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The Solutions Review editors are exploring how and why companies should develop a marketing strategy that prioritizes Generative Engine Optimization (GEO) over traditional SEO best practices.
The ongoing, but probably irreversible, shift from traditional search engines to generative AI platforms represents one of the most significant disruptions to digital marketing since Google’s PageRank algorithm fundamentally restructured how information gets discovered online. While SEO practitioners spent decades optimizing for ten blue links, the emergence of ChatGPT, Perplexity, Claude, and similar platforms has created an entirely new paradigm where AI systems are synthesizing information and presenting direct answers rather than offering pathways to websites. This isn’t merely an evolution of search marketing—it’s a complete reimagining of how brands must position themselves in the information ecosystem.
With that in mind, the Solutions Review editors are exploring how enterprise technology brands can pivot their search engine optimization (SEO) strategy into a generative engine optimization (GEO) strategy, and why doing so is no longer a question of whether to develop one, but rather how quickly it can be launched.
Search engine optimization operates on a relatively straightforward premise: convince algorithmic crawlers that your content deserves prominent placement in results pages, then capture clicks from users who have signaled their intent through query formulation. The entire framework assumes that users will navigate to your domain, consume your content on your terms, and potentially convert within your controlled environment.
However, generative engine optimization functions on entirely different mechanics. AI platforms don’t drive traffic to your website, but consume your content as training data or reference material, then reconstitute that information within their own response frameworks. The result is that users never have to leave the AI interface, since it provides them with synthesized answers that may draw from dozens of sources simultaneously, with attribution ranging from explicit citations to complete opacity depending on the platform and query type. While these AI platforms are still learning and the methods they use to learn are evolving (in response to legal and ethical developments, among others), the results are clear: people want them to stick around.
This transformation means that traditional conversion funnels could be on the edge of total collapse. The moment of engagement isn’t when someone clicks through to your site but when an AI model incorporates your perspective, data, or framework into its response. Your marketing success depends not on capturing attention through search result placement but on becoming an authoritative source that AI systems reliably reference when addressing queries in your domain.
Most current generative AI platforms operate through a combination of training data and retrieval-augmented generation (RAG). The training data represents a snapshot of internet content up to a specific cutoff date. Meanwhile, RAG systems enable models to access and incorporate more recent information through web searches or real-time document retrieval. This architecture creates several optimization opportunities that differ fundamentally from traditional SEO.
For one, training data integration means that high-quality, authoritative content published before the model’s cutoff date becomes baked into the model’s understanding of topics. The AI doesn’t need to retrieve this information because it already understands concepts through the patterns it learned during training. Consequently, content that influences training data shapes how models perceive entire topic areas.
RAG systems present different dynamics. When an AI platform performs retrieval to answer queries, it evaluates sources based on relevance, recency, and authority, then synthesizes that information from multiple retrieved documents. The goal isn’t to rank first in a traditional sense but to be included in the retrieval set and to provide information structured in ways that models can easily extract and integrate into coherent responses.
As you can imagine, platform-specific approaches vary significantly. Some AI systems provide explicit citations with links, creating a new form of referral traffic. Others synthesize information without attribution, making brand recognition and repeated exposure across multiple sources the only viable strategy for establishing mind share. Still others allow users to access source documents directly, transforming the AI interface into a discovery layer rather than a final destination.
Understanding how generative engines work is one thing, but knowing how to optimize your brand’s content and identity for them is another. That’s where the term Generative Engine Optimization (GEO) comes from. As new a strategy as it is, it’s already gaining momentum and proving to be a powerful addition to marketing strategies across markets.
Traditional SEO prioritizes domain authority as a holistic metric aggregating backlinks, traffic, and trust signals across an entire website. GEO, meanwhile, requires a more granular approach to authority that focuses on topical expertise and source credibility within specific knowledge domains. Additionally, counter to traditional SEO wisdom, which encourages broad keyword targeting, GEO often rewards extreme specialization. Being the unambiguous authority on a narrow topic makes you indispensable for AI systems addressing that subject, while being one of thousands of reasonable sources on a broad topic makes you easily substitutable in synthetic responses.
Building authority for AI systems means establishing your organization as the definitive source for specific concepts, frameworks, datasets, or methodologies. This requires moving beyond keyword-focused content toward creating comprehensive resources that demonstrate genuine expertise. AI models trained on authoritative sources internalize not just individual facts but entire conceptual frameworks, making depth and interconnection more valuable than breadth.
Verification mechanisms also matter more in generative contexts than in traditional search. AI platforms are increasingly incorporating source quality assessments into their retrieval and synthesis processes. As a result, organizations that can demonstrate expertise through credentials, peer review, institutional backing, or verifiable track records gain disproportionate influence in how models represent information in their respective domains.
Generative AI models excel at extracting structured information from unstructured text, but explicitly structured content makes this process exponentially more reliable. Organizations that format knowledge in ways that align with how AI systems process information gain significant advantages in retrieval and synthesis accuracy. Schema markup, which provided marginal benefits in traditional SEO, becomes far more valuable for GEO. Structured data enables AI systems to understand the relationships between entities, extract specific data points, and maintain accuracy when synthesizing information across multiple sources.
Semantic clarity in content structure helps models parse meaning correctly. This means moving away from creative headline writing and metaphorical language toward explicit, unambiguous expression of concepts. While traditional SEO sometimes rewards clever wordplay that captures long-tail searches, GEO favors straightforward articulation that models can confidently interpret and replicate.
