The Rise of Dark AI and the Cost of Getting It Wrong

Transcript lightly edited for spelling and punctuation. The conversation is unabridged.

Mark Evans: Hi. It's Mark Evans, and you're listening to Marketing Spark. Today's conversation sits at the intersection of something every SaaS company is trying to figure out: how AI is changing the way that buyers discover, evaluate, and choose solutions. And there's a growing focus on AEO, as evidenced by AI-generated answers, citations, and visibility in tools like ChatGPT, Gemini, and Claude. That's where most of the attention is going. But what if that's only a small part of what's actually happening? My guest today, Tom Rudnai, has been looking at what he calls Dark AI, the idea that a large part of influence is happening before brands are ever cited in the early conversations where buyers are framing the problem and narrowing their options. His team's research suggests that most interactions don't include citations at all, which raises a different question: If buyers are making decisions before you're ever mentioned, where should you be focusing? Tom is the CEO of Demand Genius, and today, we're gonna unpack what's behind this idea, what the data says, and what it means for how SaaS companies think about positioning, content, and growth. Welcome to Marketing Spark.

Tom Rudnai: Thank you for having me. Looking forward to the conversation.

Mark Evans: Why don't we start off with a softball question? When you talk about Dark AI, what's the simplest way to understand what's happening beneath the surface?

Tom Rudnai: We like to think of AI as a new form of search or an extension of just Search 2.0. When you actually look under the hood at how these models behave and what they do, it's not at all. They actually don't simply search for an answer. They compile answers. They converge on answers. And when you understand that, it really changes how you look at the channel. Because if an AI isn't going out searching for answers consistently, then the way that you influence it gets very different. It also means that it's not able to cite it. We tend to focus on citations, share of voice, things like that. But the AI doesn't want to mention sources. Search relied on the sources that it's citing, and then it corrected. It needed those links. AI doesn't cite, and only does so when it is really necessary for the user, i.e., when it was the user specifically asking to be directed to someone. Now when you think about a complex buying journey, very few of those interactions do you want to be directed to a vendor. It's simply speaking, you want to get a question answered. You want to help frame your problem, define your requirements, all of the time. We know in B2B, that's the majority of the channel. This is where that's taking place. So that's what we mean by Dark AI. It's all of those little interactions from day one of being problem aware through to actually looking for solutions where AI isn't searching for answers. It's not mentioning you. AI is influencing the buyer's requirements, is influencing the way that the problem is framed, and we know that all of that adds up ultimately to the decision much more than just who's visible at the point of conversion.

Mark Evans: I think what you've highlighted: the fact that we're barely scratching the surface when it comes to AI and the appreciation of how it works and how we should use it. On a related note, when you think about how many companies, whether they're B2B or SaaS, are approaching AI and the level of sophistication they have when it comes to understanding how the models work and how their marketing and sales can leverage the power of AI and these models, where do you think they're at? If you were a CEO and you saw this AI tsunami in front of you, would you be confident right now that your team knows what they need to do, or do you think there's a giant learning curve that a lot of CEOs need to put their companies on?

Tom Rudnai: Definitely that. Just talking to marketers, I'm always struck by how they really feel like they're in a vice, and I'm getting away from AEO and more just AI in general. AI is a very interesting trend because it's defined very quickly by marketing dollars and not more than actual case studies, use cases, and validated examples that are working really well in practice. It's also, I think, very difficult for a marketer because the earliest adopters and power users initially of AI are investors and CEOs and founders and all of the people who impose expectations on you. So you're caught between platforms that overpromise, underdeliver, overpromise to your boss, and then you're left holding a can or holding the bag when it underdelivers. You don't have any real resources, but the demands are no less of you. If anything, they're higher in terms of quality and quantity. Right. But these tools often don't quite deliver. You're the one who, with ever-decreasing resources, because content is now a commodity, code is a commodity, has to deliver on those things. So it's a difficult time.

