Monica Tomasso, Founder of Monic AI Systems, shares her perspective on AI visibility, business discovery, and why companies need to become easier for AI to understand, cite, and recommend.
Monic AI Systems was recently featured in a Cllimber interview with Monica Tomasso, Founder of Monic AI Systems. In the interview, Monica discusses how AI is changing business discovery and why companies need to think beyond traditional SEO as buyers increasingly ask AI tools for recommendations.
The feature highlights Monica's work around AI Visibility and the importance of helping businesses become easier for AI systems to understand, trust, cite, and recommend.
Featured commentary on GoalSetting.co on choosing early signals that keep team goals on track — and why cross-model recommendation consensus is the leading indicator that traditional metrics miss.
One of the most valuable early indicators I've found is what I call model consensus. Long before a business sees changes in traffic, leads, or revenue, you can often see whether AI systems are beginning to understand and describe the company consistently.
We measure how often platforms like ChatGPT, Gemini, Claude, and Perplexity agree on who a company is, what it does, and when it should be recommended. If one platform recommends a business but three others don't, the signal is still weak. When multiple systems begin surfacing the same company for the same buyer questions, that's usually an early sign that visibility efforts are working.
What changed our weekly reviews was focusing less on volume metrics and more on directional confidence. We weren't asking, Did traffic go up this week? We were asking, Did more AI systems reach the same conclusion about this business? That measure proved valuable because it showed progress weeks or even months before traditional outcomes appeared. By the time recommendation rates improved across multiple models, increases in visibility, engagement, and inquiries often followed.
AI doesn't rank. It selects. Model consensus is often the earliest signal that selection behavior is beginning to change.
Featured guest commentary on SimplySansu.com on why AI visibility is no longer a ranking problem but a conversation problem — and why most businesses fall out of the buyer conversation right before the recommendation moment.
For twenty years, businesses optimized for a search result. A keyword, a ranking, a blue link on page one. Today, that moment barely matters. Businesses are no longer being evaluated by a result — they are being evaluated inside a conversation.
When a buyer asks ChatGPT, Gemini, Claude, or Perplexity a question, they rarely stop after the first answer. They ask follow up questions, compare options, validate recommendations, and narrow their decision over multiple turns. The challenge is no longer showing up once. The challenge is staying in the conversation.
Traditional SEO measured rankings. AI systems measure confidence. Behind every AI generated answer is a model trying to decide how sure it can be about recommending something, pulling from websites, interviews, reviews, press mentions, podcasts, FAQs, and structured content scattered across the web. The old question was: can Google find you? The real question now is: does AI understand you well enough to recommend you?
Picture a real buyer journey turn by turn — Turn 1: How do I show up in AI search? Turn 2: What tools help with AI visibility? Turn 3: Which platform is best for a small marketing agency? Turn 4: Who should I hire? Different brands show up at every stage. The names that appear in turn one are rarely the names that survive to turn four. The revenue is not hiding in question one. It is hiding in question four — the turn where AI is no longer educating, it is recommending.
The winners in this next phase will not necessarily have the highest rankings. They will be the businesses that AI can confidently explain, compare, validate, and recommend throughout an entire buying conversation, from the first curious question to the final decision.
Featured in Informatics Magazine's roundup "25 Key Metrics That Deliver Value to Organizations" — on why Recommendation Rate is the most valuable KPI for AI visibility, measuring whether AI systems actively recommend a brand, not just mention it.
The most valuable KPI we've developed is Recommendation Rate, the percentage of high-intent buyer questions where an AI system actively recommends a brand. Traditional visibility metrics measure whether you're present. Recommendation Rate measures whether you're selected.
We track prompts across ChatGPT, Gemini, Claude, and Perplexity and score outcomes in three stages: Seen, Recommended, and Chosen. A business might appear in 40% of prompts, be recommended in 6%, and be the preferred choice in only 1%. That distinction matters because buyers increasingly ask AI systems who to hire, trust, compare, or buy from. Being mentioned is awareness. Being recommended influences consideration. Being chosen influences revenue.
What makes this KPI particularly valuable is that it converts an abstract concept, AI visibility, into something measurable and actionable. When recommendation rates improve, we can often trace the change back to specific initiatives such as stronger thought leadership, better structured content, increased third-party citations, or clearer positioning.
The metric has become a leading indicator of future discoverability because it measures not whether AI can find you, but whether AI is confident enough to recommend you.
Featured in PR API's research report "What AI Cites: Marketing PR 2026" on how earned, third-party coverage and expert commentary generate citations inside AI systems faster than traditional blog content.