Documentation formats that separate context from core information improve extractability. When AI systems retrieve content, they need to distinguish between background explanations and actionable insights, between qualifications and central claims, and between your perspective and the consensus. Content that makes these distinctions explicit through clear structural elements gets represented more accurately in AI responses.
The time dimension functions differently in generative engine optimization compared to traditional search. SEO frequently emphasizes freshness signals, rewarding recently published or updated content with temporary ranking boosts. GEO creates a bifurcated temporal landscape where both historical influence and real-time relevance matter, but through separate mechanisms.
Content that influences training data carries significant weight in how models understand fundamental concepts, creating a strong incentive to publish authoritative frameworks and original research as early as possible to claim conceptual territory before competitors. Simultaneously, RAG systems create demand for continuously updated information on evolving topics. Organizations that maintain current and accurate data on developing situations position themselves as essential sources for real-time analysis and synthesis.
One potential outcome of this new approach is a divergence in content strategies, with one avenue focusing on foundational content that aims to shape AI training data, and the other on dynamic content intended for retrieval systems. Organizations that recognize this split and allocate resources to both types of content appropriately will outperform those applying uniform approaches across all material.
Translating GEO principles into operational reality requires moving beyond conceptual frameworks into concrete content development and technical optimization practices. The strategic pillars outlined above provide directional guidance, but execution demands specific techniques for structuring information, formatting content, and establishing entity relationships that generative AI systems can reliably process and incorporate. The tactics that follow represent new approaches to content creation that prioritize machine extractability alongside human readability, recognizing that your primary audience now includes AI systems that will mediate how humans ultimately encounter your ideas.
Generative AI models understand information through entities and their relationships rather than through keywords and phrases. Optimizing for entity recognition means ensuring that your organization, products, executives, methodologies, and other important entities get consistently and accurately represented in AI knowledge bases. As such, variations in how you refer to your organization, products, or concepts can create ambiguity that models may resolve incorrectly, potentially conflating your entities with those of competitors or fragmenting understanding across multiple representations.
Meanwhile, entity-relationship articulation clarifies how different concepts are connected. Explicitly stating relationships between your organization and industry standards, between your products and use cases, and between your executives and their areas of expertise helps models build accurate knowledge graphs that inform how they discuss your entities in synthetic responses.
Text-focused optimization makes sense when targeting search engines that primarily process written content. Generative AI platforms are increasingly incorporating multimodal capabilities, which enable them to understand and generate images, analyze videos, and process other content formats beyond pure text. For example, AI systems can now process video transcripts and, increasingly, visual content from video frames. This means that organizations producing video content should ensure comprehensive transcription, time-stamped descriptions of visual elements, and structured metadata that help models understand not just what’s said but also what’s shown.
Visual content optimized for AI interpretation understandably requires different approaches than visual content designed for human consumption. Alt text, captions, and the surrounding textual context help models understand images, but layout, diagram structure, and the visual information hierarchy also play a role. Charts, infographics, and data visualizations that clearly label axes, include legends, and maintain high contrast support accurate AI interpretation.
Multi-modal content, specifically video, will likely become significantly more valuable for GEO as models improve at cross-modal reasoning. Organizations that invest in creating rich media content with strong structural signals and comprehensive metadata will establish a competitive advantage as AI platforms incorporate these capabilities into their retrieval and synthesis workflows.
Traditional SEO metrics focus on rankings, traffic, and conversions within your owned properties. GEO is something altogether new, though, and requires developing new measurement frameworks that account for influence and presence within AI platforms that don’t drive traffic to your site.
One way to track your company’s GEO efforts is through citation tracking across AI responses, which provides a direct measurement of when platforms reference your content. Organizations serious about GEO need to develop monitoring capabilities that track both explicit citations and implicit incorporation of your ideas and frameworks. Some tools have emerged to monitor these citations, although the landscape remains immature compared to traditional SEO analytics. Teams should keep a close eye on that market as it develops.
Brand mention analysis in AI responses measures when platforms discuss your organization, products, or executives without necessarily citing specific content. This softer metric captures presence in the AI information ecosystem even when direct attribution doesn’t occur. Another approach is to track competitive positioning in AI responses, which reveals how models present your organization relative to competitors. When users ask comparative questions or request recommendations, understanding how AI platforms position your offerings relative to alternatives provides crucial strategic intelligence.
The challenge is that generative AI platforms provide limited visibility into when and how they use content, making measurement inherently more difficult than traditional analytics. Organizations should expect to invest significantly in developing proprietary measurement approaches until the analytics ecosystem matures.
The current state of GEO represents an early-stage territory, as organizations figure out how to influence AI systems that are themselves rapidly evolving. Several developments are likely to dramatically reshape the field over the next few years, so brands must ensure their marketing and GEO-centric strategies can adapt to whatever new trends emerge. Here are a few predictions for what trends could be on the horizon:
Success in the generative AI era requires accepting that you may never control the environment where your ideas get consumed. Instead, you can influence the knowledge ecosystem that AI systems draw from, shaping how they think about and present information in your domain. Organizations that embrace this shift and develop robust GEO capabilities now will establish advantages that compound as generative AI becomes the dominant interface for information discovery.
This article was written by William Jepma on November 17, 2025
William Jepma is an editor, writer, and analyst at Solutions Review who aims to keep readers across industries informed and excited about the newest developments in Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), Business Process Management (BPM), Marketing Automation, and other WorkTech categories. You can connect with him on LinkedIn or reach him via email at wjepma@solutionsreview.com.
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