Agentic Marketing OS: Designing the Live Loop — CMO Roundtable

Julia Nimchinski:
And next up, we’ve got Carol Dietrich, CMO, Hyper Growth Advisor, lovable, 1Password, Nero, Strass Social, and she took it last year in public, and she always hosts the best marketing panels on our show. Welcome! So excited for this! How have you been, Carolu?

Carilu Dietrich:
Thanks! Well, I’m just gonna have the Julia fan club first, because I would like to admire Julia for bringing together so many thought leaders, month after month, and really looking farther ahead.
I’m super excited today to talk about Agentic OS and how it’s affecting marketers, and I have to say, it must be a… Agentic OS must be a term in the industry that Julia found somewhere and said, this is what’s gonna be next, but over the last 3 days, as I’ve been talking to CMOs, and I’m here at RSA right now, talking to a number of public company CMOs as well. Everyone’s like, yes, that’s it!
It’s a new marketing AI agentic OS. Like, I see it transforming every aspect of my business. And so, we’re gonna talk a little bit today about what’s hype and what’s reality, because I don’t think all of us think that it’s here today. But I just want to thank you, Julia, for all the work you’ve done to make amazing conference series, like, over and over. I really admire the work you do.

Julia Nimchinski:
It’s very mutual. Thank you so much, Carlo. Take it away. We have an all-star panel here, and I can’t wait to get into it.

Carilu Dietrich:
Great! Well, I’m so excited. We’re gonna talk today, I’m gonna let everyone introduce themselves, and we… we have a whole hour, so we’re gonna not only talk about the philosophy, but also give some really practical, hands-on examples. So I’m so excited for this panel. So, Esther, why don’t you start by introducing yourself first?

Esther Katz:
Hi everyone, I’m Esther Katz. I’ve been doing startup marketing for the last 15 years, and I’m head of marketing at Genesis Computing, which is a hyper-growth agentic AI company, that has created a platform that basically supplies AI data engineers to enterprises.

Carilu Dietrich:
And you’ve got a super interesting, story about how, you’re running a massive operation, but with only two people and lots and lots of agents, so I can’t wait to dig in. Claire, why don’t you go next?

Claire Darling:
Hi there, I’m Claire Darling. Thanks, Carol Lou. I, am a four-time CMO, a B2B enterprise CMO. I was just most recently at Clary. I’m sure everybody knows Clary was at the heart of, AI, AI agents in the whole revenue go-to-market system, so… Very excited to be on this panel, and I think we’re going to share a lot of good, good ideas, so happy to be here. Thank you.

Carilu Dietrich:
Thank you, and Alex, why don’t you go next?

Alexandra London:
Hi everyone, Alex London. Joined G2 as CMO two months ago. Prior to that, I was at Zoom and Expedia running marketing for both organizations, so excited to be here and dive into this topic.

Carilu Dietrich:
Great! I want to let each of you give, like, a quick, like, 2-minute overview, of how you’ve set up some agentic workflows within the different teams that you’ve been on. And Alex, because she plays a role both on the we’ll call it the analyst side, as G2 really seeing into everyone’s AI buys. You can pick, like, what G2 is saying in the market, or what you’re doing at G2.
Both of those will work. But I just want to give everyone on the audience context, because, you know, Esther is running this really complex AI operating system with a small team. Claire had a bigger team.
I’m actually doing a fair amount of advisory for public companies at this moment related to AI, and what we’re seeing is that there’s different patterns and challenges at different states of scale. So let’s just start first with, like, some level setting, so people understand each of our points of view. So Esther, go first and just tell us a little bit about this AI operating system you’ve built.

Esther Katz:
Yeah, so first of all, a little story. When Genesis and me were discussing me joining Genesis, I first came up with your, you know, typical startup ramp-up plan that included growing the team and a significant budget, and the founders looked at it and they said, yeah, that looks great, I mean, we know you know the job, but really, we’re not looking for that.
We’re looking to build something completely different. different, and eat our own cooking, or as I prefer to say, drink our own champagne, which basically is use our product capabilities to build the most lean, the most small, and the most efficient marketing team that you can imagine. And I was like, okay, well, that sounds like an interesting challenge, I’m up to it. So… I have one team member.
We run… I would believe that, in usual circumstances, I would have a 7-9 person team working with me right now, doing the amount of work that we do on delivering the pipeline. We… we… our marketing team owns the pipeline at Genesis.
and this work is done by the number of AI tools and our internal agents that all work together in what we call a context graph, which is an environment, a digital environment, where they basically communicate and collaborate with each other, and they’re all interconnected.
And they pull in the data from these different sources and push it into Salesforce, which is currently our, you know, the source of truth for sales, and we work very closely with sales, and, you know, we’re able to launch a campaign in a matter of hours, not even days. I mean…

Carilu Dietrich:
Yeah. Everyone out there, I asked her if she would put it in writing and show us something we could screenshot, so we’ve got a couple slides we’re gonna share a little farther along. So thanks for that, Esther. And your, your overall company, you said, was, like, maybe, how many total people?

Esther Katz:
We’re about 40 people now, split between sales and engineering. Again, it’s… it’s… the time is very different. The productivity and capability for these companies that are AI-first, and, you know, obviously Genesis is an AI-first company, that was actually founded by senior alumni of Snowflake. Got it. So, you know, coming from the heart of data engineering.
So yeah, we’re small, and small but, loud.

Carilu Dietrich:
Wonderful, thank you. And we have another guest here who’s arrived, Catty. Caddy, do you want to just introduce yourself, and how about you do two things? You introduce yourself and you go a little bit into, the AI. Just a quick highlight overview, we’re trying to just set the context of your point of view on… AI, and a genetic operating system, and then we’ll get into the question.

