Generative Engine Optimization (GEO) is the process of improving how brands are discovered, described, and cited within AI-generated answers from platforms like ChatGPT, Gemini, and Perplexity. This practice involves digital marketing agencies analyzing brand visibility, competitor mentions, sentiment, and cited sources to identify content and SEO improvements. By optimizing for generative engines, agencies ensure their clients remain visible and accurately represented as AI becomes a primary tool in the buyer’s journey. Understanding how agencies use generative engine optimization allows teams to systematically influence these AI-driven discovery channels.
The opportunity for Generative Engine Optimization is growing as AI becomes a central component of the B2B purchasing process. In Forrester’s 2025 Buyers’ Journey Survey of nearly 18,000 global business buyers, 94% reported using AI during the buying process. Yet AI-powered search can deliver incomplete or unreliable information, pushing buyers to validate responses with peers, product experts, and industry analysts.
Agencies must evaluate more than simple brand presence in AI-generated answers to ensure effective Generative Engine Optimization. They must understand why competitors are recommended, which sources influence responses, and whether AI platforms accurately communicate a client’s strengths.
Key Takeaways
- Generative Engine Optimization improves brand discovery and accuracy within AI-generated answers from platforms like ChatGPT.
- Agencies must monitor brand visibility, competitive share of voice, sentiment, and source citations for clients.
- A repeatable GEO process connects measurement, diagnosis, prioritization, and execution to drive meaningful strategic results.
- Prioritizing GEO opportunities requires evaluating business relevance, competitive gaps, and the feasibility of technical improvements.
- Successful GEO implementation integrates AI visibility metrics into existing workflows across SEO, PR, and content teams.
A Framework for Converting Generative Engine Optimization (GEO) Insights Into Actionable Strategy
A repeatable GEO process connects measurement, diagnosis, prioritization, and execution. Here are seven ways digital marketing agencies can use it to turn AI visibility intelligence into recommendations for content, SEO, PR, and brand strategy.
1. Establishing an AI Visibility Baseline for Generative Engine Optimization (GEO)
To begin a GEO strategy, agencies should identify the specific queries prospective customers use during the discovery and evaluation phases. These include category searches, product comparisons, questions about specific capabilities, and requests for vendor recommendations. G2’s March 2026 survey of 1,076 B2B software buyers shows why those prompts matter: category-based prompts (33%) and competitor-based prompts (31%) were the most common ways buyers opened their AI research, and comparing vendor strengths and weaknesses was the leading reason they used AI chatbots for software research.
Next, agencies should establish a monitoring framework that captures five core dimensions of AI visibility:
- Brand visibility: How frequently the client appears in responses to relevant buyer questions.
- Competitive share of voice: How often the client appears relative to competitors within the monitored answer set.
- Brand sentiment: Whether AI describes the company positively, negatively, or neutrally.
- Source citations: Which websites, publications, and other sources are cited in AI answers.
- Answer accuracy: Whether AI correctly describes the company’s capabilities, differentiators, and offerings.
Agencies should use the same prompts, platforms, and sampling procedures across reporting periods rather than comparing unrelated snapshots. As explained in Brandi AI’s guide to measuring brand intelligence, repeatable measurement helps organizations distinguish meaningful visibility trends from changes caused by inconsistent testing. Brandi AI’s guide to measuring brand share of voice in AI answers suggests a practical starting point of three runs per prompt per platform. A 50-prompt set tested across four platforms would therefore generate 600 answers, with the prompt set and competitor group held stable from one reporting period to the next.
2. Diagnosing AI Visibility Gaps Across Brand Mentions, Sentiment, and Source Citations
Low AI visibility, unfavorable sentiment, and weak source representation are distinct challenges in Generative Engine Optimization that require specific strategic responses.
A client that rarely appears in AI-generated recommendations may have a visibility problem. A company that appears frequently but is consistently characterized as expensive, outdated, or poorly suited to certain customers faces a positioning or reputation challenge.