Mark Evans: What does that conversation look like between the CEO and the head of marketing and the head of sales? The CEO obviously has these high expectations. They recognize that AI could be a game changer for their business. Marketers are mandated to embrace AI in some way, shape, or form because it's the way that they can become more efficient and maybe in the process reduce headcount or at least hold headcount. So what does that conversation look like? What should the CEO say to the marketer? What should the marketer say back to the CEO?

Tom Rudnai: It's different in every organization. It depends a lot on the size of the organization and on the CEO and the marketer. What a lot of marketers feel, it's manifesting at the moment as a crunch on headcount. It's do more with less or really, it's being used as an efficiency tool to try and cut out humans, which I think we will continue to go on that flow for a little bit until eventually people realize that actually the best results from AI come when you mix human insight in really intelligently and you use it as a tool not to automate what humans could already do, but to actually unlock complete new possibilities. I'll give you an example. We're doing our content that we do. For example, the Dark AI report that we're talking today, that's something that a startup of our size could not have attempted five years ago. We used AI to capture an immense amount of data that was qualitative in nature. Right? So that's what AI does really well. Does qualitative analysis, subjective analysis, makes judgment calls at scale. So you simply couldn't have conducted that analysis five years ago. And that's what led us to do it. We were like, actually, how can we use AI to do something on a scale that we couldn't have done before, really big, rather than just to churn out lots of low-quality pieces of content. And that's, I think, the journey that everyone is then gonna go on is once we compress headcount and we realize that we did get lower results as we drop those things, results did fall off together. We then start building headcount back up, but we build it up in a much smarter way and much more innovative to realize the benefits.

Mark Evans: Yeah. It's fascinating. I think we're in a period of learning. We're in a period of transition. I think a lot of people are just trying to figure out how they should move forward, and that is an immense challenge right now. Thank you for referencing the AI report that your company published earlier this year. In the report on Dark AI and AEO influence, what specifically did you see in the data that convinced you that influence is already set before citations appear rather than citations being one of the several factors shaping the final decisions? I guess, to load up the question even further, what were your surprising findings from the report?

Tom Rudnai: Everything surprised us because we honestly didn't go into it with too much of a kind of set belief. I had an inkling of a hypothesis that all of the advice out there that AEO is just SEO. I'm like, well, that seems like they're very different pieces of technology. It's odd to give us the exact same best practices for how to optimize for them both. But I had a little hunch, so something seemed a little bit off there. But then when we got into it, I think I was very surprised at how stark the trend was in terms of citations. So what we saw, and for context for anyone listening, what we did, we ran an immense number of prompts, and we wanted to look specifically at how AI responses vary across complex B2B buyer journeys. So we picked 14 complex categories, different levels of complexity, all B2B, and we ran prompts very deliberately across awareness, consideration, and conversion phases. And we analyzed the response through our technology and our AI to understand how they varied. And what we saw was you look at citation. Or, actually, let's start back with mention. You get mentioned at every stage of the funnel, but inevitably, the more bottom of funnel you get, the more you get through to your conversion rate increases pretty dramatically. You look at how often retrieval is invoked. This is what really surprised me. And when we talk about it not really being a search channel at all. In awareness, 0% of the time retrieval was invoked. In consideration, 0% of the time retrieval was invoked. In conversion, then it was 48%. For the vast majority, we know that the bulk sits in awareness and consideration, and it doesn't go looking for answers. So your content, it's not useless because over time, you can influence training data, but it's a pretty long horizon. It's not a very well understood thing. Accordingly with that, citation rate was very simple: 0%, 0%, 48%. So all of our measurement, all of our strategy at the moment is about the idea that I produce a piece of content, and tomorrow it shows up in AI directly cited, simply not how these models work. Only for half of those bottom-of-funnel queries is that a trend that is plausible, and there's a whole load of stuff we can go into for a while. It still isn't quite that direct.