Kady Srinivasan:
Sounds good. Sounds good. Thank you so much. Hey, everyone, really nice to be here. I’ve actually been on this panel, I think, last year, at about this time, so it’s fun to come back.
I, I’m Caddy, I’ve been in tech for a while, was in companies like Dropbox and Klaviyo, and then… Most recently, an AI startup, which I can talk a little bit more about, but, 3 months ago, I joined Freshworks, so I’m the CMO of Freshworks now.
So I’ve been in, been in this tech CMO seat, you know, 3-4 times now, but it’s been a very interesting journey to go from, SaaS to AI startup to back to SaaS, and kind of seeing the transition of how AI is completely revolutionizing the way that we are thinking about go-to-market funnels, customer journeys, and all that kind of stuff. My, perspective on this is, I think it’s twofold.
If you’re a small company, you are just so much more flexible and adaptable in terms of building AI-native go-to-market architectures. When you’re a bigger company, a SaaS company.
I think it takes a lot of re-engineering of the right kinds of user journeys and the right kind of customer journeys, so it, I can provide the perspective of how I’m going… I’m trying to change this culture from inside out, versus outside in. So we can talk about that a little bit more as we go forward.

Carilu Dietrich:
Yeah, I’m excited to talk about it. One of the companies that I’m helping is Commvault, which is a public security company, and the marketing team has been really one of the more innovative, like.
AI-driving teams within the whole company, and it’s funny, because a lot of… around a lot of survey data, I have seen that marketing teams are moving much more quickly with AI, and part of that might be because, we’ve got budgets that can be reallocated from paid media and events to things that help us drive the same outcomes we’re trying to get to, which are revenue pipeline numbers, but it does seem like AI is a place that’s really trying to do some of the cultural transformation, as well as the technology transformation.
Claire, why don’t you go next? You built a really, comprehensive set of, AI teammates at Clary, kind of across the whole life cycle, right?

Claire Darling:
Yeah, yeah, definitely. Yeah, but I’ve been at Clary, I was at Clary two years, just left in January, as I mentioned, and Yeah, about a year in, this thing called AI was starting to bubble its head, so… so really started to embrace that, in… in Clary in marketing.
We actually became the… the voice of AI and what we needed to do internally, how we needed to change the way we worked for the company, so we were showcasing what we were building, what we were doing, on Company All Hands, our Monday town halls, so it was great, great for the team to get that visibility and Acknowledgement that they were doing some great stuff.
What we did was we were looking at, we used ChatGPT… if I was going back now, I would be using Claude, but ChatGPT was really the tool that everybody was using to build, and we were also using Glean, to build, agents and workflows. So we built, nearly, by the time I left, nearly 100 AI team mates.
These were teammates, and this is usually where I think marketers start, is these were teammates to help us with jobs to be done, you know, make us faster, so velocity, make us, more productive, or just start to go into new services. So we built these teammates.
But we also introduced and looked at our tech stack and looked at, what our tech partners were building in terms of AI agents, AI bots, and started bringing in things like AI SDR, you know, as use cases. So, I think we’ll talk about those later, but we kind of took that two-pronged approach.
And absolutely, marketing was at the center of this, because, like Esther, I own, I was owning all of Pipeline, and, what the contribution to revenue on Pipeline was, so very much important for marketing to kind of be in that step and stage to drive this.

Carilu Dietrich:
And Claire, kind of back to this Agentic OS. Were your agents working together in a closed loop? Or did you kind of… were they kind of agents at the… and you know what’s so crazy is, like, can we sign six-month contracts yet, right? It’s like, literally every six months.

Claire Darling:
Yay!

Carilu Dietrich:
But technology is changing so quickly, so it is kind of funny, because, you know, you even saying, like, if I was building right this second, I might be able to… even though it’s, like, minutes ago you were building, but were your agents talking to each other and doing kind of closed-loop. End-to-end, or were they kind of assistants for each individual human still?

Claire Darling:
But the teammates in ChatGPT were very much functional, like, jobs to be done. We started moving towards more cross-functional teammates, but like Katie was saying, is, like, obviously, we had a lot of tech in our infrastructure, in our tech stack, so you kind of are undoing. What may have been brought in a year or two, three years ago.
So on our agent side, so, like, if we were… if I was talking about the AI SDR agent qualified, yes, that was connected to a lot of things, a lot of systems. So when we were using AI agents and bots that was through our tech stack, yes. Our AI teammates, no, but we were actually going in that direction.
And I think now what we’re seeing is because of Claude, a lot of people are connecting together and kind of building a lot of systems without, you know, looking at their tech stack.
And I… I’m 100% with you, Carol Lou, is I think the days of… year, two, three-year contracts and licenses for… for tech stack is… has to change, because it’s changing around us so much, and the solutions around us, like, you know, it’s changing every few months, so don’t… don’t lock yourselves into long-term contracts. You can’t… you have to pilot.

Carilu Dietrich:
Yeah, it’s a big transition. Alex, I mean, you’re in the middle of that, right? Yes. I mean, I know I was friends with Sydney, and G2 has been reporting like, the crazy influx of the number of tools coming at marketers in AI. What does Agentic OS mean to you in the marketing landscape, and how are you thinking about it at G2 these days?

Alexandra London:
Yeah, so as you mentioned, because G2 is the world’s largest, and I’d say most trusted data source for B2B software buying, we’re kind of where everyone is starting to understand what are those tools that are emerging, and how do they understand how this fits in, kind of, to the broader tech stack.
And I think more importantly, we’re also pulling on, kind of, our customers and the millions of annual research that we have around, you know, we serve more than, like, 200 million annual buyers, and so grasping where where are they searching? What the data is telling us, I think where the focus is and what the team is needing to drive for the business.
And so what we’re seeing right now is actually 93% AI searches has changed, ultimately, where AI is the first to now kind of conduct their research. That is much different than where we were just even 6 months to a year ago.
I think also what we’re seeing is 51% start their research much more in these LLMs than in Google, and then 53% is more productive with AI search than kind of that traditional search piece. And so when you piece that all together, what we’re actually seeing is now a 20% increase in kind of those emerging AI technologies that are coming out, especially in categories.
that folks are needing to understand. And so, I completely agree that this new, you know, 2-year, 3-year contract is going to completely change, because these industries that are focused on the, you know, AI component is really looking at that, like, what can you do in the next 6 months?