And this is where earned, third-party coverage reasserts itself. Monica Tomasso (Connectively) found it measurably: "Several earned media placements and expert contributions generated citations inside AI systems faster than traditional blog content. A single third-party mention on a trusted publication influenced AI understanding more than multiple self-published articles." Nikita Khandheria of ERIA points to the research: "AI platforms gravitate toward expert commentary, educational articles, and industry research rather than promotional content," citing the Muck Rack study that generative AI relies heavily on earned media and journalism. Pavankumar Kamat of Panto AI ties it together: "LLMs cite signal, not brands. Content that's canonical, uses structured metadata, and is independently corroborated gets priority."
Force 6 - The preconditions everyone forgets
Before any of the above, two things have to be true: the model has to be able to read you at all, and it has to know who you are. Hold Gemma next to Peter Moon and Monica Tomasso and you have the real finding: earned and credentialed sources dominate where trust is scarce and stakes are high, and the brand's own structured, data-rich pages dominate where the buyer just wants a fast, checkable B2B answer. The category decides.
What to take from this
It is not one rule. AI cites what it can extract and verify, but the winner depends on your category, your structure, and whether you have handed the model a clean, sourced fact it is not afraid to repeat. The work is no longer to impress a reader. It is to be quoted by a machine, which means leading with the answer, naming your numbers, dating your claims, and being readable in the first place. Stop writing to impress. Start writing to be quoted.
Featured in BacklinkBuilding.io's roundup "25 Unconventional Approaches to Optimize Content for Answer Engines Beyond Traditional SEO" — on testing how AI systems describe and recommend a business before writing any content, and building conversational content ecosystems instead of isolated SEO pages.
One unconventional approach we started using was testing how AI systems actually describe and recommend a business across different conversational prompts before creating the content strategy itself. Instead of beginning with keyword targets, we would ask platforms like ChatGPT, Claude, Gemini, and Perplexity the kinds of questions real buyers ask when comparing vendors, evaluating trust, or narrowing down decisions.
What stood out was that AI systems consistently favored businesses with clearer narratives, stronger contextual explanations, and more reinforcing information across multiple sources, not the businesses with the most aggressively optimized pages.
That changed our approach completely. Rather than creating isolated SEO content designed primarily for rankings, we started building conversational content ecosystems around real buyer questions, founder expertise, FAQs, comparisons, educational content, and consistent business descriptions across platforms.
The goal shifted from "ranking pages" to helping AI systems confidently understand, explain, and recommend the business. That is a very different mindset than traditional SEO, which historically focused much more heavily on keywords, backlinks, and traffic volume.
Submitted commentary for a TechRound.co.uk comment piece on what a winning strategy looks like in 2026 as Google AI Overviews, ChatGPT Search, and Perplexity fundamentally change how users find and consume information online.
One of the biggest shifts I am seeing is that AI search is no longer a one query experience. Users ask a question, refine it, compare options, and continue the conversation in the same session. That means brands are no longer competing just to rank once. They are competing to stay in the conversation.
What I am seeing in practice is that the businesses performing best in AI search usually have enough depth and consistency for AI systems to keep returning to them as the conversation evolves.
I often describe this as conversational persistence or conversation survivability.
Companies optimize only for the first question. The real opportunity is building enough connected expertise, trust signals, and conversational content for AI systems to confidently continue recommending the brand throughout the buyer journey.
Submitted commentary for a TechNewsWorld story on the spike in DuckDuckGo installations following Google's push of AI into search, and whether consumers are pushing back against AI-driven discovery.
I think people are overreacting a little to the DuckDuckGo story.
Yes, there was clearly a spike after Google pushed AI harder into search. But I do not think this means consumers suddenly hate AI. I think it means users want more control over when AI shows up and how much it takes over the experience. The bigger shift happening is actually behavioral.
People are no longer searching in single queries. They are talking to AI systems conversationally. They refine questions. They compare. They ask follow ups. That changes the entire discovery process online. And honestly, most businesses are not prepared for that yet.
Google still processes billions of searches per day, and ChatGPT itself is now handling massive query volume. So the trend toward AI assisted discovery is very real. The question is not whether consumers will use AI. They already are. The tension is trust.
Users want convenience, but they also want transparency. They want to know where information came from, whether the answer is biased, and whether alternative viewpoints still exist. When AI summaries feel overconfident or remove too much exploration, some users push back.
I also think AI summaries are already impacting the internet economy in a meaningful way. Publishers and businesses are seeing traffic patterns shift because answers are increasingly consumed inside AI interfaces instead of through clicks to websites.
