Whenever I type a question into an AI engine, I’ve noticed that the engine doesn’t just search for the exact words I typed. Instead, it explores a broader spectrum of possibilities. This behavior intrigues me.
Recently, I came across a fascinating study by Profound. They monitored 10,000 prompts across various AI platforms like ChatGPT, Copilot, and Perplexity over two weeks. The findings highlighted remarkable differences in how these AI engines search and process queries.
I keep hearing people suggest that AI understands their brand. It really doesn’t. Let’s clarify that upfront.
What AI actually does is pattern-match at a large scale. It condenses your brand’s positioning, product features, and tone into a series of signals that can be rapidly retrieved and remixed.
These patterns originate from two main processes:
Training: This involves what the AI model has previously absorbed.
Retrieval: This pertains to what the model can access in real-time from the current web and other sources.
The concept of “AI SEO” isn’t about creating a new channel; rather, it presents a representation challenge: which version of your brand is encoded, retrieved, and reiterated.
Many brands are already participating, but they often lack a deliberate strategy.
The Internet Has Evolved Beyond a Library
Traditional SEO operated like a library issue: you publish, Google indexes, and human searches lead to discovery.
Today’s AI-driven search is more conversational, gradually moving visibility from simple head terms to context-rich prompts like:
“With these constraints”
“Similar to this competitor but more affordable”
“Which tool suits a team like mine with these criteria?”
“Based on what you know about me, recommend…”
My role is to ensure that my brand stands out as the most relevant match within a model’s memory and retrieval pipeline.
It’s not about being ranked; it’s about how you’re represented.
AI relies on associations, not opinions.
From Keywords to Entities to Embeddings
Classic SEO targeted keywords, moved to entities, and now AI operates at a deeper level by translating entities into vectors.
This means my brand becomes a point in a dimensional space—close to some concepts, distant from others, shaped by repeated associations in content and mentions.
If my brand is consistently linked with terms like “enterprise analytics,” “real-time dashboards,” and “data governance,” it clusters near those concepts.
If my messaging leaks into unrelated areas due to repetitive content fatigue, my brand’s vector becomes less precise, resulting in lower confidence and a higher chance of being overshadowed by a competitor who signals more clearly.
Three Layers of AI Brand Visibility
Before tackling “AI SEO” issues, I need to pinpoint which layer my brand is failing on. Different strategies are required for each layer.
Training Layer
This encompasses my brand’s historical presence—press releases, blogs, documentation, reviews, even forgotten forum threads.
While full control isn’t possible, I can minimize fragmentation by updating past mentions to foster a consistent online identity.
Grasp the training layer by asking an AI chatbot to describe my brand with web search disabled.
Retrieval Layer
This involves my brand’s active web presence—indexed pages, product feeds, APIs—where traditional SEO of crawling, indexing, and rendering is crucial for defining accessible information.
Grasp the retrieval layer by conducting branded intent and market category prompts regularly using a large language model tracker, and observing consistently cited sources.
Generation Layer
In AI Overviews, AI Mode, or ChatGPT instances, my brand’s paragraph only appears if it’s essential.
I need to ask myself: what unique, quotable content ensures the LLM mentions my brand?
Grasp the generation layer by analyzing brand mentions in responses and their semantic relationships using LLM tracker data.
Four Mechanics that Decide What AI Says
Consider these mechanisms as the subtle forces shaping representation across the layers.
1. Consolidation (Identity Resolution)
AI systems consolidate brand references if there’s an obvious connection.
My brand might have varied forms:
A brand name (inconsistent spacing or casing).
A legal name.
A domain name.
An abbreviation.
A legacy name.
Humans merge these effortlessly; models don’t. They consolidate based on patterns, not intent. Every inconsistency spells fragmentation.
Allowing multiple representations of my brand divides its visibility signals.
2. Co-occurrence (Association Formation)
Models learn through co-occurrence:
Brand + category
Brand + use case
Brand + audience
Brand + competitor
Consistent pairing strengthens associations; inconsistency weakens them. It’s that straightforward.
