How to Stay Visible as Generative AI Changes Search

As organizations race to develop generative engine optimization (GEO) strategies, many are approaching AI visibility the same way they approached search engine optimization over the last two decades: Publish more content, optimize keywords and try to improve rankings. Yet the rise of generative AI is changing how information is discovered, evaluated and surfaced.

Members of the Senior Executive AI Think Tank—a curated group of executives, technologists, AI practitioners and digital transformation leaders—argue that many organizations are operating under flawed assumptions about how generative systems work. Their collective message is strikingly consistent: AI visibility is less about gaming algorithms and more about establishing trust, authority and credibility across the digital ecosystem.

According to a 2024 Gartner forecast on generative AI and search, traditional search traffic is expected to decline significantly as users increasingly rely on AI assistants and conversational interfaces to find information. As AI-generated responses become a primary gateway to information, organizations must rethink how they establish authority online.

The experts below explain why many GEO assumptions are misguided and where leaders should focus their efforts instead.

Authority Still Wins

For organizations looking for an entirely new GEO strategy, Peter Boyd, CEO of PaperStreet Web Design and a 20-year veteran of the SEO industry, offers a simple reality check: The fundamentals have not changed nearly as much as many people think.

Boyd says many companies have become distracted by new terminology and emerging AI trends while overlooking the core principles that have always driven online visibility.

“You still need to produce amazing content that is shared,” Boyd says. “You still need to be cited as industry leaders by other journals, articles, online publications and directories.”

He argues that many organizations have become overly focused on publishing content on their own websites while underinvesting in the broader ecosystem of authority-building activities that help establish expertise.

“One of the key missing elements that many companies are missing is citations from reputable sources,” he says. “Writing more on other well-known publications is the key.”

Rather than treating GEO as a technical exercise, leaders should continue investing in thought leadership, earned media, industry publications and high-value content that others reference and share.

Don’t Mistake GEO for a New Discipline

Goran Paun, Principal and Creative Director at ArtVersion, a Webby Award-winning design consultancy specializing in UX, digital strategy and brand systems, believes the industry’s fascination with GEO is creating unnecessary confusion.

“Every few years, the digital industry finds a new acronym and makes familiar work sound mysterious again,” Paun says.

The biggest misconception, he argues, is that GEO requires organizations to abandon existing digital strategies.

“A lot of companies are frozen because they think AI visibility requires an entirely new playbook,” he says. “It does not.”

According to Paun, AI systems evaluate many of the same underlying signals that have long influenced digital discoverability.

“The fundamentals are still very much the same since the inception of the web: Publish useful content, build authority and create something worth finding,” he says. “AI systems look for patterns of credibility, clarity and usefulness across many signals, not that differently from what search engines have always done.”

For organizations seeking visibility in AI-generated responses, Paun recommends focusing on three areas.

“The three parts still matter: quality content, proper technical structure and semantics, and third-party validation that shows the organization is a credible source in its field,” Paun says.

Becoming a Trusted Entity

Brock Murray, Co-Founder of seoplus+, says many organizations misunderstand the difference between visibility and authority.

“The biggest misconception is that GEO is a completely new discipline,” Murray says. “Most companies assume AI visibility requires a new playbook, when in reality, the fundamentals are still semantic relevance, topical authority, content quality and trust signals.”

Where many organizations go wrong, he says, is focusing almost exclusively on their own websites.

“Companies focus too much on their own website and not enough on their broader digital footprint,” Murray says.

That distinction matters because generative systems frequently synthesize information from numerous sources rather than relying on a single webpage.

“AI models often reference high-authority third-party sources when forming answers,” he says. “The real challenge is becoming a trusted entity, not publishing more content.”

Murray advises organizations to “build authoritative content, earn credible mentions, structure information clearly and create signals that humans and AI agents can easily understand and trust.”

Visibility Depends on Infrastructure, Not Just Content

Gaurav Rastogi, Senior Director of Enterprise Data Analytics, Data Science and Strategic Insights at Hertz, approaches the GEO discussion from an enterprise AI and data architecture perspective.

According to Rastogi, one of the most common misconceptions is treating AI visibility as a direct extension of SEO.

“Most organizations misunderstand generative AI discovery as an extension of SEO,” he says. “They assume rankings, keywords or content volume drive visibility.”

In reality, generative systems often rely on retrieval pipelines that prioritize semantic relevance, consistency and authority.

“If content isn’t retrieved, it can’t be cited,” Rastogi says.

This distinction shifts the conversation away from content production alone and toward the underlying systems that make information accessible and usable.