Mark Evans: If a marketer came to you or a CEO came to you and said, Tom, what are the three takeaways from your report? What should I do? How should I act upon what you've discovered from this in-depth piece of research? What would you tell them?

Tom Rudnai: So I'm gonna break it into kind of measurement and then strategy. So I think for measurement, we have to develop new metrics that are fit for the new problem rather than trying to apply old ones, like, SEO-style metrics. The best one that we've been able to come up with, and this is new, right? So I don't have all of the answers. I'm always very keen to say that. It is looking at fit? So there's an immense different number of ways that I can ask a single prompt to AI. Right? So if you just try one of those and measure the response and say, did I get cited with so many different variations? What is very predictive of how you are represented across all of those different interactions, every stage of the journey, it's just actually what does AI think of you. So how does it view your strengths, your weaknesses, your fit for different buying criteria, different use cases? You can map that to different personas that you want to influence and get a really good sense of, okay, not for this query that cites us, but for a CFO at a big company who we know wants X, Y, and Z, does it think we're good or not? And if the answer is not, what does it say we're bad at, and how can over time we correct that? And that produces, like, if you can make sure that whenever the CFO of your big deal that you're trying to close goes on and off, are you good value for money? You just want to make sure it says yes, and that's more valuable than any amount of visibility at the top of the funnel. So that's it for a measurement point of view. Focus on fit more than visibility. And are you demonstrating the right use cases?

Mark Evans: And how do you define fit? What's the layman's version of fit?

Tom Rudnai: So you can quantify that. We run sentiment analysis on how your brand is represented for different criteria. So let's take, if I'm buying, we'll go with a commonplace example. I'm buying shoes, which is a relatively high consideration purchase. There's different buying criteria that people might apply. They want comfort. They want springiness. They want them to be environmentally friendly. They want them to have a really cool switch at the side. Whatever the criteria people might use to buy, we can ask the AI how good do you think Nike, Adidas, all of these are for that. Run sentiment analysis on that. Then that gives you your really clear breakdown of what it thinks you're good at and bad at. And then you go through a process of mapping that to what your different segments or your different stakeholders might want. I won't get into the math to analyze it. That is something that you can quantify, right? And track as a percentage and a KPI.

Mark Evans: So that's number one. Number two piece of advice would be what? From the report?

Tom Rudnai: Stop viewing content as a one-to-one route into a particular query. Take a step back. Search at its very most basic level. The job that you do with search: you find a really high-intent query or a very common or high-intent query, high volume or high intent. You write a piece of content for it that was summarizing knowledge against that query and is the best summary one. That's search in a very simple nutshell. AI doesn't work like that. You would need to find, there's, like, an endless number of those queries. What it looks at more is the entire corpus of your content. Because it doesn't, when I put in "what CRM should I buy," it doesn't go searching for an answer to that. That one piece of content, which you keyword stuff with "what CRM should people buy," isn't doing you any good. What is doing you good is the perception that you build up over all of your content as to who you are, who you're for, what you're good at, what you're bad at. So that's actually quite a big shift because it changes the approach to content creation because a lot more of your effort then has to go away from pumping out new content and towards maintaining, improving content that you already have. You want all of your content to be high quality, consistent, clear, and current. Right? If it's all of those things, it's consistently painting the same picture of your brand to AI and to humans.

Mark Evans: As a content marketer, I'm not sure whether I should be terrified of that point of view or excited. The pressure not to create content means I don't know how my job is going forward. On the other hand, the focus on high-quality content and making sure that the content I already publish is optimized is actually not a bad thing because if I'm proud of what I've done, obviously, I'm incentivized to make it better. If you're a marketer and you're going to say to your CEO, "This is our approach to content marketing. You have to not look at it as single bullets that we're firing to see if we can hit the target. It's gonna be almost like a slow burn. The totality of our content and the quality of our content, that will make the difference in terms of AI. So we need to take a more pragmatic, more measured, more quality-focused view of the world." Is that the way that I should interpret it?