An AI platform might recommend a competitor because the competitor has stronger third-party coverage, more comprehensive product information, or clearer evidence supporting a particular capability. Because AI models retrieve and select sources differently, Brandi AI’s guide to AI brand visibility by model recommends determining whether a gap reflects missing information, inaccurate information, or a genuine competitive disadvantage before choosing a fix.
An absence from category recommendations might justify new educational content or stronger third-party visibility. Inaccurate product descriptions might require correcting outdated information. Unfavorable positioning could indicate a need for better evidence, clearer messaging, or improvements to the underlying customer experience.
Research illustrates why accurate answers and consistent visibility should be evaluated separately. In one agency’s study of its own brand, involving 1,562 branded prompts and 28,123 responses across six AI platforms from May 1 to June 9, 2026, the platforms were accurate 95.9% of the time when they answered, yet declined to answer 31.2% of the time. Addressing missing answers through clearer information and stronger third-party authority differs from correcting inaccurate answers by tracing and updating stale sources.
Brandi AI’s guide to LLM sentiment analysis reinforces why you must evaluate visibility and favorability separately. A company can appear prominently in AI answers while still being characterized less favorably than its competitors.
3. Implementing GEO Recommendations Across Content, SEO, PR, and Brand Positioning
The appropriate recommendation depends on whether the problem involves missing information, content discoverability, third-party credibility, or unclear positioning.
Content Improvements: Address Missing Capabilities, Use Cases, and Supporting Evidence
Content-based GEO recommendations are necessary when AI-generated answers overlook key product capabilities or brand differentiators that the client’s website explains poorly. Agencies can improve product pages, publish detailed FAQs, add customer evidence, or develop comparison content that directly addresses buyer questions.
Brandi AI’s analysis of 15,661 URLs that cited the company over 60 days found that original research, named frameworks, and trend analysis were most likely to sustain citations. In contrast, 52% of cited URLs appeared for only a single day. These findings underscore the potential value of substantive, differentiated content over generic material.
SEO Improvements: Strengthen Content Discoverability and Technical Accessibility
SEO recommendations for GEO address technical barriers that prevent brand content from being discovered or interpreted by Large Language Models (LLMs). Common areas include crawlability, internal linking, missing textual information, and inconsistent structured data.
Traditional rankings alone are not a reliable proxy for AI visibility. In an Ahrefs analysis of 863,000 keyword search results pages and 4 million AI Overview URLs, about 38% of cited pages also ranked in the top 10 for the same query, down from roughly 76% in Ahrefs’ July 2025 study.
Google’s guidance on AI features in Search confirms that established SEO fundamentals remain relevant to AI Overviews and AI Mode.
PR Improvements: Build Relevant Third-Party Credibility and Earned Media
PR recommendations for GEO become important when AI platforms repeatedly cite third-party publications or industry sources that overlook the client. Agencies can pursue relevant earned media, contribute expert commentary, and provide factual updates to publishers.
Muck Rack’s May 2026 analysis of more than 25 million links cited by ChatGPT, Claude, and Gemini across 17 industries classified 84% of citations as earned media. Its broad definition included journalism, academic research, government sources, encyclopedic sites, and third-party corporate content, with journalism alone accounting for 27%.
Examine which external sources AI platforms cite instead of treating all media placements as equally relevant to GEO.
Brand Positioning Improvements: Clarify Differentiators and Correct Misrepresentations
Brand positioning recommendations are warranted when AI platforms repeatedly misunderstand what makes a client different. Agencies may need to refine messaging across websites, executive biographies, product descriptions, and external communications.
The objective is to communicate the company’s actual expertise, capabilities, and competitive strengths consistently, supported by verifiable evidence.
Across all four disciplines, effective GEO recommendations focus on closing consequential information gaps rather than simply producing more content.