3. Attribution (Who Says It, Where)
Models monitor who describes the brand, by whom, and in which context.
First-party mentions hold one layer; third-party mentions are another. High-trust sources carry greater significance.
This isn’t due to “authority” in traditional SEO, but because these sources frequently emerge within reliable contexts in both training data and retrieval corpora.
4. Retrieval Weighting (What Gets Used in AI Answers)
When generating answers, AI systems choose which data to use, based on clarity, relevance, uniqueness, and extraction ease.
If essential facts are hidden between metaphoric lines, models will source elsewhere. Explicit repetition and structured, direct facts foster selection by the model.
You’re Not Writing Poetry, You’re Building a Graph
In both on-page and off-page content, core entities must be unmistakable: my brand, products, categories, audience, and differentiators.
Crafting a consistent, clear, canonical position ensures that machines comprehend it without errors.
Brand is a market category for audience needing use case, differentiated by proof.
I must honestly evaluate if my answers could apply to competitors, or better yet, ask AI to determine that. If validation is positive, a rewrite makes it distinctively me.
Subsequently, roll out the positioning consistently across various media: on-page with structured chunks, in data references, in “sameAs” links, industry publications, partner sites, user reviews, community discussions, and social media.
Deliberate repetition and reduction of unnecessary terminology variation fortifies associations, compounding strength over time.
AWarn against brand drift where inconsistencies allow for misrepresentations and information gaps invite AI hallucination. Vigilance on content edges, consolidation, or removal of conflicting pages is crucial.
It’s not about outsmarting AI, but minimizing entropy.
If this sounds mundane, that’s a positive sign. Brands poised to thrive in the AI era won’t rely on clever tactics but on disciplined execution.
Inconsistent answers lead to your brand’s misrepresentation. AI systems might unintentionally pass along an unintended version of your brand to potential customers.
First 5 Steps to AI Brand Visibility
1. Establish your brand’s canonical bio: Define spacing, casing, abbreviation norms, and clear positioning for the brand name.
2. Implement graph-based schema: Identify linkage between your brand (consolidated by “sameAs”) and vital entities.
3. Make proofs easily quotable: Ensure that awards, benchmarks, customer figures, policies, and notable brand details are prominent and retrievable.
4. Rectify historical identity fragmentation: Address and unify past mentions to reinforce canonical positioning wherever possible.
5. Intentionally repeat key associations: Brand with category, use case, audience, competitor. Not only on your site, but expand on high-trust third-party sites.
It’s Not About You
If AI systems lack confidence in resolving your brand representation, they default to a safer choice, typically a competitor sending clearer signals. This doesn’t mean the competitor is superior, just more machine-friendly.
AI doesn’t require perfect understanding of your brand; it needs an approximation accurate enough to endorse you. My job is to manage that approximation through consistency, structure, and strategic distribution.
Not by overwhelming content production, but by ensuring my brand’s story is clear and unmistakable.
I’ve often heard from paid search managers that dealing with AI agents can feel repetitive. Imagine exporting your performance data, pasting it into a chat window, receiving a useful answer, and then having to repeat the process every day. That doesn’t sound like automation, does it? It’s just good old manual work with a tech twist.
Interestingly, the issue isn’t with the AI tools themselves. Many of them excel in data analysis when they have access to the right information. The real hurdle is providing this data to them in real time, without constantly needing a human to copy it over. This data wall explains why many PPC accounts today operate nearly the same way as they did before the advent of AI agents.
Every ad platform tends to operate in isolation. Google Ads might record conversions, while your CRM notes whether those leads are qualified, and your inventory system checks stock availability. Without deliberate integration, they each function in their own silo. PPC managers have traditionally bridged this gap manually with regular exports and cross-referenced spreadsheets. Although this worked while humans managed it, it doesn’t hold up when an AI agent needs to take action in real time.
Consider a keyword with good volume and a satisfactory CPA, according to Google Ads. But in HubSpot, these could be marked as disqualified leads. The AI, lacking this context, continues its work blissfully unaware, leading to unnecessary budget spend until someone catches the discrepancy during the monthly review. This is a data access problem that better prompts alone can’t fix; a robust data pipeline is essential.