“Visibility is increasingly an infrastructure and systems problem,” he says. “If your data isn’t structured, consistent and accessible, AI systems won’t ingest or interpret it effectively.”

For leaders pursuing AI visibility, Rastogi recommends focusing on cloud-modernized data pipelines, structured information architecture and consistent entity signals.

“Visibility comes from high-quality signals across the lifecycle—not isolated optimization efforts,” he says.

“Organizations that are strategizing GEO like search by ranking keywords and citing sources deterministically are making a mistake.”

– Rajasekhar Chitta, Enterprise Transformation Leader at Cox Enterprises

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Trust Beats Manipulation

Rajasekhar Chitta, Enterprise Transformation Leader at Cox Enterprises, brings more than 25 years of experience leading large-scale technology transformation initiatives and AI adoption programs across enterprise environments.

He warns that many organizations continue approaching GEO with assumptions borrowed from traditional search marketing.

“AI visibility is earned through trusted signals, not search-style manipulation,” Chitta says.

The challenge, he explains, is that generative systems function differently from deterministic search engines.

“Organizations that are strategizing GEO like search by ranking keywords and citing sources deterministically are making a mistake,” he says. “Responses are probabilistic, shaped by training data quality and reinforcement signals.”

This means organizations cannot assume that citations represent endorsements or guaranteed rankings.

“Citations are contextual, not endorsements,” Chitta says.

Instead, organizations should focus on creating reliable, governed and authoritative information environments that support both AI systems and human users. He recommends maintaining accurate content, implementing governance controls and incorporating validation processes that reduce errors and misinformation.

“Sustainable visibility comes from credibility, auditability and trustworthiness,” Chitta says.

Be a Primary Source, Not an Aggregator

Chandrakanth Lekkala, Principal Data Engineer at Narwal.ai, spends his time designing large-scale AI, machine learning and cloud platforms that process massive amounts of enterprise data. From that perspective, he believes many organizations fundamentally misunderstand how generative systems work.

“The biggest misunderstanding is that generative AI copies content, like search engines do,” Lekkala says. “It does not crawl and rank; it synthesizes patterns from the training data.”

That distinction has important implications for visibility strategies.

“In this context, keyword stuffing and backlink tactics are hardly relevant,” he says.

Instead, organizations should focus on becoming authoritative sources of information that others reference and trust.

“Organizations should focus on producing authoritative, clearly structured, factually dense content that gets cited by credible external sources consistently,” Lekkala says.

He argues that the organizations most likely to remain visible are those that create original knowledge rather than simply repackaging information available elsewhere.

“Real AI visibility comes from being a primary source and not an aggregator,” he says. “A generative system endorses your organization’s information with confidence and consistency over varied queries not because of SEO, but through epistemic credibility.”

“Organizations should stop trying to manipulate AI and start becoming the best answer in their category.”

– Fabio Danze Montini, Investor and Owner of FDM Industrial Sales & Marketing SL

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Visibility Alone Is Not the Goal

Fabio Danze Montini, Investor and Owner of FDM Industrial Sales & Marketing SL, works extensively with industrial small and midsize enterprises and advises organizations on AI, sales and marketing strategy.

He believes many companies are focusing on the wrong objective.

“Many companies treat GEO as SEO with a new label: more keywords, more AI-written content, more attempts to ‘train’ the model,” Montini says. “That is the wrong assumption.”

According to Montini, generative systems do much more than rank information.

“Generative systems do not just rank pages; they retrieve, compare, compress and synthesize information from sources they consider useful, clear, current and credible,” he says.

This changes what organizations should prioritize.

“The biggest mistake is confusing visibility with trust,” Montini says. “Being indexed does not mean being cited.”

He argues that generic marketing language provides little value in an AI environment.

“Vague branding language is almost useless to AI,” he says.

Instead, organizations should focus on highly specific, verifiable information such as product documentation, use cases, FAQs, comparisons, customer proof points and technical details.

“What works is precise, verifiable knowledge,” Montini says. “Organizations should stop trying to manipulate AI and start becoming the best answer in their category.”

Ultimately, he notes, most industrial organizations care less about visibility metrics and more about business outcomes.

“Most industrial SMEs do not mind visibility and branding; they need conversations,” he says.

“The question is probably not ‘How do we get cited by AI?’ but ‘Would our content still be understandable, trustworthy and useful if nobody landed on our website first?’”

– Andre Shojaie, Founder of HumanLearn

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Make Expertise Machine-Readable

Andre Shojaie, Founder of HumanLearn, AI governance leader and Forbes contributor, believes organizations need to rethink the entire purpose of content creation in an AI-first world.