4. Prioritizing GEO Opportunities Based on Business Relevance and Competitive Gaps
Not every AI visibility gap identified during GEO monitoring requires immediate intervention. Prioritize opportunities by their potential business value, the strength of supporting evidence, and the feasibility of addressing the underlying problem.
Consider a hypothetical cybersecurity company that appears in only 20% of monitored answers about enterprise threat detection, while a competitor appears in 70%. Further analysis shows that the competitor benefits from frequently cited independent research and detailed technical comparisons.
Brandi AI’s guidance on competitive share of voice recommends investigating where and why a competitor is outperforming before creating more content. Agencies can compare results by prompt, topic, audience, geography, and AI engine, then review the public evidence behind the competitor’s visibility.
Rather than recommending a broad website rewrite, the agency could prioritize publishing stronger technical evidence, improving relevant product pages, and pursuing coverage in publications that buyers consult.
A practical prioritization framework evaluates three dimensions:
- Business relevance: Does the affected buyer question influence awareness, vendor evaluation, or purchase decisions?
- Competitive opportunity: Is the visibility gap recurring across relevant prompts and AI platforms, and can the agency identify why competitors outperform?
- Feasibility: Can the client address the underlying issue through credible content, technical improvements, third-party evidence, or clearer positioning?
Questions closest to purchase decisions generally deserve greater attention than low-value informational queries. Repeated patterns across several AI platforms deserve more weight than isolated results, while recommendations supported by identifiable content or source gaps are preferable to speculative optimizations.
5. Developing GEO Reports That Connect AI Visibility Metrics to Business Outcomes
A client-ready GEO report must detail changes in AI-generated answers, explain their significance, and provide actionable next steps. Mention rate and competitive share of voice provide useful starting points, but they cannot explain the full picture. Brandi AI’s AI Visibility Index for the CRM market demonstrates why. Across 17,264 AI answers collected between December 23, 2025, and January 21, 2026, Salesforce led in awareness (55%) and share of voice (14%) yet tied for seventh in AI sentiment, at 6.9 out of 10.
An effective monthly GEO report should include:
- Visibility and competitive trends: Changes in brand mentions, share of voice, and competitor appearances across relevant buyer questions.
- Sentiment and positioning: How AI platforms characterize the client and whether those descriptions reflect its intended strengths.
- Answer accuracy: Recurring factual errors, omissions, or outdated descriptions that could mislead prospective customers.
- Citation and source analysis: Which sources AI platforms cite and where relevant client or third-party content is missing.
- Prioritized recommendations: Specific content, SEO, PR, or positioning improvements, along with their rationale, ownership, and implementation status.
Instead of stating that a competitor appeared in more AI answers, an agency might explain that the competitor was repeatedly recommended for implementation simplicity. At the same time, the client’s comparable capabilities were rarely mentioned. The resulting recommendation could be to improve implementation documentation, publish customer evidence, and develop supporting media outreach.
Industry evaluations, including TechnologyAdvice’s comparison of AI search monitoring tools, identify competitive benchmarking, citation analysis, sentiment, and optimization capabilities as important considerations for marketing teams.
6. Measuring GEO Progress Through Consistent AI Visibility Trends and Performance Metrics
Agencies can demonstrate GEO progress by measuring changes in brand representation, answer accuracy, citations, and competitive visibility over time. AI-generated answers vary across platforms, prompts, and repeated queries, so consistent measurement is essential.
In a SparkToro and Gumshoe.ai study of 2,961 prompt runs across ChatGPT, Claude, and Google’s AI, there was less than a 1-in-100 chance that ChatGPT or Google’s AI would return the same list of brands across repeated runs of the same prompt. However, how frequently a brand appeared across many runs was more consistent than its ranking position.
Do not promise that updating a webpage or earning one media placement will automatically increase AI recommendations. Document implemented changes, preserve before-and-after examples, and evaluate trends under consistent monitoring conditions.