Carilu Dietrich:
And as an AI-native company, it’s just as terrifying. So, as Julia said, I was advising Levable earlier this year, and one of the challenges is that Levable is an application creation AI agent that helps you make websites and prototypes and all sorts of visual design.
But it, you know, it has, like, a pretty competitive landscape, coming from all sorts of different, dimensions, including Claude itself, to create web pages. So I, like, it’s changing for buyers, it’s changing for sellers, it’s just, a wild ride.
But one of the things that’s so exciting about being in technology is that we get to keep learning every year, and I’m actually really excited to learn about Esther, your project, and so I’d love for you to, like, dig a little bit deeper. I can share the slides here if you want.
I don’t know which one you want to share first, but you want to talk a little bit about your tech stack, and a little bit about this context graph, because it sounds like you’ve not only created the agents that do some of the specific things that all of us have been doing, and Claire hit on. But you’re, orchestrating a lot of them together, it seems like, as well.

Esther Katz:
Yeah.

Carilu Dietrich:
Take us through, like, maybe 5 minutes to give us an overview.

Esther Katz:
You can start with the first one, where you have outbound and inbound.

Carilu Dietrich:
Oh, yes, hold on one second. Sorry, I started looking at a question from the audience. We will take questions from the audience. If you want to put them in, I’ll keep looking at the chat. But first, we will go to SlideShare. Okay, can you guys see my screen? Can you see my sharing, my PowerPoint?

Julia Nimchinski:
Yes, Carol, we do.

Carilu Dietrich:
Okay, great. Esther, did you want me to go to this slide first?

Esther Katz:
Yeah, the one that has outbound and inbound.

Carilu Dietrich:
Yep, I think that that’s what’s on the screen right now.

Esther Katz:
Okay, because I’m looking at my screen, because I have it here in front of me, so I don’t see it, and this way I can see you all. So, you know, what you can see here is basically two main motions, outbound and inbound. there is a little bit of brand marketing here, but of course it’s not complete. This is mainly focused on, lead generation and lead capture.
And you will see all these very familiar, you know, let’s say, disciplines of Outreach, of accepting the leads, de-anonymization of, website visitors, rolling them into sequences, nurturing, or, you know, sales sequences. And normally. And it looks pretty normal.
The only difference is that there are no humans involved in these processes, except… my teammate, Kevin, who is an AI principal, GTM, and myself. So, when we planned this, we have kind of drawn this map, and then we have hand-selected the tools. I spent maybe 4 months going to demos and interviewing various tools. And then we bought these tools, and then we connected them with EVE.
What you see here, EVE is a Genesis orchestrating agent. what you see here in the square, Agentic data engineering platform, Genesis Computing, this is our own, heart… agentic heart, let’s put it this way. This is where EVE lives. It’s a very, simple… chat interface that might remind you of, you know, ChatGPT or whatever. She comes with pre-built blueprints or workflows.
She has access to everything that is stored on Google Drive, or Slack, or any other data tool. Of course, you know, all the data, we use Snowflake for that. So she knows everything about anything or anyone. Any piece of data that is created within the company, she has access to.
She also knows everything about marketing, and at some point, she will get all the data from any of the AI tools inside her, or she will have access, and she’ll be able to crawl it, pull it up, and work with it, according to the blueprint that she has been tasked with. I don’t know if that makes sense. It’s not easy to understand, because it’s a completely different way to work.
But I think, for me, the way to understand is think about these agents as departments and employees of these departments, or disciplines in marketing, and… I don’t know, let’s take content generation. So, we have this tool called Avery. Avery is both a, data tool, and a content generation tool. So, Avery is going to look at, every LLM model out there.
It’s gonna track how often, does your brand shows up in search. It’s gonna make, suggestions on what should be improved. And it’s also going to create content for you. So it’s gonna spit out that blog post. That will look like it was risen by a human. And then, through Eve, it’s gonna be funneled into, for example, Mutiny page, right? Mutiny is a tool that creates, web pages and campaigns.
So, she will then funnel it into Mutiny, and then we will create a campaign page, which will have the blog, which will be personalized to the industry vertical. And all of this is gonna take, like. It’s gonna take minutes for them to create it, but for us to organize all this is gonna take probably a few hours.
And that’s how we move from data to final execution in a very, very short time using very, very limited human resources.

Carilu Dietrich:
And it’s so fascinating because, I mean, I think all of us… I know these… I apologize to everyone on the phone, I know this is small, this was kind of my request for her to, like, show it all, and so you could screenshot it and, like, zoom in yourselves. But, I think so many of these flows make sense to all of us, because it’s the, activities we knew were happening.
But what is shocking is that two people are making all of these things happen. How do you split up the roles and responsibilities between the two of you on the team for all this that you’re orchestrating?

Esther Katz:
I do what I like to do. I’m so lucky. So I do strategy and…

Carilu Dietrich:
boss.

Esther Katz:
And then, you know. usually I come up with an idea, looking at the data and, you know, tracking how are we performing in the existing campaigns. And then, you know, I would get together with my human team member, and we would brainstorm how we can use that, and then we would come up with an idea, and then this idea will go into execution.
I do a lot of copywriting, or let’s say me and my writing agent. We do a lot of copywriting together. Again, I like it. I also think that copywriting is probably one of the disciplines where More human touch is needed than less.
And then… you know, we… we have a design agency, so when it comes to, let’s say, field marketing, we’re doing Snowflake, we’re doing Databricks Summits, so we have to come up with something very creative. So all these ideas will really come from humans, and sometimes I will brainstorm with the agent, with Eve, and say, you know, pull up everything we did, and give me a few ideas.
But mainly they’re human-generated, and then they’re funneled into execution with an agency, and boom, like, it takes us a few days to get the whole, let’s say, Snowflake Summit presence laid out for approvals and budgeting.

Carilu Dietrich:
Do you want to share any of the other slides, just for two more minutes, before we kind of go on to talk about some of the insights that you’ve surfaced with the whole group?

Esther Katz:
So, if someone… if anyone is interested to understand what is context graph, then you can, scroll down.

Carilu Dietrich:
Yep, I’m on the context graph, slide now.