So I would frame this less as consumers rejecting AI, and more as consumers negotiating what role they want AI to play in search.
Featured in AMA Phoenix's roundup "Learning From Competitors: 18 Innovative Ideas From Business Leaders" — on how studying the way AI systems describe and recommend businesses (not just competitor websites) reshaped our approach to AI visibility.
One of the biggest mindset shifts came from studying not just competitor websites, but how AI systems were actually describing and recommending businesses in real buyer style searches.
In several audits, we found competitors appearing in AI generated answers even when their websites were not particularly strong. What stood out was that they had clearer descriptions across third party platforms, stronger supporting content, and more consistency in how the business was represented online.
That completely changed how we approached visibility.
Instead of focusing only on rankings or traffic, we started focusing on helping AI systems understand a business more clearly over time. That meant creating content around real buyer questions, strengthening business descriptions across platforms, and making sure key ideas appeared consistently beyond the company website.
One of the most interesting results was that some businesses began appearing in AI generated recommendations within weeks, not because their rankings dramatically changed, but because the overall picture AI saw became clearer and more trustworthy.
Featured in BacklinkBuilding.io's roundup "26 Experiences with Disappointing SEO Tools and the Alternative Solutions That Worked Better" — on why traditional SEO platforms miss AI visibility, and the workflow that replaced them.
One of the biggest disappointments with traditional SEO tools was realizing how little visibility they provided into how AI systems were actually describing and recommending businesses.
Most highly rated SEO platforms are still primarily built around rankings, keywords, backlinks, and click based search behavior. Those metrics are still useful, but they do not fully explain why certain businesses appear in AI generated answers while others do not.
What we kept running into was a gap between traditional SEO performance and actual AI visibility. In some cases, businesses with weaker rankings were still appearing more often in conversational search because they had clearer descriptions, stronger third party signals, and more consistent contextual information across the web.
That led us to build a different type of workflow focused less on rankings alone and more on tracking how businesses appeared across AI systems, what buyer style prompts triggered visibility, and where AI platforms still lacked understanding about the business.
The biggest shift was moving from static optimization to a continuous feedback loop. Instead of treating SEO as a one time project, we started treating AI visibility as an ongoing process of helping answer engines better understand and confidently recommend a business over time.
A bylined article published on the Connectively Blog exploring why a website-first strategy is no longer sufficient for AI discovery, and what businesses must do to be included in AI-generated answers.
For years, the foundation of online visibility was simple. Build a strong website, optimize it for search engines, and drive traffic through rankings.
That model is breaking.
Today, more buyers are turning to AI systems to ask direct questions like "Who should I hire?" or "What is the best option?" Instead of seeing a list of links, they receive a single, synthesized answer.
That shift changes what visibility actually means.
It is no longer enough to have a well optimized website. Your business has to be understood and trusted across the web in order to be included in those answers.
Most small and mid sized businesses still treat their website as the center of their online presence. They invest in content, optimize pages, and focus on rankings. The issue is that AI systems do not rely on a single source. They evaluate information across multiple platforms to determine what is credible. If your expertise only exists on your own site, it is often not enough. In practice, we see businesses with strong technical search presence struggle to appear in AI generated responses simply because there are not enough external signals reinforcing who they are and what they do.
AI systems are not just indexing pages. They are making decisions about which sources to include. From what we have observed, three factors consistently influence whether a business is recognized and surfaced.
First, clarity. Your business needs to be clearly described in a consistent way. If your messaging changes across platforms, it becomes harder for systems to interpret your role and expertise.
Second, validation. Information that appears in multiple places is more likely to be trusted. When your ideas and insights are distributed beyond your own website, they carry more weight.
Third, perspective. Generic content blends in. Businesses that communicate a clear point of view are far more likely to be surfaced because they add something distinct.
One example that stands out involved a company that had invested heavily in its website. The site was fast, well structured, and filled with content. But when tested across AI tools, the business rarely appeared in recommendations. The issue was not technical. It was distribution and clarity. Once their expertise was expanded into other formats and platforms, and their messaging was aligned, their visibility improved. Not just in search, but in AI generated answers as well.
This does not require abandoning traditional search optimization. It requires building on top of it. Start by making sure your core message is clear and consistent. Your website, profiles, and content should all reinforce the same understanding of who you are and what you offer. Then, extend your presence beyond your site. Share insights in formats and places where your expertise can be recognized, not just published. Finally, focus on saying something meaningful. Content that reflects real experience and perspective is more likely to be picked up than content that repeats what already exists.
