If I Started SEO in 2026, I'd Do This

If you can get these three things right in 2026, SEO will become your best performing channel to get high-v value customers by far. Look, the SEO landscape has fundamentally shifted. And the strategies that worked even 2 years ago, just focusing on Google and building a bunch of backlinks are simply not going to be enough anymore. And I know this because I've completely changed the approach I'm using for the 10 plus B2B companies I actively manage SEO for. Like one B2B SAS company who adds over $30,000 in new MR every month from organic search alone. Or a different B2B SAS company who added five paying customers in month one of adopting these strategies. What we're seeing is that the winning companies in 2026 are doing things a little different than what we've been used to. So, in this video, I'm going to walk you through the three-part framework I'd use to build an organic customer acquisition engine from scratch in 2026, and we're going to look at both the traditional SEO and the AI search of things without it taking you 6 months to see results. So, let's dive in. All right, so level one is all about understanding the new reality of search and getting those foundations right. So, here's what's actually happening. The way that B2B buyers discover and evaluate vendors has fundamentally changed. Nearly half of B2 buyers are now using AI assistants like Chat GBT, Claude or Perplexity as part of their research process. And this number is growing really fast. So this means that you now have two search problems to solve, not one. You've got traditional Google SEO, which still matters. And now you've got AI search visibility, which is a completely different challenge. A lot of companies mistakenly assume that if they're ranking well on Google, then they're just automatically showing up in AI answers. And this is just not how it works. The data shows that only about half of the sources cited in AI answers actually rank in Google's top 10 results. And so you could be absolutely crushing it on Google and still be completely invisible when your prospects ask Chat GBT for vendor recommendations.

So let's talk about the three things you should nail at this foundational level. Before you do anything else, you need to understand where you actually stand today. Most companies have no idea how they're showing up or not showing up in AI answers. The way to do this is simple. Take your most important buyer intent queries. So the questions your ideal customers are asking when they're actively evaluating solutions and then just run those through chat GBT, Plexity, Claude, and Google's AI overviews. What you're looking for is are you being mentioned? Are you being recommended? What sources are being cited? And critically, who are your competitors that are showing up in these answers? This gives you a baseline. And then from there, you want to start tracking what we call your mention rate, your citation rate, and your share of voice. These are the metrics that actually matter in AI search. Mention rate is how often your brand comes up in relevant answers. Citation rate is how often your content is being used as a source to influence those answers. And then share a voice is your overall presence compared to competitors. If you're not tracking these metrics, then you're flying blind and you definitely don't have an AI search strategy. The second thing you need to understand is how these AI models decide what to site and recommend. And this is where things get really interesting. In traditional SEO, domain authority was a big deal.

The idea was the more backlinks you get from reputable sites, the more Google trusts you and then the higher your content is going to rank. Now, this signal still matters for Google and SEO. But AI models are looking at something different. They're looking at cross-source cooperation. So, what does this mean? Well, AI models are trying to give accurate, trustworthy answers. And to do that, they verify information by checking if the same facts appear consistently across multiple independent sources. So if your website says one thing, but G2, Reddit reviews, and industry publications say something different, then the AI is going to trust the consensus. It's not going to trust you. Just think about it from the AI's perspective. It's essentially doing what any diligent researcher would do. It's checking multiple sources to make sure the information is accurate before presenting it back to the end user. And so within this process, your website is just one input. What third parties are saying about you across the internet carries significant weight. So the action here is to create what we call a set of facts document. This is your single source of truth. It includes your pricing, your key features, your differentiators, and your company positioning. And then what you want to do is you want to order every third-party platform where your company appears and make sure that information is consistent everywhere. So you want to look at places like G2, Capterara, Trust Pilot, LinkedIn, industry directories, YouTube, old press mentions, all of it.

Now the third foundational piece is understanding how to think about content for AI search. And if I'm being honest, this requires a significant mindset shift. In traditional SEO, you'd find a keyword with good volume, create a page optimized for that keyword, and try to rank for it on Google. Whereas in AI search, you need to think bigger. You need to think about what we call your prompt universe. This is the entire range of questions, scenarios, and context in which your ideal customer might ask these AI assistants for help. Here's why this matters. When someone prompts an AI assistant, the model doesn't just answer that one question in isolation. It performs what's called the query fan out. So, it breaks the initial question into semantically varied subqueries to gather comprehensive information. It might look for things like pricing information, alternative solutions, integration details, use cases, limitations, all from that single user prompt. And so if your content only answers that narrow primary question, well then you're just missing most of the opportunity. And your competitors who are covering all those adjacent topics are the ones that are going to be cited in the answers. So the work here is to map out your entire prompt universe. Go beyond keywords, think in full contextual questions like what is the best project management tool for a remote marketing team of 20 people with a limited budget and then structure your content to answer that primary question but ensure you also cover as many related secondary questions as possible.

Okay, so level one was about understanding the landscape and getting those foundations right. Level two is about building a repeatable system that consistently earns you citation. This is where you move from random acts of content to a machine that generates predictable results for you. The first piece of this system is your content approach. And here's the thing. Traditional blog content, the kind that most companies are still producing, is not optimized for AI citation. It's optimized for Google rankings and human readers, which is fine, but it's just not going to be enough anymore. To consistently get cited by AI, your content needs to be structured in a very specific way. And that's why we developed the citable framework. It's a seven-part methodology that ensures every piece of content you create is optimized for retrieval and citation without hurting your Google rankings. Now, without going too deep into each letter, the key principles are lead with a clear direct answer that AI can easily extract. Ground every claim you make with verifiable sources. Structure your content in self-contained blocks that can be pulled independently by these LLMs. Keep information current and consistent. and make sure your entity relationships are explicit. So, who you are, what you do, who you integrate with, and who you compete with. The goal here is to create content that's so well structured and so authoritative that AI models can confidently site it. You're essentially making the AI's job easy.