“I think the biggest misunderstanding is that GEO will work like a new version of SEO,” Shojaie says. “Generative systems are not just looking for pages to rank; they are trying to assemble a reliable answer from signals of clarity, consistency, authority, structure, context and corroboration across many places.”

That shift requires organizations to ask a fundamentally different question.

“The question is probably not ‘How do we get cited by AI?’ but ‘Would our content still be understandable, trustworthy and useful if nobody landed on our website first?’” he says.

The answer, according to Shojaie, involves balancing human credibility with machine readability.

“Organizations should focus less on gaming visibility and more on making their expertise machine-readable and human-credible at the same time,” he says.

Practical examples include transparent sourcing, clear definitions, named experts, original viewpoints and current examples.

“Visibility may belong less to the loudest brand and more to the clearest source,” Shojaie says.

Trusted Knowledge Wins

Sabarinath Yada, Business Architect Associate Manager at Accenture, has spent more than two decades leading modernization and enterprise transformation initiatives across insurance, healthcare and financial services organizations.

From an AI architecture standpoint, he says many organizations still misunderstand how generative systems access information.

“Generative systems do not browse the web like traditional search engines,” Yada says. “They rely on semantic patterns learned during training and, in some cases, retrieval-based methods that surface supporting content at response time.”

As a result, tactics borrowed from traditional SEO often fail to deliver meaningful outcomes.

“Applying outdated SEO thinking to generative AI can lead organizations to waste effort on superficial tactics,” he says.

Yada believes executives must distinguish between visibility and influence.

“Leaders must stop confusing visibility with influence,” he says. “AI systems do not reward brand size alone but prioritize relevance, structure, trust and evidence.”

For organizations looking to strengthen their position, he recommends developing knowledge assets that serve both people and machines.

“The real advantage lies in building content and knowledge assets that are valuable to people, understandable to machines and governed with discipline,” Yada says. “In the AI era, visibility is not about being everywhere but about becoming the most trusted source.”

Credibility Becomes the Competitive Advantage

Rodney Mason, Chief Marketing Officer at Minty, an AI-powered shopping platform with hundreds of millions of user opt-ins, sees many organizations making the same strategic mistake.

“Many organizations assume GEO is the new SEO—that if they optimize keywords, publish more content or engineer citations, AI systems will automatically surface them,” Mason says. “That is an oversimplification.”

Instead, he argues, generative systems increasingly evaluate broader signals that indicate trustworthiness and usefulness.

“Generative systems increasingly evaluate signals of authority, originality, trustworthiness, structured data and real-world relevance—not just content volume,” he says.

Another common misunderstanding is treating visibility as a ranking challenge.

“Visibility is not primarily a ranking problem,” Mason says. “AI models often synthesize information from multiple sources and favor content that is unique, factual, current and consistently referenced across the web.”

The organizations most likely to thrive, he says, will be those that create information others cannot easily replicate.

“Organizations that want to be a source AI wants to cite must create proprietary data, publish original research, maintain structured and machine-readable content, build brand authority and ensure information is accurate and accessible,” Mason says. “In an AI-first world, the most visible organizations will be the most credible and useful.”

10 Strategies for Building AI Visibility That Lasts

• Earn authority beyond your own website. Third-party citations, industry mentions and thought leadership remain critical signals of expertise.
• Focus on fundamentals instead of chasing acronyms. Quality content, technical structure and credibility continue to drive discoverability.
• Build a trusted digital footprint. Become a recognized entity across the broader web, not just on owned properties.
• Treat visibility as a systems challenge. Understand the importance of structured data, retrieval readiness and modern information architecture.
• Prioritize trust over optimization tactics. Credibility, governance and auditability create sustainable AI visibility.
• Create original knowledge. Become a primary source rather than republishing information available elsewhere.
• Deliver precise and verifiable information. Focus on evidence, use cases and customer proof instead of promotional language.
• Make expertise understandable to both humans and machines. Stress transparency, context and machine-readable knowledge.
• Develop trusted knowledge assets. Build content ecosystems grounded in relevance, structure and evidence.
• Invest in proprietary insights and research. Original data and unique expertise will increasingly determine AI visibility.

From Visibility to Trust

The race to optimize for generative AI has prompted many organizations to search for shortcuts, tactics and formulas that will improve visibility. Yet the members of the Senior Executive AI Think Tank consistently point to a different reality: Generative systems reward trust, authority, clarity, structure and originality far more than manipulation or volume.

As AI increasingly becomes the interface between organizations and their audiences, leaders should focus less on trying to influence machines and more on becoming genuinely valuable sources of knowledge. The organizations that succeed in an AI-first world will not necessarily be the loudest or the most prolific—they will be the most trusted.