Traditional search optimization is still important. It is still the foundation. But it is no longer the full structure. Visibility today is shaped by how well your business is understood across the entire web, not just how well your pages are optimized. The companies that adapt to this shift are not just easier to find. They are the ones that get chosen.
Submitted commentary for an expert roundup on the future of AEO (Answer Engine Optimization) and AI-driven search across Google AI Overviews, ChatGPT, Gemini, and Perplexity.
AI search fundamentally changed digital marketing in 2026, but not in the way most marketers expected. We did not lose SEO. We had to expand it. The shift from keyword rankings to AI generated answers forced businesses to rethink visibility entirely. The question is no longer just, Are we ranking on Google? It is now, Are we visible across Google AI Overviews, ChatGPT, Claude, Gemini, Perplexity, and industry specific AI platforms?
What emerged is what I call the Authority, Discoverability, and Defendability framework, the three signals increasingly shaping whether a business gets surfaced, cited, or recommended inside AI generated answers. Authority is about depth and expertise. AI systems consistently favor businesses that demonstrate nuanced, experience driven knowledge over thin, generic content. Discoverability is about structure and reinforcement. AI systems need clear, connected information across websites, FAQs, educational content, third party mentions, and trusted sources to confidently understand what a business represents. Defendability is about whether AI systems can confidently stand behind the recommendation. Citations, reviews, credentials, external validation, and consistent narratives all help reduce uncertainty and increase recommendation confidence.
The biggest shift is that companies can no longer optimize only for algorithms. They have to build enough clarity, consistency, and credibility for AI systems to confidently retrieve, defend, and recommend them. That is where the competitive advantage lives in 2026.
Featured as an AI marketing expert in the Loudoun Chamber of Commerce blog ahead of the Technology Committee's AI Build Lab.
We'll actually be doing that in the AI Build Lab on April 24th. We'll be using AI to analyze your websites to create a schema for your business and brand. Then that schema can be uploaded into the back-end of your website. And that helps tremendously as a foundation.
As AI shifts how consumers search and discover businesses, many small business owners are asking: How is AI search affecting my business and what should I do about it?
The key is making your business machine-readable to AI systems. Most tools focus on Google rankings. But ChatGPT, Claude, and Perplexity are where your customers search first in 2026. If you don't help AI systems understand what your business does, you're invisible to AI recommendations.
A practical example: using AI to generate schema markup and create FAQ knowledge bases matters because it makes your business machine readable for when someone asks an LLM your exact question. Instead of hoping Google surfaces you, you're showing up in the actual recommendations AI gives. That's a direct path to qualified leads.
The companies winning right now aren't the ones with the best Google rankings. They're the ones LLMs actually recommend.
Submitted to a Bulldog Digital Media feature on AI search traffic and the challenges of measuring AI-driven discovery.
Yes, we actively track AI visibility and AI influenced discovery patterns across ChatGPT, Gemini, Perplexity, Claude, Grok, and Google AI Overviews. One of the biggest challenges is that traditional analytics still struggle to fully capture AI influenced discovery journeys. A user may first encounter a business through an AI generated recommendation, then later return through branded search or direct traffic, masking the original influence source.
Because of that, we measure more than referral traffic alone.
In our current AI visibility tracking framework, we measure:
- 40% Seen by AI — visibility across 30+ tracked prompts, per platform
- 6% recommendation visibility where AI systems actively recommend or cite the business
- 1% "Chosen by AI" visibility where the business becomes the top recommended option
We also use a three tier visibility model:
- Memory visibility: whether AI models already recognize the brand from training data
- Live visibility: whether the brand appears during real time AI assisted searches
- Forensic visibility: whether the brand surfaces during actual browser based buyer journeys
The most important metric is not raw AI referral traffic alone. It is whether AI systems confidently retrieve, recommend, and reinforce the business during meaningful buyer decision moments.
One unexpected challenge we encountered when deploying AI agents into content workflows was that the outputs often became too generic when they were built primarily from traditional marketing materials. The AI could generate content quickly, but the messaging started sounding flattened, repetitive, and disconnected from how founders naturally explained their expertise, customer problems, and decision making process.
That forced us to pivot the strategy. Instead of relying heavily on static website copy, we began using structured founder conversations and interview transcripts as the core source material feeding the AI agents autonomously. The difference was significant. The content became much more conversational, context rich, and aligned across platforms because the AI was learning from natural explanations rather than isolated marketing language.
The biggest lesson was that AI agents work far better when they amplify authentic expertise instead of trying to manufacture authority from generic source material.