Esther Katz:
Yeah, so for those who are not familiar with data engineering terms, context graph is basically a digital twin of your company. Every data piece is here. So, here, what this shows is how do we work with data de-anonymization on the website? RB2B is our tool that we use, it’s external, it’s a software.
And then what happens with all the RB2B data, how it’s funneled into all these other different tools? There is a special agent that is an ICP qualifier, so RB2B will say, you know, John Smith from company XYZ visited your website, spent 3 minutes. Okay, it’s qualified. Then it goes into next qualification stage. It pulls out from the file all the data on how it should qualify.
You know, they should have a certain title, they should be in the company for a certain period of time, whatever your qualification criteria are. And then it makes a decision. What am I doing with this? potential prospect. Am I funneling it into Salesforce, and sending a notification to the account executive in that particular region?
You know, it’s like, it’s gonna come up with different flows, because we have created these flows already, so now it only has to choose. And… if… when you are inside the product, inside Genesis, this is a screenshot inside Genesis. This is what I can see.
I can go into my marketing environment and pull up that context graph and see everything that’s going on, and then I can zoom in and go inside any of these dots and look what’s going on inside there.

Carilu Dietrich:
Got it. Which is amazing, because kind of working with bigger companies. You can see how valuable that would be, but the governance and agreement, and we’ll go to Caddy next, about massive companies, that it’s just so difficult to figure out. Like, you’ve been able to move so fast, how do big companies operationalize this at scale?

Esther Katz:
So, I can tell you that Genesis only works with big companies, right? We are a unique case, but we don’t sell to startups, really. And enterprises are implementing this for their own workflows. They’re not necessarily marketing workflows, mainly they’re just purely data engineering workflows that service any other department.
in the company, but they are all moving towards this, and governance is not an issue, because none of this data belongs to Genesis, or Genesis really, like, has access to it. It lives in your environment, and it’s platform agnostic, so you can use Databricks, Snowflake, whoever. To see the…

Carilu Dietrich:
The whole data graph, not how it all fits together.

Esther Katz:
Yep.

Carilu Dietrich:
Caddy, where are you guys on the journey? Like, what is step one for CMOs who are trying to deploy the Ajetik OS at scale?

Kady Srinivasan:
Yeah, I think the, for me, the way that I’m crafting my journey through this is there is just so much, lack of automation in what we do today, especially in big companies. I think this is probably going to be true, especially if you go out of tech and into more the real-world kind of companies, it’s probably even more true that there is very little automation.
So I’ve actually started my team to start doing things like create your gem, create workflows in Gemini, and just get that base… some of that basic stuff rolling, because then people are just a lot more effective, doing that. Then, the second step is I’ve identified a couple of people on the team who are, I would say, AI-native kind of, talent, and so they, to what Esther was saying earlier.
we’ve started to build what we call an AI brain, which is essentially the entire context graph of everything that we are doing from a marketing perspective. So we are dumping all of our campaigns into it, our narrative positioning, just kind of making it the… kind of the Jarvis, type of things, if anyone knows Avengers.
We are creating that Jarvis of the world, and that has been incredible because it’s giving us a few different lenses. It’s one… It’s giving us a sense of how are we showing up in the market from a competitive perspective? Are we actually stacking up? What are the blind spots? what’s happening there.
Secondly, it’s giving us a sense of how discordant are we in terms of all of our messaging going out. Because, you know, as a big company with multiple products, multiple product lines. all of our messaging is fragmented, and it has been incredibly hard to keep it all together in one place.
And then, I think the third one is, now that we’ve got this, I want us to get to the point of multi-agent orchestration and being able to create that step-by-step journey of, take this, create this, take the feedback, put it back in, doing that kind of stuff. That, I suspect, will happen over.
the course of a few months, and or if I can get people who are trained on cloud skills, and who can do it faster. So that’s how I’m thinking about it. I cannot underline how important it is to to do change management.
I know we all want to talk about AI and how quickly everything is moving, but it’s still humans, and it takes an incredible amount of time to move an organization to the level of urgency that you want them to adopt. and be okay with the changes, and be… and learn to embrace the changes, and not fear the changes.
So, that part just takes a lot of time, and that’s been a… you know, that’s been kind of my initial journey. I think we are still at the very, very starting phases, and we’ll see how we kind of go from here.

Carilu Dietrich:
On the teams where I’ve seen the most change, it’s often that the CEO is so engaged, and has so much urgency, and has approved budget, and is moving it. I mean, clearly, Esther, they, like, made that the requirement when they hired you. Claire, at Clary, you were able to deploy a lot of products pretty early. Were you sunsetting other products to find that budget? Was your CEO really supportive?
Like, how did you drive that urgency that Catty’s talking about, and that Esther just has natively?

Claire Darling:
Yeah, I think two things. I mean, we’ve, you know, our tech stack, when I went in, probably looks like 9 out of 10 of the SaaS like, company marketers, tech stacks, so we definitely looked at, sunsetting or, ending our contract early, because we didn’t see a future for that technology, or we weren’t seeing ROI or value.
So yes, money and budget did free up from that, but Where I did it was, rather than say, I’m going to buy all these tech tools, and I’m glad I didn’t, we piloted, so things like the qualifieds of the world, the follows of the world for our microsites for personalization, we really were selective. We used, Ludible for our customer, case study creation, so we picked where it would have most impact.
If we were going to buy some technology early. But like I said, we were using ChatGPT, and what I did was, and Katie said this, is like the whole change management. My team started building… my team started building in ChatGPT, they started building in… we had Glean, so we started building our workflows in Glean.
I actually brought the team from Glean, Kix, who is the CMO over there, and his marketing operations, leader, came to my staff calls once a month, and they shared what they were building in Glean, and we then built the same thing in Glean, and they shared what they were doing. The same thing on ChatGPT.
We were… the team was just said, okay, go and look at your use cases, go and look at what… where you think AI can help you the most, and go build out those those little AI teammates and workflows. And the way I positioned it to them, and I think Katie was right on this, is, like, change management.
was, for the team, was very much around, this is gonna help you, this is… you’re all gonna need these skill sets, all of our roles are going to change.
And, you know, having this skill set of being able to and saying, I’ve built these ChatGPT teammates, I’ve built these Glean workflows, they were starting to build in Cloud, was huge, and so future potential, current skill set, future career was really how we got the team embracing on that. And, you know, Andy, our CEO, was very much like, you know, well, why did you start doing this?
And I said, well, because where the world is changing, and he really embraced and supported this from a marketing perspective, so didn’t really have to go and get additional budget or anything.
And then, I think what’s changing now, and I’m sure the rest of the panelists will agree on this one, is you have to look at your headcount, you have to look at your humans and your program split, and it’s not necessarily anymore that 60-40, is you may not hire more people because you’re actually building more of these, or building more of these teammates, or you’re bringing in more of this technology, so… that’s all changing, so I think, you know, how we look at marketing investment and budgets is really changing as well, so that’s really how we did it.