The Model Context Protocol (MCP) is here to address this by providing a standardized way for AI clients to connect to various data sources. Before MCP, one would need to build separate connectors for systems like Google Ads, CRMs, and inventory systems, but MCP simplifies this connection significantly.
Now, with MCP, an AI agent could efficiently work with Google Ads and CRMs like HubSpot, cross-referencing conversions with CRM dispositions. This setup can automatically adjust bids based on data, eliminating the need for human intervention in the reporting process, saving valuable time.
Yet, having an open pathway to data without safeguards introduces new risks. Imagine an AI with write access to a Google Ads account. Without defined parameters or constraints, actions taken by the AI could become unpredictable. This unpredictability is why guardrails must be established around the AI, rather than relying on the AI tool itself to handle this responsibility.
Optmyzr’s MCP allows advertisers to control what actions the AI can take, ensuring a balanced approach to AI management. This ensures the AI can effectively manage campaigns while staying within safe operational parameters.
The MCP from Optmyzr integrates these controls into its system, allowing AI agents to perform complex tasks such as executing a full Rule Engine strategy from a simple directive while ensuring the appropriate checks and balances are in place. The result is an agent capable of operating with the precision of a seasoned PPC strategist across your entire portfolio, offering a level of intelligence and safety unattainable through raw API access alone.
For those who wish to explore the possibilities of AI with care, Optmyzr’s MCP provides a secure and efficient pathway, integrating seamlessly with tools like Claude Desktop or ChatGPT for a comprehensive AI-powered approach to managing marketing campaigns effectively.
I’ve recently delved into the fascinating world of conversational commerce AI, and I can’t help but feel excited about how it’s changing the shopping landscape. From how we discover products to the actual purchasing process, this technology is redefining our retail experiences.
What really intrigues me is what these changes mean for brands operating in an AI-dominated retail space. The implications are huge, and it could very well spell the end for traditional shopping carts as we know them.
In exploring the world of agentic AI, I’ve come across a startling prediction from Gartner: by the end of 2027, more than 40% of these projects will have been canceled. This isn’t due to the technology being insufficient; it’s because of the human factors involved. The real issue lies not with the tech, but with our deployment strategies and the absence of essential human insights.
Gartner’s research, involving over 3,400 organizations that are currently investing in agentic AI, makes it clear that the downfall isn’t in the capabilities of AI itself. It’s in the decisions we, as humans, are making. Anushree Verma from Gartner notes that most of these AI projects are merely hype-driven experiments, lacking in strategic direction and governance.
This brings a critical reminder for those of us in marketing: agentic AI can optimize and scale tasks exponentially, yet without a knowledgeable human behind it, the technology is as good as the strategy guiding it. We need agents that can handle audience selection, content generation, and journey orchestration effectively, but we must steer these agents with insight and responsibility.
If we’re spurred by fear of missing out (FOMO), we might find ourselves hastily deploying AI solutions. This rush can lead to poorly constructed workflows and inadequate data strategies, resulting in agents implementing erroneous actions at inappropriate times. FOMO isn’t a sustainable strategy; it’s a costly oversight.
Another pitfall presented by Gartner is what’s termed ‘agent washing.’ This is where existing chatbots are disguised as agentic AI without delivering authentic autonomous functionality. As marketing teams, if we invest in these disguised solutions, we’re essentially falling for dressed-up automation without real AI benefits.
Deploying AI prematurely can be damaging. Gartner anticipates that by 2026, many companies might harm their customer relationships through misguided AI applications, leading to eroded trust and damaged brand reputations. Our role as marketers should be to prioritize strategy and judgment alongside technological advancements.
One of the gravest challenges we face is the potential erosion of critical thinking brought about by reliance on AI. Gartner predicts half of the organizations will need to reassess competencies, ensuring that our human ability to question and evaluate AI outputs remains sharp and undiminished.