One innovative approach we've used with AI is building marketing content from structured founder conversations rather than starting with generic campaign copy. Instead of asking AI to simply generate personalized messaging, we use interview driven conversations to capture how founders naturally explain their expertise, customer problems, decision making process, and industry perspective. AI then helps transform those conversations into educational content, FAQs, social content, and buyer focused messaging across channels.
What surprised us most was that the content consistently performed better when it preserved the natural language and contextual depth of the original conversations instead of sounding overly optimized or polished. The impact was not just stronger engagement. We also saw clearer positioning, longer time spent engaging with content, and improved visibility inside AI generated answers because the messaging reflected how real people naturally ask and answer questions.
One unconventional approach we started using was testing how AI systems actually describe and recommend a business across different conversational prompts before creating the content strategy itself. Instead of beginning with keyword targets, we would ask platforms like ChatGPT, Claude, Gemini, and Perplexity the kinds of questions real buyers ask when comparing vendors, evaluating trust, or narrowing down decisions.
What stood out was that AI systems consistently favored businesses with clearer narratives, stronger contextual explanations, and more reinforcing information across multiple sources, not the businesses with the most aggressively optimized pages.
That changed our approach completely. Rather than creating isolated SEO content designed primarily for rankings, we started building conversational content ecosystems around real buyer questions, founder expertise, FAQs, comparisons, educational content, and consistent business descriptions across platforms.
The goal shifted from "ranking pages" to helping AI systems confidently understand, explain, and recommend the business.
One limitation I've consistently seen with AI generated PR outreach is that AI can produce content very quickly, but it often lacks the deeper contextual understanding needed to build genuine authority and trust over time. Many AI generated pitches sound technically correct, but they flatten expertise into generic summaries that feel disconnected from the real perspective, experience, or patterns a person has actually observed.
What I learned is that the strongest AI assisted PR workflows still need authentic conversational input at the center. Instead of relying entirely on AI generated positioning, we started using interview driven content and real founder conversations as the source material. That gave the AI much richer language, stronger narrative consistency, and more differentiated insights to work from.
The outreach became more specific, more human, and more aligned with how experts naturally explain complex ideas. AI works best in PR when it amplifies genuine expertise rather than trying to manufacture it from scratch.
One of the biggest mindset shifts came from studying not just competitor websites, but how AI systems were actually describing and recommending businesses in real buyer style searches.
In several audits, we found competitors appearing in AI generated answers even when their websites were not particularly strong. What stood out was that they had clearer descriptions across third party platforms, stronger supporting content, and more consistency in how the business was represented online.
That completely changed how we approached visibility. Instead of focusing only on rankings or traffic, we started focusing on helping AI systems understand a business more clearly over time. That meant creating content around real buyer questions, strengthening business descriptions across platforms, and making sure key ideas appeared consistently beyond the company website.
One of the most interesting results was that some businesses began appearing in AI generated recommendations within weeks, not because their rankings dramatically changed, but because the overall picture AI saw became clearer and more trustworthy.
My experience with AI became very personal before I ever started working in it. About six months before my role was eliminated, our CTO pulled the entire team onto a Zoom call and told us, very directly, to start learning AI. It wasn't framed as optional. It felt more like a signal that something was changing fast.
After that, I had a decision to make. Go look for another role and stay on the same path, or take that moment seriously and lean into what was clearly coming.
Instead of job searching, I started learning, testing, and building with AI. That eventually became Monic AI Systems, where I now help businesses understand how they show up inside AI-generated answers.
What stuck with me is how quickly something abstract turned into something personal. It changed how I think about work, value, and how fast you have to adapt to stay relevant. I also see now that a lot of people are quietly going through that same shift. It doesn't always show up in headlines, but it shows up in how people think about their future.
I adapted our content strategy by making one clear change: standardizing the brand's description and key facts across the website, third-party profiles, and partner listings. During our initial 30-day audit we tightened inconsistent language so AI systems receive a single, consistent signal about what the business does.
This simple editorial discipline makes it much easier for models like ChatGPT and Perplexity to recognize and name the brand. As a result, the brand began to appear more often in AI-generated recommendations rather than being described in varied or missing terms.
The most important shift is moving from optimizing for visibility to optimizing for selection.
For years, digital marketing has focused on getting seen. Ranking higher, driving traffic, increasing impressions. But with AI-driven search, that model is changing quickly. People are no longer scrolling through lists of links. They are asking direct questions and receiving a small number of synthesized answers. In many cases, those answers include only a few options, sometimes just one.
That means the goal is no longer to be one of many results. It is to be one of the few options that gets included and trusted.