Carilu Dietrich:
I’m seeing this, like, tale of two worlds. AI-native companies are growing and scaling pretty quickly, because there’s just so much demand for AI products. You know, everyone’s budget line item is flat for software, or flat for headcount, but, like, please invest in AI and figure out what works and get us competitive advantage.
So AI companies seem like they’re adding employees, but everyone else is… is either kind of, like, letting attrition decrease the size of the team, or potentially making cuts, and as they create more efficiency, and it seems like that it might be where some more of the AI budget even comes from, instead of just the tech tools.

Claire Darling:
Yeah, and the traditional roles, in the organization and marketing are changing.
Like, they’re not… an organization design does not look the same now as it did 6 months to a year ago, so some of the roles have changed, and there’s, you know, we know about these new roles, like go-to-market architect, go-to-market engineer, marketing engineer, kind of looking at AI, so… some of the functions, like, say, creative, is very different.
Do you bring in a human for creative, or are you bringing in AI? You’ve got those choices now, so I think it looks very, very different in how you’re structuring your team, and And kind of what you invest in going forward. So I still… I also think you have to think in six-month increments, too, is… because who knows?
Who knows where the technology’s going, and if you need a person or… or tech to… to fill that gap.

Carilu Dietrich:

Alex, I have a slide from you where you can share a little bit more about the AI agents you have, and I don’t know if they’re orchestrated together, or they are still kind of, like, helping, you know, like, it feels like we’re on this, you know, continuously building foundation, where it’s like, oh, we built the foundation, now we’re building something else, and then all of a sudden we find out that our next gen is just the foundation of the next next gen.
So I, I know G2 has a lot of, is working with a lot of cool companies, and we have some of them highlighted here. But give us a little bit more. Hold on one second.

Alexandra London:
And I think that’s to your point, where everyone’s building that kind of, like, agent orchestra where it’s all feeding into one another, but there’s also those quick wins of companies that I think are just trying to figure out how to get started.
And there’s what you’re building internally, but also what you’re building externally versus the headcount that’s needed to support your customers when it comes to your website chat and email outreach. And these are quick agents that you can build to fulfill those gaps across the team, but also a significantly higher ROI when you’re building this at scale to interact with your customers.
I’d say the tools that you’re seeing here, this is just a subset. As you mentioned, we work with pretty much every company in Fortune 500 and beyond out there of all these different tech stacks that we have. But really, it’s how are you building these together as we’re seeing the market emerge of this kind of 20% growth in the AI marketing tool adoption.
So I think there’s what companies are trying to build versus actually the adoption that we’re seeing, and I do think there’s a difference there. What we talked about, I think, even, you know, yesterday as a group a little bit ahead of time, was How does this now appear to marketers of, like, what do you unlearn to relearn?
And I think a lot of this is also figuring out, within these kind of, you know, month increments versus year increments of build, it’s what are we untangling from our normal SaaS tech stack to rebuild in a way that will help us move with speed and velocity? And a lot of that comes with the agents you’re building internally, as well as what you’re building on-site.
I mean, most recently, we just launched our MCP that we’re now accessing for our own. kind of drink your own champagne, as well as what our customers can access around real buyer data in real time, and how do you build a full go-to-market strategy off of that using an agent through MCP directly connected to Anthropic?
And so these are ways that I think folks who maybe, you know, see what Esther’s built, which is incredible, and think, how am I going to build this at mass scale? Like, there are steps that every company and marketers can take just to get, you know, those first agents out the door. Before you build it, at scale.

Carilu Dietrich:
Absolutely. I mean, I think we’re definitely in the resurgence of the build versus buy story.
You know, like, clearly, as you were highlighting, using OneMind, or Qualified, or a number of other tools that have AI baked into them, I’m looking at one right now called Upside that’s an AI-native company that does analytics and data, bringing together data from all over emails and calendars so that you can see what’s really influencing B2B deals.
But so there’s so many of these that are not, you know, that are by AI tools, and then there’s CMOs who either have, like, more build skills themselves, or build folks on the team, and in fact, Megan Eisenberg was from, the CMO of Samsara was supposed to join us on this panel, and she’s one of the CMOs that I’m most impressed with for having built a GTM engineering team.
And they’re, like, hand… developing so many of their own AI, AI… products, for, like, lack of a better word. I mean, agents, orchestrations, processes, all of it. So, yeah, it’s really changing the landscape. How do you guys think that this, like, open question to the whole team, how do you think this changes the role of a CMO?
Does our job change, or is it still setting the strategy and figuring out the ICP and assigning work? I mean, Esther is assigning work to all of her agents, her big organization. Is the job different?

Claire Darling:
100%. Very… it’s very different. I think if we look at… CMOs from, again, like, a year, two years ago, our role has changed. Everything around us is changing. If you think about, you know, how buyers behave. like, Alex, you said this, it’s like, the LLMs, people are doing, AI searches, they’re not, you know, and now agents are buying, rather than a human buying.
We have to think about that as we’re thinking about, you know, how we go to market. I think our role is really getting a lot more into terms of strategically, how do we orchestrate not just marketing, but the whole of go-to-market? We can’t just think about our function or silo, because we touch sales, we touch inside sales, we touch customer success.
I think we are… we’re really kind of looking at, you know, how do you govern this? How do you… how are we kind of building, you know. campaigns, like Esther was showing, I think it’s all very, very different. It’s all very exciting.
And then you’ve got your tech stack, so you’ve got processes changing, so I think the whole… way that we are working and running marketing has… has changed, and AI is at the center of this. So, I would… I would just say that as a generic comment. I think it’s… it’s so… it has changed. The CMO role is very, very different.

Alexandra London:
I think we sit, and I completely agree. We set as true growth operators now at all aspects of the full funnel. It no longer is about which campaign is running in which month, and which channel is going to execute. It has to be the full end-to-end journey changing in real time.
And more precise with all the intent data that you’re serving it, as well as the buyer is changing in real time, and where do you show up. you know, traffic is declining, how do you quickly re-measure?
I think our old-school attribution models are going out the door, and you have to pivot, and so it really puts us at the center of not just AI, but this growth operator role, and it’s not just about, you know, which campaign in a whole month planning cycle, but this always-on adapting in real time with personalization.