The second piece of the system is content volume. And I know this might be controversial, but just hear me out for a second. In AI search, each piece of content is not competing for a single position like it does with Google. These AI models use passage level extraction. So they're pulling specific blocks of text from your content to reconstruct answers back to the user. This means every article you publish has multiple opportunities to be cited. In reality, each piece of content could have 5, 10, or more what we call passage candidates. And because these AI models are constantly looking for fresh, relevant information, there's a real advantage to having more surface area that they can pull from. Now, I am not saying quantity over quality. Every piece should still follow the citable framework. But the companies winning in AI search right now are publishing at a pace that most traditional SEO teams would consider aggressive. We're talking about daily publishing, not weekly, certainly not monthly. And the way they do this without burning their team is to build AI assisted human in the loop workflows. So we use AI to help with research, drafting, and efficiency. But we have subject matter experts overseeing the entire process. everything from accuracy, depth, and brand voice because we don't want to create generic AI slop content that will only hurt you in the long run. Now, the third piece of the system is recognizing that your website is only part of the equation. In fact, I'd argue that your website represents maybe 20% of your AI search opportunity.

The other 80% is what's happening off your site. These AI models crawl the entire web. And because of this cross-source cooperation, these third party mentions carry significant weight about how you're being represented in these answers. And so this means you need a deliberate off-page strategy, not just for backlinks, but for AI visibility. So start by looking at what sources are actually being cited in AI answers for your category. Run those visibility audits we talked about in level one and pay attention to where those citations are coming from. You'll likely see patterns, certain publications, certain platforms that show up repeatedly. And so then we want to build a presence on those platforms. If Reddit keeps getting cited, then you need a Reddit strategy. If G2 reviews are influencing the AI answers, then you need to be actively managing your G2 presence. And by the way, Reddit's influence is much bigger than most people realize. We recently analyzed over 140,000 AI citations. And what we found is that Chat GT allocates 27% of its web searches to Reddit, but it only shows a small percentage of those as visible citations in the web interface for the user. Now, if you want to see which Reddit threads are actually being cited for your category, we've got a free tool you can use, which I'll link in the description. You just enter your company's domain. We'll create a set of prompts that we test across AI systems like Chat GPT. And then we'll show you the actual Reddit threads and comments that are influencing AI answers being shown to your ideal customers. Okay. So, the key thing to take away here is that your off- page strategy should be informed by AI answers and not just traditional SEO metrics. If a platform is getting cited by AI, that's where you need to be visible if you want to succeed. Okay, so moving on. Level three is for companies that don't just want to show up, but they want to own the conversation in their category. This is about building a defensible advantage that is going to compound over time. AI models are swimming in generic AI slop.

There's an ocean of blog posts, articles, and guides that all roughly say the same thing. And so, the content that stands out, the content that these AI models actually have to site is content that contains unique and original information. This is where your first party data becomes valuable. your customer insights, your proprietary research, your internal benchmarks, your product usage data. This is information that only you have and your competitors can't copy. So when you publish this original data, you become the primary source. AI models looking for authoritative information on that topic have to reference you because you're the origin of that data. Now, this is how smaller companies can compete with larger companies who traditionally dominated SEO. It's not about who has the biggest content budget. It's about who has unique insights to share. And so your first party data is an asset that you're probably underleveraging today. The second piece of category ownership is getting smarter over time. So this means building systems to track what's actually working and what isn't and then using that data to continuously improve. Internally, we call this building your knowledge graph. So you're tracking which clusters, topics, and formats are performing best in AI citations as well as the traditional SER. And so we're asking questions like which topics are you winning on? which are you losing and what's the actual structure and depth of content that consistently gets cited over time. This gives you predictive power. You can look at the content opportunity and have a real strong sense of whether these topics or these content formats are likely to earn you citations based on that historical data. And so you're not guessing anymore. You're actually making informed decisions. And so this kind of systematic learning creates a compounding advantage. every month, every piece of content that you ship, you get a little smarter about what works, while your competitors are still operating on intuition, gut feel, and they're just going to fall behind.

Now, the final piece is moving from answering just questions to shaping the entire conversation itself. When you've built a strong AI presence, so you're getting cited consistently, you have unique data and insights, and you understand what works because you're getting smarter over time, you're now in a position to define how your category is talked about within AI assistance. You can introduce frameworks and terminology that become the standard, and you can set the criteria by which solutions in your category are evaluated. This is the difference between being a participant in your market and being the leader. When these AI models answer questions about your category, they're drawing on the narrative that exists across the web. And if you've done your work to establish your perspective, your frameworks, your point of views, your differentiators, that's what's going to be reflected in the answers. Now, this doesn't happen overnight. It is the result of consistent execution across all three levels. Getting visible, getting cited, and getting smart. But when you do get there, you've built something that is very difficult for competitors to displace. Now, in this video, we looked at the three-step framework I would use to start and scale SEO in 2026. But if you want to go deeper and learn how to actually implement this yourself, then click here and learn how you can realistically dominate AI search results this year as a B2B SAS company. I'll see you over there.