To do that, businesses need to focus on how clearly they are understood across the web. Not just on their own site, but in how they are described, referenced, and reinforced through third-party sources. AI systems are looking for consistent signals of expertise, not isolated pieces of content.
Over the next few years, the advantage will not come from producing more content. It will come from being the business that AI systems confidently select when someone asks who to choose.
Question directly used the term SEO — answer mirrors the interviewer's language to combat the framing.
No, I don't think SEO is becoming obsolete, however, organic traffic is shifting to AI.
For a long time, SEO meant getting into a list of links. Now people are just asking AI tools for an answer, and they get the answer of the brand that has made themself recognizable inside AI answers. That's a big shift; you can be ranking really well and still not show up at all if your business isn't included in that answer.
What I'm seeing with clients is that SEO still matters, but it's not the whole picture anymore. You can have a well optimized site and still be invisible in the places where people are actually making decisions. It's less about optimizing pages, and more about making sure your business is clearly understood across the web so AI systems can recognize and choose you.
I'm Monica Tomasso, founder of Monic AI Systems and an AI visibility expert. I have over 20 years of experience at companies including BP, ExxonMobil, and NTT Data, and I led digital transformation and data initiatives that delivered more than $300M in measurable business impact.
What I'm seeing right now is that the real innovation in AI isn't happening in the models themselves for most businesses. It's happening in how companies actually use them. A lot of teams are experimenting with AI, but very few are treating it as something that changes how they get discovered, evaluated, and chosen.
That's where I spend most of my time. We're helping companies move from using AI as a tool to using it as a channel. Instead of just generating content or automating workflows, they're thinking about how to show up inside AI generated answers when someone asks who to hire or what to choose.
That shift sounds simple, but it changes everything. It forces teams to align their data, content, and positioning so systems like ChatGPT or Perplexity can actually recognize and recommend them.
One example is a company that had strong traffic and a solid digital presence, but almost no visibility in AI tools. The issue wasn't technical. It was that their expertise wasn't clearly represented across the web in a way AI could interpret. Once that was fixed, their visibility started to change quickly, not just in search, but in how they were surfaced in AI driven conversations.
The companies pushing boundaries right now are not just building with AI. They're building for how AI makes decisions.
What we are seeing right now is that the most valuable data is no longer just behavioral or transactional. It is conversational.
At Monic AI Systems, we track the actual questions buyers are asking across platforms like ChatGPT, Claude, Perplexity, and Google AI, and how businesses show up inside those answers. Underneath that, we track patterns across prompts, mentions, and sources to understand what is actually driving inclusion.
That gives us a very different type of signal. Not just what people click, but what they ask, what they are comparing, and ultimately what gets selected. For companies thinking about data strategy, this is an important shift. Traditional analytics tell you what happened after someone visited your site. But AI driven discovery is happening before that, inside the answer layer.
The opportunity is to understand:
- what questions are being asked
- how your brand is being positioned
- where you are being excluded entirely
We have seen that even small changes in how a business is described and where those signals exist across the web can significantly impact whether it is included in AI generated recommendations.
From a data perspective, this creates a new category of insight. One that connects directly to visibility, trust, and selection. For organizations participating in conversations like this, the value is not just exposure. It is becoming part of the data ecosystem that AI systems use to form answers. This is where long term visibility is now being shaped.
One factor small businesses should consider: does this tool make you machine readable to AI?
Most tools focus on Google rankings. But ChatGPT, Claude, and Perplexity are where your customers search first in 2026. If a tool doesn't help you build structured data and machine readable content, you're invisible to AI recommendations. Being recommended by an LLM drives inbound leads directly to your door.
A practical example: a tool that automates schema markup and creates FAQ knowledge bases matters because it makes your business machine readable for when someone asks an LLM (ChatGPT, Claude, Gemini, etc.) your exact question. Instead of hoping Google surfaces you, you're showing up in the actual recommendations ChatGPT gives. That's a direct path to qualified leads.
The companies winning right now aren't the ones with the best Google rankings. They're the ones LLMs actually recommend.
Yes. The biggest change is that Google Analytics is starting to validate AI influenced discovery as a measurable part of the customer journey instead of letting it disappear into direct or unattributed traffic.
That changes the workflow because we can no longer evaluate campaigns based only on last click attribution or traditional referral paths. We are now looking much more closely at patterns like branded search lift, engagement quality, assisted conversions, and higher intent behavior coming from visitors influenced by AI systems.