Carilu Dietrich:
it seems like the timelines of marketing are just a different game. Remember when we would be like, oh, we’re gonna do annual planning, and then we’re gonna have campaigns, and there’s gonna be 3 campaigns this year? I mean, we ran a campaign… campaign, I’ll put it in air quotes, at Lovable, when, OpenAI released some version of ChatGPT, where we got, like. 40 hours notice, maybe?
And then the engineering team started playing with it, and then we, like, integrated it, and made, like, an offering, and had, like, a video, and had… a landing page, and had social media campaign with influencers in, like, 30 hours. Like, I have never launched a campaign from, like, product concept initiation to campaign live in market gathering leads in 30 hours.
And, like, okay, we’re a small company, and, like, okay, it’s, like, maybe one of the biggest things that happens, but it does seem like… you know, Metadata.io is a CMO that I’m friends with as well, Lisa, and she’s working on… Metadata’s release, this, like, end-to-end AI engine where you can, like, create ad copy, you know, concept ad copy, create ad copy, serve it, look at the performance of it, and optimize it, like, all within one AI agent.
And again, there’s this build versus buy, some people are building that themselves, but the velocity is, like, a crazy difference, and Esther, it just looks like what you’re doing. The velocity could be unlike what you’ve done in other roles, even with much, much bigger teams.

Esther Katz:
We just launched a new version of our website, and I think it took us less than 2 weeks to get it done. I mean.

Carilu Dietrich:
Remember when those were 6-month projects? I’ve, like, worked on a lot of 6-month projects to refresh a website.

Esther Katz:
And I remember going into this project now, thinking, oh my god, it’s gonna take all my life away, and then it didn’t. It was really, really fast, and it was really, really painless, and I think one aspect of it is maybe what we don’t talk enough about, is how do we collaborate, now having these agents on our side, and I feel that we collaborate better.
There is less friction, there is less going back and forth, and even if there is, it takes less time, it’s easier to execute, so there is less frustration. And that makes the collaborative process more enjoyable, and I think people create great products and campaigns when they enjoy what they do. This is a big part for me in that job, to do it well, is to have fun.

Carilu Dietrich:
claire, you were… looked like you were gonna talk about the velocity. Did you guys really change the velocity as you rolled out all these jobs to be done?

Claire Darling:
I mean, you just got me thinking about the MetaIO that we were talking about, is, yeah, we had an AI teammate that would go scrape competitors’ websites, go scrape their ad campaigns, go see what they were doing on LinkedIn, and we could, within an hour, change our ads, change our copy, and then get that back out through LinkedIn. That… Yeah, that used to take a long time.
And Esther, congratulations, the website in 2 weeks, amazing. Yeah, I remember the days of 6 months, even longer, for websites, and you do wonder now, like, how websites are gonna… how valuable websites are gonna be, going forward, and I think that’s even changing in terms of how we look at all of our channels that we’re going, you know, what’s the importance of each of those channels?
It’s shifting now for marketers and CMOs.

Carilu Dietrich:
Have any of you rewritten your websites for agents yet? I’ve been talking kind of off the record with folks about, like, certainly LLM traffic is rising, SEO is going down, conversion rates from LLM traffic converted, like, 3 times the, the SEO rate, so it’s, like, qualified traffic. So, like, philosophically, we should build or rebuild our websites.
To serve agents, but we still need to serve humans, too, and do, like, shadow them? Do you… like, make compromises? How have you guys approached the website agent and human serving?

Esther Katz:
I completely changed the format of our blogs, for example. Well, let’s start from the beginning. I use at least two tools currently that serve the SEO slash geo purpose for analysis, data analysis, and, let’s say all the backend of it, all the coding, to make it crawlable to the maximum, to make it very, very readable and appreciated by the robot crawlers.
On the other hand, the format and the way we structure information has changed as well. So, we have… we have… restructured it to match the requirements of models, but I do believe it also improved how we write our blogs for humans as well, because models are very smart. They like everything structured, they don’t like fluff, and I think humans, we like the same thing, right?
Because anyway, they were made after us. So… when we restructured, first of all, our website traffic bursted. Many things that we have done led to that, and we are now very much improving in the geo-search.
And also feel that humans started appreciating those blogs more and sharing them more, and even, you know, within our team, you know, like, sales team, they were never big on reading blogs, but now that they’re so structured and condensed, I see them relating to blogs all the time.

Carilu Dietrich:
Yeah, we had to get rid of a bunch of images, maybe, and, like, serve things up in a more QA format, but certainly the scannability and readability is, like, a great breakthrough.

Esther Katz:
Yeah, now I have TLDR. Yeah, I have, like, this is a real hack. TLDR and FAQs in every blog. It’s very easy to create them, and LLMs love them.

Carilu Dietrich:
You know what’s funny? Even before the LLMs, a couple years ago, one of my girlfriends is the head of comms for Google Cloud, and she was like, no one reads press releases anymore, like, we do a blog from the CEO, we don’t pay the wire, we do some social media, and she’s like, I use the blog… I use the press release as an FAQ about the news, instead of, like, being an old-style press release.
So I guess back to your point, like, the hacks that make things easier for humans to read and consume, in some ways, are really the hacks that make it easier for the agents to consume and index the information. So, yeah, I mean, it does seem like the whole point of what we’re trying to do, with AEO and GEO is good content, again. Good content delivered well.
We have a couple questions from the audience. We have one for Esther, why put Salesforce in the middle of cutting-edge operation agents and humans? It seems expensive, for simply being a system of record.

Esther Katz:
You have to compromise somewhere, you know?

Carilu Dietrich:
Yeah, I’m seeing companies continue to have Salesforce at the center of the agentic, mostly because it, like, is also so integrated into the sales processes. And I think that if we’re really talking about an agentic OS of the future, it’s gonna bridge marketing, sales, and support, and have to be some sort of integrated experience.

Esther Katz:
I have a huge sales team that uses Salesforce, have been trained on Salesforce. I mean, huge in our standards, like, 50% of our staff. So, you know, I had to… I have to keep them happy, we all have to keep sales happy, so that was my compromise.