Tom Rudnai: Yes, I think so. I would say as well, like, whatever the "X, Y, Z is dead" is the very common refrain on LinkedIn. Like, search is not dead. There is still SEO. So, like, I'm talking about your AEO strategy, not your SEO, and that there is space for both of them so you can blend that in a way that achieves the results you need and the timeline you have and stuff like that. But strictly from an AEO perspective, yeah, it's quality over quantity. Every piece of content, we have this kind of framework called content debt. Every piece of content you put out, you take on a small amount of content debt because the amount of monthly, quarterly work that you have to do just to maintain your overall library just increases ever so slightly. So when you understand that, you can be very deliberate about how you do it. We're an early-stage company. We're gonna keep putting out content at actually relatively high volumes. We're pretty good with how we do that, and we have this kind of quality threshold so that we're really careful. But we need to build authority in our chosen areas, and so we actually need some. And so, potentially, it's like building technology. You accept that sometimes. We'll take on a bit of that debt. Yeah. You just have to have an understanding of it and know that it has to be managed.

Mark Evans: One of the other sort of analogies that you talk about is the idea of an iceberg. And curious about the mechanism underneath the metaphor: how does a brand become viable before it's ever mentioned?

Tom Rudnai: For listeners, the metaphor also for me, like, it's an epiphany that I don't know if it's actually visual for other people's world. But basically, the search funnel again, because all of the volume sits at the top and then you try and push people down. Mhmm. AI is the exact inverse. Right? All of your traffic, you get your traffic from the bottom-of-funnel posts, not the top-of-funnel posts, which means that you actually get a lot less traffic. So the way that we think about the iceberg is what above the surface is that 16% of AI responses where there is a citation. So they're the ones that under current metrics you can track. So if you say, "AI is not that important to us. We're only seeing this many citations, and then we get 100 hits a day." Well, you are seeing the tip of the iceberg because 16% of responses produce a citation, and then what percentage of those actually click through to your site? So if you're seeing 100 hits, you can multiply that massively to see the actual overall impact that AI is having. It's a very common, for anyone who's watched the Titanic, the vast majority of the mass of any iceberg sits beneath the surface, and that's just a visual that I find really helpful for understanding AI. Anything that's showing up in your current metrics is the tip of the iceberg.

Mark Evans: So if you're being successful in terms of tip-of-the-iceberg activity, that means that you're probably doing really well with activity below the surface as well. You just can't see it.

Tom Rudnai: I think it does and it doesn't. What I can't tell you definitively yet is the correlation between success at the tip and success beneath the surface. I think the tip is: are you visible when someone searches for your solution? Everything below is: are you influencing problem framing and requirement building in a way which gets more people to search for your solution? It's like performance marketing versus brand marketing or positioning, and that's what it all comes to.

Mark Evans: What I find interesting about that analogy, if in fact it's accurate, is that top-of-the-funnel content, especially in B2B and SaaS, is sexy. It's fun. It's lists. It's "10 things you should know." It's "why we're the best."

Tom Rudnai: It's the

Mark Evans: kind of stuff that is low-hanging fruit, easy to engage. It's the kind of stuff as marketers we think that consumers like to read because it's, I'm not using the wrong word, but it's light. Bottom-of-the-funnel content is dense and heavy and big on product education and showing people how platforms work. And as a marketer, I can tell you it's tough, sloggy. It's important, but it really isn't that exciting as opposed to being on the elliptical where you're racing really fast. Well, gonna be

Tom Rudnai: It's leg day.

Mark Evans: Yeah. It's a leg day. That's your subscribers. Shifting gears a little bit. When you look at AEO and how companies should approach it, when you see how companies are trying to frame themselves as the only option, a lot of them lead to brand positioning and the idea if you have clear and compelling brand positioning, that's how you're gonna attract and influence buyers. But in the AEO world, influence is a different creature. Can you provide some insight into that traditional mindset of positioning versus the way that buyers behave and what they react to in this AEO world?