In this rapidly evolving landscape, the successful marketer will be one who integrates AI while maintaining a leadership role. This encompasses being a multidisciplinary thinker who utilizes AI to transcend traditional roles, driving strategy and ensuring that AI recommendations align with our brand’s vision and values.
As we embrace the agentic era, it’s imperative that we balance technological advancements with human insights. We shouldn’t slow down but rather be deliberate—ensuring that our AI endeavors are guided by robust human judgment to harness true value, protect customer trust, and avoid costly missteps.
I’ve always found brand positioning to be an intricate dance of claims, proofs, and strategic framing. While AI can validate claims, it won’t decide on the conclusions that best elevate your business. Let me share how framing transforms proof into brand loyalty.
In today’s digital world, every brand has its arsenal of claims and underlying proofs scattered across its digital presence. AI engines like ChatGPT and Google’s AI can verify these, but they hold no narrative power to create an engaging story for your brand.
Often, there’s a disconnect between what your audience desires and what brands or AI understand. The missing link? A powerful frame that converts disjointed data into a compelling brand narrative.
Here’s where I introduce the claim-frame-prove (CFP) approach. Claims and proofs are mechanical, but framing adds that strategic layer necessary to craft your brand’s narrative.
Claims and proofs are mechanical tasks AI can handle, but creating a strategic frame is your brand’s unique prerogative.
Building your brand through CFP means understanding that AI can link known facts but cannot make that creative leap your brand requires. AI connects the dots logically but lacks the ability to reach a commercially beneficial insight.
Consider the alphabet analogy: while C is an apparent commercial reach, J represents a nuanced insight, and Q symbolizes a bold vision your brand can aspire to.
I’ll illustrate with some personal examples. My work in answer engine optimization demonstrates this journey from mere understanding to unique brand positioning.
A + B → C
A: I coined answer engine optimization in 2017. B: I also run a brand engineering firm. AI arrives at the simple, logical conclusion: I’m connected to AEO implementation. While true and functional, it lacks depth.
A + B → J
By pushing further, the narrative evolves. J: I might be the only practitioner with extensive insights from a decade’s worth of operational data.
This move from A and B to J is vital. It’s about identifying which non-obvious insight fosters brand growth and constructing a logical link from accepted realities to this aspirational leap. That logical bridge is essential for AI to consider it factual, rather than mere self-promotion.
Why AI Can’t Decide What’s Best for Your Brand
AI won’t instinctively choose the best narrative for your brand—that responsibility is yours. Even as AI gets more sophisticated, it lacks the commercial insight to select paths that benefit your brand uniquely.
A creative marketer makes two critical moves: discovers imaginative insights and aligns them strategically with brand goals. Not a feat even the most evolved AI can match, as it lacks the personal stake in this narrative crafting.
I use an approach called “empathy for the machine,” which helps brands create content that AI can easily comprehend and relay, rather than leaving connections for AI to interpret independently.
This method enables a three-tiered communication with AI, evolving from mere proof of claims to frames that the AI can transmit seamlessly to your audience.
Level 1: Scattered Proof of Claims
Many brands rest here—proofs exist in separate spaces, disconnected, leaving AI to infer relationships. The reality is that without explicit links, much of this value is lost.
Without these connections, AI struggles to assert your brand’s credibility, potentially leaving valuable insights untapped.
Level 2: Connected Proof of Claims
At this stage, connections via copy, hyperlinks, and schema are established, significantly reducing the AI’s workload and increasing your brand’s credibility.
Proper connections allow AI to confidently present your brand’s claims as facts, significantly enhancing its visibility and competitive positioning.
Level 3: Framed Proof of Claims
This is where strategic framing really takes shape—bridging claims, proofs, and strategic insights to position your brand distinctly in the market.
With well-framed claims, AI doesn’t just confirm but actively advocates for your brand’s superiority, making your voice the narrative AI conveys to the world.
I’ve learned that SEO is not just about getting noticed — it’s about earning trust and becoming the top choice.
Wil Reynolds, founder and CEO of Seer Interactive, really got me thinking about how artificial intelligence is changing the game for us SEOs.