Carilu Dietrich:
Another question… I think, Caddy, let’s start with you for this one, because, it’s about for CMOs of the larger companies. How did you determine the use cases? Did you do a proof of value before expanding to the rest of the company? And what about, let’s just start with those two. And I have some comments on that after you, so go ahead.

Kady Srinivasan:
Yeah, I think that’s a really good question about use cases. I think for us, a lot of it is… We are trying to thread the needle in a market that’s very complex and is very competitive, so we had to find.

Carilu Dietrich:
Can you also tell people about what Freshworks does, just the scale and size, just because you.

Kady Srinivasan:
Wait a minute.

Carilu Dietrich:
late. Usually, I’d be like, just, and so that everyone’s on the same page.

Kady Srinivasan:
Yeah, sorry, yes, thank you for that. Freshworks is a… we are almost a billion-dollar public company. We have about 5,000 or so employees. And we serve two different markets. One is, we call CX, which is our customer experience, side of the business, which is… think of it as, competing against the Sierra’s and the Decagons of the world, which is a hyper-competitive space.
And then, on the other side, we have ITSM, or IT Service Management, so we compete against ServiceNow, Atlassian, and a whole bunch of other people. So, you can imagine, we are a SaaS product competing in two different spaces, which are being disrupted by AI startups. And we have to be extremely clear and precise about who we are going after.
in terms of ICP, and the messaging that we communicate with them, and what we communicate as our value proposition. So our… if you kind of step back and say, first principles, thinking, what do we need to use AI for? That’s basically what it was. It… using… for us, using AI is a way of getting to be more precise and more resonant in the market based on everything that’s going on in the world.
Previously, it would have taken us, you know. 5 PMMs to be scanning the market for 2 months before we could get a sense of how things are happening, and now it… you get it in minutes, and then you can hopefully take some action, on it.
The second thing is, you know, I think of it as always trying to find what I call arbitrage opportunities, so in terms of where are my competitors not leaning in that I should be leaning into? Where are those pockets that I can go into? For instance, actually. we haven’t done it yet here, but in my previous company, we found newsletters, of all things, could be… was a crazy source of MQLs.
We literally 10x’d MQLs, just by newsletters alone. And so.

Carilu Dietrich:
Newsletter sponsorships, you’re saying?

Kady Srinivasan:
I knew that.

Carilu Dietrich:
Or any targeted sponsorships?

Kady Srinivasan:
Yeah, and the.

Carilu Dietrich:
AI space, I think, right?

Kady Srinivasan:
In the AI space, but it’s also, like, things like Morning Brew, and, like, things that have been around for a bit. And it’s, like, it’s, tying back to what I think Claire was talking about in terms of content as well. It just really helps you define and get more precise about the kind of content you’re generating for certain audiences.
So the use case there for us was how can we get find these arbitrage opportunities, how can we get a little bit better at a full funnel approach of nurturing a customer through the different parts of the journey, you know, that sort of stuff.
So, my suggestion has always been use first principles to figure out what kind of problem you’re solving, and then use AI to layer on top of that and help solve that problem, knowing that the velocity is going to be so much more, but also the noise is going to be so much more. like, driving 10x MQLs.
my sales team was super happy, but then they were like, oh, this is all junk, we can’t keep up, and now… then there’s a whole set of other conversations about, do we need to hire BDRs, or go find AI SDR, and do… and what are we going to do with that? Like, so…

Carilu Dietrich:
Or change the lead scoring! Now we have AI!

Kady Srinivasan:
Exactly, or I’m just going to… I have too many things on my calendar, so I’m going to pick the most important thing, and then you get into… speed to lead just tanked for us as we were generating all these MQLs. So, there’s, like, all of these things, and we… I mean, this group is so, smart and talented.
They’re talking about go-to-market architectures, and really taking that systems approach of thinking about one introduction or infusion of an AI thing here is going to create a lot of follow-on effects. Throughout the journey. And then… Let’s make sure we are controlling for those things.
So the proof of value is, honestly, right now, I’m trying to control it, control the governance, control the fragmentation, control this AI beast, and then I’ll start to expand it outside.

Carilu Dietrich:
Yeah, okay, so, I mean, I guess I, I want to leave, time for my one, like, lightning round, but I would say, for the AI-native companies that I’m advising, there’s, like, no budgets for AI, there’s, like. tons of investment money, it’s like, buy stuff and innovate and go fast.
And then for more traditional companies that I’m advising, the bigger they are, the more likely it is that there has to be a pilot that’s controlled to, like, test the security and the governance and make sure people really use it and start to show return on investment, because efficiency is still dominating for a lot of companies that are trying to, like, get through from being, maybe a SaaS company to an AI forward, or an AI next.
Company. So, just answering the question. Okay, I want to do two things. One, I have a friend, and she and I have a gratitude text back and forth to appreciate all the wonderful things, and make us appreciate them while they’re happening.
And so I would just like to appreciate these fantastically bright women in technology who are at the cutting edge, not only of marketing and their jobs, but also of AI. So, you guys have to smile for my gratitude text, really quickly. Okay, great.
And then, I want to do one last thing, like, a super lightning round, because we have 7 minutes, and everyone who’s in the next session is going to join this Zoom 3 minutes ahead of time. But, like, the coolest AI agent that you’ve, like, seen or want to build next or the one that you think people should, like, use to get started. You can do either one. So I’m gonna go first.
I think, actually, the one you should do to get started is competitive. Like, I just see so many people getting advantage of, like, quick, quick, scans of competitive and immediate results that they can take action.
And I think some of the coolest ones… are actually going to happen in the analytics space, where we’ve had so much data, but we can’t really see, like, what’s happening in unstructured data, not just in all the structured data that we’ve been querying in our normal ways. So, Esther, next, an easy one, or a cutting edge?

Esther Katz:
I have two, I can’t decide, so I’m just gonna give you two. So, the first one is the project manager. We still didn’t build it, because there is a problem with Slack, but what I wanted to do is to crawl all the chats in Slack.
And… pull up the requests, because they’re all over the place, and then bring those requests into our marketing dashboards, and then prioritize them and set them as tasks, so we don’t have to do that manually, and even think about it, and just get… look at our, like, project list. This is number one. And number two is a dashboard builder.
So, what we… what we are working on right now is Pulling up everything that you saw in the context graph. and then getting rid of all the unnecessary information, and then putting it into a very easy dashboard, so I never have to answer that Slack again, how many leads we got from that campaign. So that’s… that’s the tool. If you manage to build that dashboard before… before I do, let me know.
We can compare the notes.