Tom Rudnai: Would you mind expanding on the question a little bit?

Mark Evans: In the traditional world, if, like, we all lead into brand positioning as: "What do you do? Who do you serve? How are you different? And what's in it for the buyer?" And if... Yeah.

Tom Rudnai: We feel if we get

Mark Evans: that message out into the marketplace, then that's gonna be enough to make them say, I wish I should check out brand XYZ. But in the AEO world, I don't know if that stuff matters as much as opposed to creating enough content, enough high quality content. Does it complement good brand positioning, or is it more important than good brand positioning? Like, how do you get that brand story out in an AEO world?

Tom Rudnai: So there's a few things that we've seen work really well. I think the first is specificity is actually really important. You've mentioned it, like, the all-in-one. That's not a very good claim to make anymore the way let's use the CRM category because one, a lot of people know. If I'm a CRM startup right now, I'm not going to be the CRM for everyone and for other things. Salesforce and HubSpot are probably gonna have pretty locked down. What I can do, I can go a lot more specific. And going back to those use cases, I can say, okay. I'm the best CRM for small and medium businesses in London who are really interested in MCP or AI-first integrations or something called, like, describe Atio. There's specificity is a really easy way to embed your positioning. And when the criteria, the AI over the course of a long set of interactions with the user, what it does during that time is it gathers context and applies criteria. And if you could find your positioning, which is a big enough meaty part of it, then it's gonna eventually come to you at that point. There's an analogy or an image that I quite like here, which is think of, like, a spotlight on the street. You can run around at the bottom trying to, like like, it's very difficult to hover every even trying to get into the spotlight. You have a couple of things that you can do. You can run around chasing. That's what I think most people are doing at the moment, or you can actually go further up towards it, and then suddenly, it's an awful lot easier because you can get the spotlight a greater percentage. Right? So that's what they're trying to do is go up towards it and try and influence the way that AI frames your category for specific groups of people in a way that will ultimately make visibility inevitable rather than you having to chase it. So how do you do that? What the biggest thing that we've seen work is original research. But I don't know if you saw Google did a big core update recently, and one of the biggest kind of trends that came out of it, people have analyzed that, is 22% visibility boost in the AI overviews for content that was based on original research versus content that wasn't. So we have this concept called information gain, which I think should be a KPI for every content team out there at the moment, which is basically, again, if we go back to that old role of content query: summary of knowledge against that query. Best one wins. That is useless now. If all you're doing is summarizing knowledge and get probably getting AI to do well, I could get AI to do that for me on a bespoke basis. I don't need your content, and AI doesn't need your content. It's useless to humans, and it's useless to machines. What is useful and what does get consistently picked up and reinforces position is having some sense of your content ID or some kind of original research. That's what we call information gain. Something that produces net new knowledge that is worth citing, that is worth incorporating into how AI understands your category. That seems to really consistently boost the authority of your site and lead to influence. We actually we have a framework for measuring, which is you have different tiers. So there's level zero, which is no information gain. Level one is interpretive gain, so a new slant on an existing piece of knowledge. Right? And you can achieve that with AI if you're really good at how you you can feed your positioning, your market perspective in, and how they produce level one. Level two is empirical information gain. So new data, new research, survey, whatever it is. AI won't produce that. And then level three is conceptual information. So backed by original research, something new. And in a way, I would like the Dark AI, for us, is a new concept. Right? Picked up. Again, to go back to your question about a while back of what the CEO should do, one of the biggest things I would say is investing in that original research function because it feeds emphasis.

Mark Evans: Basically, you're eating your own dog food by creating this original research and focusing on the Dark AI. What's happened since you published the report? Have you been able to see, like, within AEO or within SEO or within inbounds? Is that kind of thing? Is it actually working for you? That's an interesting case study. You're telling other people to do, but is it working?