One of the most interesting shifts is that AI driven visitors often behave differently from traditional paid or social traffic. They tend to arrive with more context, spend more time engaging, and convert faster because part of the research and trust building already happened inside the AI experience itself.
The addition of AI traffic visibility in Google Analytics helps connect those dots more clearly. It is pushing analytics workflows beyond simple traffic reporting and toward understanding how AI assisted discovery influences the full buyer journey.
At a practical level, we operationalize this through a coordinated set of AI agents rather than a manual content process.
Each one handles a specific part of the workflow. One focuses on breaking down a conversation into the real questions a buyer would ask. Another turns those into clear, structured answers. Others handle how those pieces are connected and reinforced so they do not sit in isolation.
One interview can become buyer focused FAQs, comparison pages, educational articles, social clips, and structured business descriptions. The output is not a single article. It is a connected set of content written in a way that is easy for both people and AI systems to understand.
We then make sure those ideas appear consistently across more than one platform. When businesses are described clearly and consistently across a small number of trusted sources, it becomes much easier for AI systems to recognize and trust what the business represents.
The important part is that this is not a one time process. The system tracks whether the business is actually appearing in AI generated answers and adjusts the language and structure over time.
The biggest shift in the playbook is that ranking is no longer the end goal. For years, traditional search was about getting onto a results page and letting the user decide. Now, more of that decision is being made before a click ever happens.
With AI-driven search, people are asking direct questions and receiving a small number of synthesized answers. In many cases, those answers include only a few options, sometimes just one. That means strong rankings do not guarantee visibility in the moments that matter most.
The playbook is shifting from optimizing pages to making sure your business is clearly understood and consistently represented across the web so it can be included in those answers.
Traditional search is still foundational, but it is no longer where the decision happens.
What this signals is less about BuzzFeed itself and more about the underlying model it was built on.
BuzzFeed was designed for a world where distribution came from search and social, and where traffic translated directly into advertising revenue. That model has been under pressure for years, but AI is accelerating that shift.
More and more discovery is happening without a click. People are getting answers directly from AI systems instead of visiting multiple sites. That reduces the value of scale-driven content models that rely on volume and ad impressions.
So the question is not just what happens to BuzzFeed under new ownership. It is whether that model can be rebuilt for a world where being visited matters less than being referenced or included in an answer.
For media and advertising more broadly, this is a signal that visibility is moving upstream. The value is shifting from traffic to influence inside the systems that shape decisions. The companies that adapt will not just focus on producing content. They will focus on being included, cited, and trusted in the places where answers are generated.
One of the biggest challenges we've been working through is closing the gap between measuring AI visibility and actually improving it.
It is relatively easy to see where a brand does or does not show up across tools like ChatGPT or Perplexity. What is much harder is turning that insight into consistent action. For us, that led to building a more agent driven approach. Instead of treating content, distribution, and tracking as separate efforts, we needed a system where those pieces work together — capturing real expertise, turning it into structured answers, placing those signals across the web, and then continuously adjusting based on what shows up in AI generated responses.
The challenge was not just technical. It was making sure the system stayed grounded in real expertise and did not drift into generic or low quality output, especially as it scaled.
What we are seeing now is that visibility improves when those loops are connected. Measurement alone does not change outcomes. Action tied to what is being measured does.
I align story angles with search intent by shaping newsroom copy to directly answer real buyer questions and by using the same clear, consistent descriptions across the website and external profiles. I prioritize angles that provide a plain first-paragraph answer rather than promotional framing so both readers and search systems immediately understand the point.
One simple editorial tweak that raised findability was adding a brief, neutral FAQ or "what this is" paragraph to each newsroom story that states who the product or service serves and what it does in plain language. That structural change preserves journalistic tone while giving search systems and readers a clear answer to surface, helping improve organic discovery without sounding like marketing copy.
One foundational local visibility mistake I still see is businesses relying almost entirely on their website and not fully building out their Google Business Profile.
In 2026, that profile is often the first thing both search engines and AI systems use to understand a business. I've worked with companies that had strong sites but incomplete or inconsistent profiles, and they struggled to show up locally.
The simplest fix is to fully complete and regularly update their Google Business Profile, including services, categories, photos, and clear descriptions of what you do. It's one of the fastest ways to improve both visibility and trust.
Short answer: not fully, and not without a human pass.
We actively use AI powered audit tools, including Otto within Search Atlas, and they are incredibly effective at scanning sites quickly and surfacing technical issues at scale. They catch things like internal linking gaps, crawl inconsistencies, and metadata issues far faster than a manual review ever could.
But I would not send an AI audit directly to a client without reviewing it first. What we see in practice is that AI is very good at identifying issues, but not always at identifying what actually matters.