In his SEO Week session, “SEO is a performance channel, GEO isn’t. How do you pivot?” he emphasized that too many of us are chasing the wrong goals and crafting content that people simply don’t buy into.
Marketing isn’t just about being seen
Reynolds challenged us to look beyond visibility to what truly drives success — belief in our brand.
“Marketing was never just to be seen or be visible,” he said. “It’s about transforming that visibility into brand belief… and ultimately, being chosen.”
He outlined a crucial journey for marketers: being seen, being believed, and then being chosen.
Even when we hit that number one ranking, the job isn’t done. As Reynolds put it, “Job’s not finished.”
Low-quality marketing is everywhere
Reynolds made me rethink some of the standard marketing tactics we use that don’t actually provide value.
He criticized methods like automated outreach, saying, “That’s not marketing.”
I found myself questioning my past work habits — was it really marketing?
The industry is producing ‘zombie content’
Reynolds shed light on our tendency to churn out templated content just to rank, equating it to “zombie content.”
Lists like “best restaurants in Minnesota” when such searches aren’t even realistic? It truly made me think about content creation differently.
Short-term tactics vs. long-term brand building
Reynolds pointed out the stark contrast between short-term wins and the sustained success of building a powerful brand.
“Some focus on winning now, others play the long game,” he explained.
He made it clear that chasing immediate results often leads to producing work nobody wants.
SEO success doesn’t translate to AI visibility
Reynolds illustrated this with an example about “ethical jeans,” showing how AI results can diverge significantly from SEO.
A brand could rank highly on Google yet fail to gain traction in AI models due to a lack of genuine credibility.
Visibility without belief doesn’t lead to outcomes
Just having visibility doesn’t guarantee anything if people don’t trust or believe in us. A reality check I needed.
This visibility is merely a stepping stone, not the end goal.
What people say matters
Reynolds encouraged us to listen actively to how people discuss brands, especially on platforms like Reddit.
Despite how brands might try to show themselves as leaders, user sentiment can reveal a drastically different picture.
The wrong metrics are being measured
Many of us fall into the trap of focusing on easy-to-track metrics instead of those that tell the real story.
Reynolds suggested that if our visibility isn’t driving results, we’re looking at the wrong data points.
Watching real users changes the picture
He emphasized the breakthroughs that come from observing actual users interact with AI tools. It’s eye-opening and transformative.
Start with your brand
Understanding exactly how our brand is perceived in AI-generated content is vital.
If we’re not ensuring our brand is accurately represented, all our marketing efforts might be in vain.
AI can shape your brand narrative
Reynolds shared a personal experience where AI misrepresented his company, prompting him to take action by publishing clear, corrective content.
There is too much content
With all this content flooding the digital space, I’ve realized the importance of stepping back and curating high-quality material instead.
Rethinking performance
Reynolds drew attention to the varying effectiveness of different traffic sources, reminding me to focus on the ones that truly convert.
A final question for marketers
He left us pondering: Are we prepared to give up a fraction of visibility for the sake of being more credible?
I find it quite fascinating how the world of search has transformed over the years from manual PPC efforts to AI-driven systems. Reflecting on Ginny Marvin’s journey offers a glimpse into these dynamic changes and underscores the importance of staying curious and adaptable as marketers.
My journey into PPC wasn’t fueled by a master plan but rather by a desire to reinvent myself professionally. Transitioning from print publishing and advertising sales, I found myself at a crossroads when the startup magazine I had helped establish ceased operations. That pivotal moment pushed me towards digital marketing, starting from entry level.
Starting fresh meant embracing the unknown. As Marvin put it, she didn’t know what she was doing initially, which makes her story relatable for anyone starting anew. This fresh start paved her path into search marketing, eventually leading her to significant roles at Search Engine Land and Google as the Google Ads Liaison.
During our interview, Marvin shared insights into the evolution of paid search, highlighting common misconceptions marketers still hold, and emphasized how the next era of search will value curiosity over control.