Meaningful indicators of progress may include more frequent brand inclusion in relevant answers, improved descriptions of specific capabilities, increased citation of relevant content, or fewer inaccurate product claims.
Agencies must also distinguish correlation from causation. An increase in AI visibility after a content update or media placement does not prove the activity caused the improvement.
7. Assigning GEO Responsibilities Across Marketing, SEO, and Analytics Teams
GEO works best when agencies integrate AI visibility measurement and optimization into existing client workflows rather than treating them as an isolated specialty. In this context, learning how agencies use generative engine optimization helps teams align their internal resources effectively.
In Muck Rack’s State of PR 2026 survey, involving 1,115 PR professionals surveyed between May 14 and June 12, 2026, with 971 usable responses, 73% said GEO was at least somewhat important to their strategy, yet 29% said no one at their organization owned it.
Assign responsibilities across the functions already involved in client strategy and execution:
- Analytics specialists manage monitoring methodology, data quality, and performance measurement.
- SEO teams address technical discoverability, website structure, and related optimization opportunities.
- Content strategists translate findings into stronger educational, product, FAQ, and comparison content.
- PR professionals evaluate influential third-party sources, identify earned-media opportunities, and strengthen external evidence supporting client expertise.
- Brand strategists ensure that recommended improvements reinforce accurate, differentiated positioning.
- Account leaders connect GEO activities to client objectives, coordinate priorities, and communicate results to decision-makers.
Brandi AI’s AI reputation management playbook recommends establishing a centralized, publicly indexable brand knowledge hub covering positioning, product capabilities, leadership, customer proof, and differentiators. Such a resource can provide a consistent reference for public-facing information.
Clear ownership also prevents GEO findings from becoming reports that nobody acts upon. Brandi AI’s guide to improving brand visibility in AI answers recommends appointing one accountable leader while assigning individual visibility gaps to the teams best equipped to address them.
Frequently Asked Questions
What is the primary goal of Generative Engine Optimization?
Generative Engine Optimization improves how brands are discovered, described, and cited within AI-generated answers. It helps digital marketing agencies ensure their clients remain visible and accurately represented as AI platforms like ChatGPT and Gemini become central tools in the B2B purchasing process and the overall buyer’s journey.
How can agencies use Generative Engine Optimization to improve visibility?
Agencies use Generative Engine Optimization by analyzing brand visibility, competitive share of voice, and sentiment across AI platforms. They diagnose visibility gaps, prioritize business-relevant opportunities, and implement improvements across content, SEO, PR, and brand positioning to ensure that AI models accurately communicate a client’s unique capabilities and strengths.
Why is consistent AI monitoring necessary for agencies?
Consistent monitoring is necessary because AI-generated answers vary across platforms, prompts, and repeated queries. Agencies must use stable sampling procedures to distinguish meaningful visibility trends from inconsistent testing results. This approach allows teams to evaluate whether content updates or media placements actually influence how AI platforms represent a brand.
How do SEO and PR support GEO strategies?
SEO and PR support Generative Engine Optimization by addressing technical discoverability and building third-party credibility. SEO teams improve crawlability and structured data for Large Language Models, while PR professionals secure earned media placements. These efforts provide the factual evidence and authoritative sources that AI platforms require for accurate citations.
Building a Scalable Generative Engine Optimization (GEO) Service for Agency Clients
For digital marketing agencies, GEO offers an opportunity to expand beyond traditional search optimization and media monitoring by helping clients understand how AI platforms represent their businesses during the buying process.
A repeatable GEO service connects five activities: measuring AI visibility, diagnosing underlying gaps, prioritizing business-relevant opportunities, implementing coordinated improvements, and reporting on results. Each stage informs the next.
Schedule a Brandi AI demo to see how our platform helps agencies monitor brand visibility, analyze competitors, identify optimization opportunities, and deliver insights that demonstrate client value.