Tom Rudnai: Yes and no, because it's not instantaneous. I think certainly, yes, it's produced inbound and produced results, and it allows me to go and have conversations like this. That has a powerful effect. You get press coverage, things like that. It's helped establish our new positioning. We pivoted into this problem a little bit, and it's helped to establish that within the models. If you go and ask about us, we're not quite at a point yet where it's gonna mention us over some of the competitors. It's a good case that, you know, that's one of our big priorities for the next couple of months is to solve that: a deeply hypocritical gap for us. What you have when you're early.

Mark Evans: As I said off the top, I get a lot of inbounds for being guests on the podcast, just like probably every other podcaster these days in the days of automation. I would say 95% of them I reject because they're irrelevant or off topic. But when I saw the pitch about Dark AI and AEO, it was like, well, I could rally around that.

Tom Rudnai: So, backed by research, and what everyone wants. I think particularly in this space, there is so much it's a space being defined by content marketing rather than research. And we tried to do that for a little bit, and then I was like, hold on. Why don't we actually just use facts to cut through the noise? That's actually a pretty good idea.

Mark Evans: People remember storytelling, but they don't remember facts. That's part of a storytelling presentation that we all marketers like to give. I guess facts do matter. If you're the CEO of a fast-moving company, your marketers are probably using AI. I suspect they've got Claude or ChatGPT, promo subscriptions. They say they're using it to do frameworks and to do research, so they're probably using it to create content. Maybe it's not the highest value content, but in some way, shape, or form, they're using it to create content. But if you wanna be on the on the cutting edge, if you really wanna start to leverage AI and harness the power of AEO, where would you start? From the top down, what kind of direction would you start to give to your teams, whether it's sales or marketing or customer service, in terms strategically, as far as everybody knows where you need to go strategically, how you need to leverage this new wave of technology. But how does the CEO ensure that best practices are being followed, that their efforts are being successful? Because at the end of it's all about ROI. How are we gonna spend our resources, and what kind of return are we gonna get? Like, what kind of tools or insight does the senior executive team need to have to make sure we're doing the right things?

Tom Rudnai: I think my first thing would be that you have to give people space to play, and you have to give them permission to do that. And that requires a little bit less obsession with efficiency. I think it's something we've done. It's a sad fact of technology and AI. It's all creating this hyperfocus on how efficient someone is, and it creates no space to play with it. So I don't think it's a coincidence. When I said earlier, the early adopters are CEOs and investors. It's people who are in control of their own time and then can say, you know what? I'm gonna spend today playing with Claude, and I'm gonna see what I can do. If you're in a team, you've got stripped KPIs and someone breathing down your neck for what you could do. Right? I had someone doing that every day. The number of days where I'd have to say, I was playing with this. It didn't work. And that's not acceptable. So you have to create, like, the freedom and the culture that people can do that and look and judge the outputs of it over a longer time horizon. Like, what did you do today? It's just yeah. That that sort of. The other thing I would say, challenge people to produce quality with it. And, to be sure, we could go in for ages into how you do that, but I would challenge people in the team not to replace something, not to have it do something end-to-end. Look at a process. Think. If I had 10 times as much time to do this thing, what would I do, and how can I use AI to then go and achieve that? Because AI does have all the time in the world, all the patience in the world. And when you're really clever with how you blend it into a process that works, you can unlock so much. You can improve the output so much rather than just multiply the output.

Mark Evans: I love the idea of quality over quantity because in the AI world, quantity is easy. It's easy to scale your content, a couple of prompts, and you've got 10 blog posts for the rest of

Tom Rudnai: the... Yeah. Whatever you wanna do.

