• Source authority

• Independent corroboration

• Narrative momentum

• Conflicting evidence

• Earned-evidence score

Accuracy and risk

• Incorrect claims

• Outdated claims

• Unsupported conclusions

• Emerging negative narratives

• Model disagreement

• High-confidence reputation risks

Actions

• Amplify

• Clarify

• Counter

• Canonicalize

• Create

• Validate

• Correct

• Consolidate

• Monitor

The scorecard should produce understanding and decisions, not more analytical work.

Step 43: Report the conclusion before the methodology

An executive briefing should begin with:

What AI systems currently believe

State the dominant perception directly.

Why it matters

Connect the perception to business and reputation outcomes.

What changed

Explain whether the narrative strengthened, weakened, corrected, fragmented, hardened, or faded.

Why it changed

Identify the sources, claims, earned-media narratives, current events, and retrieval conditions connected to the movement.

How confident the company should be

Report the score and confidence level.

What should happen next

Identify the specific communications actions.

The methodology should remain available for auditability.

It should not overwhelm the conclusion.

Step 44: Assign ownership

AI brand perception crosses organizational boundaries.

Communications

Should typically lead:

• Priority narratives

• Desired and undesired perceptions

• Earned-media strategy

• Message pull-through

• Brand-centric favorability

• Executive positioning

• Reputation risk

• Strategic recommendations

• Leadership reporting

SEO and digital

Should lead:

• Crawl access

• Indexability

• Site architecture

• Canonicalization

• Internal links

• Structured data

• Search reporting

• Technical implementation

Content

Should lead:

• Canonical pages

• Answer clarity

• Editorial quality

• Research presentation

• Content maintenance

• Factual specificity

Product

Should confirm:

• Product capabilities

• Availability

• Technical facts

• Roadmap status

• Customer outcomes

• Documentation

Legal and policy

Should advise on:

• Regulatory claims

• Litigation

• Public records

• Correction language

• Disclosure requirements

• Risk

Analytics

Should support:

• Scoring

• Measurement

• Data integration

• Baselines

• Longitudinal reporting

• Confidence methodology

The Chief Communications Officer should play a central role when the objective is to manage how AI systems interpret the company’s reputation.

Step 45: Create governance

A mature program should document:

• Narrative owners

• Source owners

• Measurement cadence

• Scoring methodology

• Data retention

• Model and prompt changes

• Correction procedures

• Escalation thresholds

• Executive reporting

• Legal review requirements

• Crisis-monitoring procedures

• Rules for factual versus strategic recommendations

Governance is particularly important because:

• Models change

• AI interfaces change

• Retrieval conditions change

• Prompts evolve

• Company facts change

• Competitors act

• New evidence appears

The program must remain comparable without pretending the environment is static.

Step 46: Define escalation thresholds

Not every score change requires executive attention.

Escalation may be appropriate when:

• A material factual error appears across several models

• A negative narrative becomes highly consistent

• Web retrieval introduces a new reputation risk

• An inaccurate source is cited repeatedly

• A competitor begins owning a priority narrative

• Message pull-through falls materially

• The LLM Perception Score declines beyond a defined threshold

• A crisis narrative begins appearing without retrieval

• Current evidence contradicts official company information

• Cross-model fragmentation increases sharply

The threshold should reflect business significance, evidence strength, and confidence.

Step 47: Avoid common strategy mistakes

Starting with thousands of prompts

This creates noise before the company has defined what matters.

Treating visibility as perception

A brand can appear frequently and be portrayed poorly.

Treating citations as inherently favorable

A citation may support criticism, controversy, or outdated information.

Ignoring earned media

Independent coverage is a major source of narrative evidence.

Relying only on owned content

Company claims become stronger when independently validated.

Testing only one model

Different systems may express materially different perceptions.

Testing only one run

One answer may be an outlier.

Ignoring web-off perception

Current retrieval may be accurate while older perceptions remain persistent.

Ignoring web-on perception

Current accessible evidence may be strengthening risk or correcting outdated beliefs.

Creating content for every possible prompt

Stakeholder questions are effectively unlimited. Build around narratives and real information needs.

Using generic content

Broad claims without evidence create weak perception.

Failing to define competitors

AI systems often create meaning through comparison.

Creating an unexplained score

A number without a rubric creates false authority.

Reporting a score without confidence

Performance should be separated from the strength of the evidence.

Optimizing the test rather than the narrative

The goal is not to win a fixed prompt list.

It is to strengthen the underlying information environment.

A practical 90-day AI brand perception plan

Days 1–15: Define the strategy

• Select five to ten priority narratives

• Define desired perceptions

• Define undesired perceptions

• Document priority messages

• Identify stakeholders

• Select competitors

• List supporting claims

• Assign internal owners

Days 16–30: Map the evidence

• Inventory owned content

• Analyze earned-media coverage

• Identify authoritative sources

• Review customer and expert evidence

• Identify factual inconsistencies

• Evaluate competitor evidence

• Identify obvious crawl or indexing problems

• Establish the initial source map

Days 31–45: Establish the baseline

• Build prompt families

• Test relevant AI systems

• Run web-on and web-off tests

• Complete repeated runs

• Capture citations

• Measure perception attributes

• Calculate initial narrative scores

• Assign confidence ratings

Days 46–60: Diagnose the gaps

• Compare AI perception with earned media

• Identify presence gaps

• Identify message gaps

• Identify competitive gaps

• Identify accuracy problems

• Identify authority gaps

• Identify citation risks

• Identify retrieval and technical problems

Days 61–75: Build the action plan

Prioritize:

• Amplification

• Clarification

• Correction

• Canonicalization

• New evidence

• Independent validation

• Technical improvements

• Earned-media activity

• Competitive repositioning

Assign:

• Owner

• Deadline

• Evidence required

• Expected narrative impact

• Measurement date

Days 76–90: Execute and retest

• Publish priority canonical pages

• Correct factual inconsistencies

• Begin earned-media and validation work

• Improve technical access

• Update external sources

• Retest priority narratives

• Compare results with the baseline

• Report early movement and confidence

• Set the ongoing cadence

A 90-day period may not be sufficient to create durable narrative change.