Interestingly, PPC clicked for me faster than SEO. My initial foray into the industry was through SEO at a small agency, but I quickly discovered my passion when the paid search manager took a vacation, and I temporarily managed the campaigns. This experience showed me the power of PPC’s speed and measurability, especially coming from a print background where results were slow and uncertain.
Marvin observed that Google’s clear focus and rapid iteration were key to outpacing competitors like Yahoo and Microsoft. Google’s relentless enhancement of its offerings to align with advertiser needs set it apart and solidified its leadership in the industry.
I remember the early days of PPC being a manual slog full of exhaustive keyword lists and precision-targeted campaign strategies. We spent hours meticulously crafting keyword combinations, but today’s campaigns are more sophisticated and goal-oriented, aligning more naturally with business objectives rather than conforming to platform constraints.
When Search Engine Land was in its infancy, Marvin was also establishing her footprint in the search field. The platform quickly became essential for industry news, insights, and expert analyses, fostering professional growth by making information accessible.
One standout characteristic of the search community, as Marvin noted, is its openness to sharing and collaboration. People have always been generous about sharing their experiments, successes, and failures, recognizing that ongoing learning benefits everyone. This spirit of community has been a cornerstone in my own career development.
Regarding AI, Marvin asserts that it’s not as novel as many perceive. Although the rapid advancements fueled by large language models seem sudden, machine learning has been embedded in systems like Google Ads for years, refining aspects like Smart Bidding and close variants.
The real shift lies in consumer behavior, where search patterns have become increasingly complex and diverse. With people using images, voice, and multimodal inputs, modern search engines understand intent beyond simple keywords, necessitating a comprehensive view of the customer journey.
Despite all these changes, the essence of search success remains tied to business results. What’s different now is the enhanced ability to accurately measure outcomes and align campaign activities with strategic business goals, highlighting the critical role of data and first-party signals.
Looking ahead, Marvin champions curiosity as the trait that will define successful marketers over the next two decades. Adaptability, understanding customer behavior, and proactively learning new technologies like AI will keep marketers ahead of the curve.
Marvin candidly remarks that while PPC marketers often claim to embrace change, they can be resistant when major shifts occur. Her advice is to adopt a long-term perspective because seemingly abrupt changes often have deep-seated, gradual developments.
Experimentation is key, according to Marvin. Even if a new feature doesn’t yield immediate success, dismissing it entirely could be shortsighted. As platforms and capabilities evolve rapidly, what didn’t work before might succeed now, and clinging to outdated methods could hinder progress in the evolving search landscape.
Reflecting on her career, Marvin expressed pride in the resilient and collaborative nature of the search community. Her contributions at Search Engine Land and Google have always been geared towards fostering an informed and empowered marketing community. To her, “by marketers, for marketers” is more than a motto; it’s a driving mission.
Have you ever wondered which domains lead the way in the world of AI citations, specifically with giants like ChatGPT and Gemini? I’ve delved into a staggering 58.6 million AI citations to uncover the patterns and top-performing sites dominating this space. Join me as I share insights into these trends and explore strategies to boost your own citation share.
The AI industry is bustling with innovation and adaptation. Identifying which domains stand out can give us valuable insights into the digital landscape’s future. Let me walk you through the journey of how these insights can be leveraged for growth and visibility in this ever-evolving domain.
As I delve into the concept of net information gain, I’m uncovering its immense importance in Answer Engine Optimization (AEO). This isn’t just a theoretical pursuit; it’s about translating original insights, real experiences, and clear opinions into a framework that enhances rankings and AI citations.
Understanding net information gain transforms how we approach content creation. It’s not just a buzzword; it’s a tangible metric that drives meaningful AI advancements. By focusing on genuine informational value, I can elevate content beyond mediocrity and into a realm where it truly resonates with both users and algorithms.
I’ve observed that when I infuse content with authentic insights and leverage my personal experiences, search engines and AI systems notice. It’s this distinct edge that propels content to the forefront, ensuring it isn’t just seen but valued and referenced.
Embracing net information gain is my key strategy for thriving in the competitive AI landscape. By consistently prioritizing substance over superficiality, I position myself — and my content — to challenge and outshine AI content mediocrity.