For example, we ran an audit where the AI flagged dozens of technical improvements across a site. All valid. But the bigger issue was not technical at all. The business was not showing up in AI generated answers. There was no clear positioning, no consistent presence across the web, and no signals that would allow AI systems to recognize and recommend them. That is something a human sees immediately.
On the flip side, we have absolutely seen AI catch structural issues across large content sets that would have taken hours to find manually.
It is not about replacing the human review. It is about changing the role. AI is becoming the fastest way to surface issues. Humans are still needed to interpret what matters and connect it to visibility and outcomes.
The bigger shift is that a clean technical audit no longer guarantees visibility. You can fix everything on the site and still not be chosen when someone asks an AI system who to hire. So the real question is not whether AI can replace audits. It is whether the audit is measuring the right things.
One foundational essential that is still overlooked is whether your business is actually understood, not just optimized.
Most small and mid sized businesses focus on the basics — pages, keywords, and technical setup. That is the foundation. But they stop there. What has changed is that the foundation alone is no longer enough to drive visibility.
You can have a well structured site and still not show up when someone asks an AI tool who to hire or what to choose. That is where most businesses are falling short.
I think of it like a house. Traditional search is still the foundation, but AI visibility is now the rest of the structure. If you never build beyond the foundation, no one sees the house.
The simplest fix is to move beyond your website and make sure your business is clearly represented across the web. Consistent messaging, real expertise, and presence in multiple places all help systems recognize and trust you.
Because today, it is not just about being indexed. It is about being included in the answer.
We are using AI extensively, but not in the way most teams think about it.
Many organizations focus on automating content production — writing blogs faster, generating social posts, and scaling output. That is useful, but it is not where the real shift is happening. We use AI to capture expertise and distribute it in a way that AI systems can recognize and trust.
At Monic AI Systems, we use an agentic content model. It starts with structured conversations through an AI podcast, and then uses coordinated AI agents to expand that into a network of assets across the web — articles, FAQs, comparisons, and authority signals. The goal is not just efficiency. It is visibility inside AI generated answers.
What is working is scale. AI makes it possible to capture and distribute ideas far more efficiently than traditional teams. What is not working is that most of that content is not differentiated. AI did not create a content advantage. It eliminated it.
If everyone uses the same tools to produce the same types of content, you get a massive volume of material that adds no new perspective. That is exactly what AI systems ignore. The bar has shifted from "can you produce content?" to "are you worth including in the answer?"
We are increasing our use of AI to strengthen how businesses show up in AI generated recommendations. The future of content is not about publishing more. It is about being present when decisions are made.
Most companies are approaching voice search as a formatting problem — optimizing for conversational queries, FAQs, and long-tail keywords. That's already outdated.
Voice is just the interface. The real shift is that AI systems are now making the decision about which businesses to recommend. Whether someone types, speaks, or taps, they're increasingly getting a single synthesized answer.
That means the goal is no longer to "rank" for voice queries — it's to be included in the answer itself.
This is where our strategy has shifted. We use what I call an agentic content system, anchored by an AI-powered podcast. Instead of creating isolated content pieces, we capture real expertise through structured interviews, then use AI agents to expand that into a distributed network of assets — articles, FAQs, comparisons, and authority signals across multiple platforms that LLMs pull information from.
This matters because AI systems don't just crawl your website. They evaluate your presence across the web. So rather than optimizing for voice search directly, we're building a footprint that AI systems can consistently recognize, trust, and recommend.
One tip: stop optimizing for how people ask questions, and start optimizing for how AI systems choose answers. If your expertise only lives on your website, you're invisible. AI visibility comes from being distributed, validated, and cited across the broader web.
One of the biggest shifts happening right now is that content can no longer be created only for rankings. It also needs to help AI systems confidently understand, connect, and explain what a business actually does. The brands performing best across both Google and large language models tend to create content that is conversational, context rich, and continuously reinforced over time.
One strategy I've found especially effective is using interview driven content systems rather than isolated blog creation. Conversational interviews naturally produce the types of language patterns, buyer questions, explanations, and contextual depth that both search engines and AI systems interpret well.
From a single conversation, businesses can create interconnected FAQs, educational articles, comparison content, founder insights, social clips, and structured business descriptions that reinforce the same core themes across multiple formats and platforms. This matters because AI systems increasingly synthesize understanding from multiple reinforcing signals, not just individual pages.
The companies winning visibility are not necessarily producing the most content. They are creating the clearest and most consistent understanding of who they are, what they do, and why they are credible.