It is sufficient to establish the operating system.

A practical strategy template

For each priority narrative, document:

Narrative

What is the strategic perception?

Desired perception

What should stakeholders and AI systems conclude?

Undesired perception

What conclusion creates risk?

Priority messages

Which specific ideas should pull through?

Stakeholders

Who cares and what decisions are they making?

Competitors

Who is competing to own the narrative?

Supporting claims

Which facts establish the perception?

Evidence

Which earned, owned, expert, customer, regulatory, and public sources support it?

Current AI perception

What do AI systems currently say?

Web-on perception

How does current retrieval affect the answer?

Web-off perception

What perception persists without visible retrieval?

Citation intelligence

Which sources visibly recur, and which sources are likely to influence future answers?

Earned-media environment

Which narratives and claims dominate current coverage?

Current score

What is the narrative-level LLM Perception Score?

Confidence

How stable and well-supported is the result?

Gap

What is preventing the desired perception from becoming dominant?

Action

Should the company amplify, clarify, counter, canonicalize, create, validate, correct, consolidate, or monitor?

Owner

Who is responsible?

Measurement date

When will the narrative be tested again?

How to know whether the strategy is working

A successful strategy should produce measurable improvement in:

• Narrative presence

• Brand prominence

• Brand-centric favorability

• Message pull-through

• Factual accuracy

• Competitive position

• Recommendation quality

• Citation quality

• Source authority

• Cross-model consistency

• Cross-run stability

• Retrieval resilience

• Earned-media evidence strength

• Narrative trajectory

• LLM Perception Score

It should also produce qualitative changes:

• AI systems describe the company more accurately.

• Priority messages survive synthesis.

• Stronger sources appear.

• Outdated sources appear less often.

• Competitors no longer own the category by default.

• Web retrieval reinforces rather than weakens the narrative.

• Established misconceptions begin to fade.

• Leadership can understand why perception changed.

• Communications actions connect to measurable reputation outcomes.

What success does not mean

Success does not require:

• Identical answers across every model

• Citation of the company website in every response

• Perfect scores on every prompt

• Elimination of all negative information

• Control over exact wording

• Publication of hundreds of AI-targeted pages

• Artificial repetition of company claims

• Guaranteed rankings or citations

A credible strategy accepts that:

• Models differ

• Answers vary

• Sources compete

• Negative evidence may be legitimate

• Perceptions change over time

• No company controls every output

The objective is to make the accurate and strategically favorable interpretation the strongest supported conclusion.

The role of SEO, AEO, and GEO

An AI brand perception strategy should incorporate, but not confuse, several related disciplines.

SEO

Ensures important content can be discovered, crawled, indexed, and found through search.

AEO

Makes important facts and answers clear, direct, specific, and easy to extract.

GEO

Improves the inclusion, citation, and representation of brands and sources in generative answers.

AI reputation intelligence

Determines what AI systems believe, why that perception exists, and what communications action can improve it.

The progression is:

Discoverability → Answerability → Generative inclusion → Reputation

Each layer supports the next.

The final outcome is the interpretation produced.

Questions leadership should ask

1. What are our most important brand narratives?

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

3. What is our overall LLM Perception Score?

4. How confident are we in that score?

5. Which narratives are strengthening or weakening it?

6. Where do models agree and disagree?

7. How does web retrieval change the result?

8. Which messages are absent or contradicted?

9. Which competitors own the desired narrative?

10. Which facts are inaccurate or outdated?

11. Which sources are most frequently cited?

12. Which sources have the greatest likely citation influence?

13. How strong is the earned-media evidence?

14. Which narratives are emerging, established, or durable?

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

16. Can we demonstrate that communications activity changed the perception?

A company that can answer these questions has moved beyond prompt monitoring.

It has an AI brand perception strategy.

The central lesson

AI brand perception is not created by prompts.

It is created by the information environment.

Prompts reveal how that environment is being interpreted.

Citations reveal part of the source behavior.

Earned media reveals how independent sources are framing the narrative.

Owned content provides the canonical facts.

Repeated testing shows whether the perception is stable.

Web-on and web-off analysis shows whether the narrative is current, emerging, persistent, or durable.

The LLM Perception Score synthesizes the result.

The strategy begins with the company’s most important narratives.

It then connects:

• Desired perception

• Undesired perception

• Priority messages

• Stakeholder questions

• Competitors

• Supporting claims

• Earned media

• Owned evidence

• AI perception

• Citation intelligence

• Scoring

• Communications action

• Longitudinal measurement

The goal is not to manipulate AI systems or dictate the wording of every answer.

The goal is to build an evidence environment in which the accurate, favorable, and strategically important perception of the company is the strongest conclusion available.

That is how brands move from tracking AI answers to managing AI perception.

And that is how communications teams can understand, command, and protect reputation in the age of AI.

Frequently asked questions

Sources

• OpenAI: Publishers and Developers FAQ

• OpenAI: ChatGPT Search

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

• Google: AI Features and Your Website

• Google: A New Resource for Optimizing for Generative AI in Google Search

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

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

• Microsoft: Introducing AI Performance in Bing Webmaster Tools

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