Mark Evans: But how, like, CEOs are busy. They put other priorities, and they're not always watching what marketing is doing. How do you make sure that there's alignment in terms strategically, as far as everybody knows where you need to go strategically, how you need to leverage this new wave of technology. But how does the CEO ensure that best practices are being followed, that their efforts are being successful? Because at the end of it's all about ROI. How are we gonna spend our resources, and what kind of return are we gonna get? Like, what kind of tools or insight does the senior executive team need to have to make sure we're doing the right things?

Tom Rudnai: It's difficult because I think it's really hard to put, like, a carte blanche rule because it's gonna be different for every team. Somebody's gonna have that. I take us on this particular topic. That's all I can really speak to other than people that I immediately know and obviously talk to about this. But for us, we're a relatively small team. I use AI to create alignment a lot, so I've built a system where, basically, via MCP, all of this conversation, which I'm recording, I'll have I'll have my notes from Grainola. I have my notes on our one-to-one. It all feeds into a central document, which manages our good market alignment, and the team in Claude can go. And because it has my one-to-ones with everyone in the team, they can say, what's John up to? What's Steve up to? Whoever the people are on the team, and they can ask. So it keeps everyone aligned on a kind of project basis through that. And that helps because when you're asking lots of people to play, that becomes a really high risk that you just have five people playing with the same thing and trying to solve the same problem. So trying to consolidate all of that into a way that they can get quick feedback on whether an idea is worth pursuing before the next one-to-one, I think, is really important to the best of your ability. Right. I think within a leadership team, again, I think it's gonna vary depending on where they are, but I think it would just be setting the expectation and giving the kind of freedom to then fail to solve that problem, I think, is where people get tricked up.

Mark Evans: I love the idea of experimentation. I spent a lot of my days experimenting with Claude, especially Co-work as a game-changing tool in terms of how I'm using AI. I think that you're right that a lot of marketers are under pressure to perform, and that means that often they they're afraid of making mistakes or doing something that doesn't follow best practices or a campaign that isn't as successful as it should be. And maybe Google was on the right track when they said that 20% of their time should be spent experimenting. I think marketers need that, and salespeople for that matter need that too is that, you know, there's tons of tools out there. There's tons of things you can do, and sometimes they're not gonna work, and that's okay. But, well, great conversation, Tom. Thanks for this. The idea of Dark AI is super interesting. The report, which I'll share in the show notes, is a must read. There's some there's a ton of really great research in there, and it's good to it's good to see that you're, that you're walking the walk and talking the talk when it comes to that kind of thing. Where can people learn more about you and Demand Genius?

Tom Rudnai: Down to our website, demand-genius.com. That's there. We we publish all of our research there, and we publish it all really openly. So go to the bottom. We outline the full methodology. By all means, go reproduce it. But if you find something different, tell me. We'll publish that, too. We're very committed to doing this with the market and figuring out this new thing that we all have to learn how to do together. So probably one of the few things that I promise you is that I will have a different story a year from now if we do this again. So, yeah, I'd encourage you to do that and also connect with me on LinkedIn, Tom Rudnai. Rudnai is very difficult to spell, R U D N A I. I'd always love to connect.

Mark Evans: You should ask. If I'm a B2B or SaaS, why should I call you? What can you do for me? What's your raison d'être?

Tom Rudnai: Oh, God. This is the elevator pitch. I hate it. This is the elevator pitch.

Mark Evans: There's no pressure now.

Tom Rudnai: You've got me there. I will help you build an AEO strategy that focuses on influence over visibility. So see what AI thinks of you. Understand how you can better align content positioning, reputation to influence that over time, and then track the revenue impact of that as well in a more content and data-driven lens to sort of influence.

Mark Evans: Thanks, Tom, for the great and insightful conversation, and thanks to everybody for listening to another episode of Marketing Spark. If you found this conversation valuable, subscribe on Apple Podcasts, Spotify, or your favorite podcast app. Drop a quick rating and share it on social media. You can reach me by email, Mark@MarkEvans.ca. Connect with me on LinkedIn or visit marketingspark.co. I'll talk to you next time.