Carilu Dietrich:
Awesome. Who wants to go next?

Kady Srinivasan:
Yeah, I can go. I… I still say… I mean, like, my thing is 11X. I know it’s not exactly the top of mind for a lot of people, but one of the things that’s really cool about it is it really tracks, like, real-time signals. If somebody is… company is expanding and they have rented a new space, it’ll tell you, so that you can go and pitch to them. So, I love that, that stuff.
But then, for everybody that’s building out there, please, let’s figure out a way to scrape LinkedIn, in a way.

Carilu Dietrich:
They’re gonna let us do it without kicking us off forever?

Kady Srinivasan:
Yes! Come on, somebody has to crack this code, he can’t sit behind that wall forever.

Carilu Dietrich:
Does LinkedIn sell all of that? Can we buy it? What you want to spray?

Kady Srinivasan:
They don’t do anything, and it’s like, literally, you have to apply humans, and they even throttle the amount of… number of humans.

Carilu Dietrich:
Throttle. They throttle the number of humans.

Esther Katz:
Try go- try goji berry.

Kady Srinivasan:
Okay, alright, I’ll try that on.

Carilu Dietrich:
That’s clear.

Alexandra London:
I think to add on, similar to what Esther said, as well as a few others so far, having kind of that daily landscape of just what to focus on, something that’s crawling your Gmail, Slack, everything across the board, this already exists today, it’s very easy to build in Cloud Cowork, and I think just step one, right? If you want to get familiar with it, I think that’s the most important.
It can send you a daily reminder of just where your focus is, meetings coming up, and things that you might have missed, I think we all get lost in Slack, so that would be one that I’d highly recommend for everyone. It’s kind of your personal assistant to start the day. And then I’d say the future is, how does everyone almost become their own analyst, or data engineer?
I think we are well past the times of having to wait for a ticket, or you’re seeing a drop in your data, how do you quickly react? you can now start to build that, but how do you build it at scale with very precise dashboarding?
I think there’s quick, easy hacks that you can build when it comes to some of these reports, but I’m excited for the next wave of how detailed they become and how they actually are building almost Looker, Tableau, whichever you’re using dashboards for you.

Carilu Dietrich:
Yeah, and I’m super excited about that, too. The CMO of HEX is in a number of CMO groups with us, Ashley Stepien, and, she’s like, there’s a future when you don’t have dashboards. You just go and ask the question, like, how are we, are we, like, getting close to our revenue number? Like, oh, there’s a problem? Tell me about that.
And you just talk to it, and it generates some of the, charts on the fly that you need in the context that you’re looking for, and that’s, like, a super exciting future for those of us who have been waiting on our marketing ops and analytics teams for an insight for days and weeks and months. I agree. Claire, last one, bring us home.

Claire Darling:
Yeah, I would say, because every company’s going to be different, I would… I would recommend for everybody listening to this is, like, go have your team brainstorm on what are the things that they spend all their time on, jobs to be done, and then they can go build, like, teammates, AI teammates, whether it’s through Cloud, ChatGPT.
I would say that’s… that’s a place to start if you haven’t started. Where I’m excited is… The changes around… how, like, our fellow sales leaders and sellers are doing, like, account research, right through to outbound.
That is all changing, and that… that is… that is getting really, much more efficient, much faster, and much more informed through AI, and I would say that’s… that’s really a really interesting place. And I’m just going to do a plug for… I… I love Qualified.
I am absolutely blown away with what… and Alex, you’re using at OneMind, and what they’re doing, and that whole autonomous agent, right through for the full funnel, going for… not just a… qualifier, but they’re actually going through to doing demos and selling. It’s… watch this space. That’s… that’s really exciting, what’s happening in that world. So those… those are the areas, I would say. Yeah.

Carilu Dietrich:
Wonderful. Julia, do you want us to have one more fun question, or, do you need to introduce the next panel?

Julia Nimchinski:
Let’s do it. Let’s have a final question.

Carilu Dietrich:
More fun question. If you were gonna, like. you know, one of the challenges is we all have to become these marketing generalists now. Become an analyst, design your own stuff, be the strategist. If you were gonna give advice to marketers who are building their careers. Where do you think they should invest their time in the world that we’re in right this second?
If they want to become a CMO, how about if they want to become a CMO, which part of the marketing stack should they, focus in on in this current AI world? Esther.

Esther Katz:
I don’t know what’s gonna happen to marketing and marketing stack. It’s developing too fast, but I know one thing. We’re humans selling to humans using bots to assist us. Bots, intelligent agents, we are promised AGI, I don’t believe in it, but let’s presume sometime it’s gonna happen. So, until it’s human making decisions.
or even delegating their decisions to AI, we would still have to appeal to that human element. So go and study psychology. At psychology, if you’re just, you know, in college, at psychology course, you need to understand how humans think and behave. Anything that has to do with behavior, including behavior analytics, is going to be super, super important, because you’re going to get tons of data.
you will have to prompt your AI to analyze it, you will have to know the principles of human behavior to get something useful out of it.

Carilu Dietrich:
Okay, Alex, you’re next. You can be controversial. Because the answer is, you can get there from any of these disciplines, but…

Alexandra London:
Hang on. Okay. I would say the data. You have to ground yourself in being able to understand all aspects of the data. Everything from understanding full finance components to the data layer that your team sits on, but I think that being able to pull insights from that and action upon it is so critical, and so I’d say heavily invest and don’t rely on others for the data.

Carilu Dietrich:
Caddy, what do you think?

Kady Srinivasan:
I think similar to what Esther said, I think it’s about, being able to understand a business and help influence or understand how to take the right kind of decisions. So maybe it comes down to psychology, maybe it’s a business degree, I don’t know what it… what exactly that would be, but, like, the ability to figure out what outcomes you want and help drive towards that outcome.

Carilu Dietrich:
Claire?

Claire Darling:
I agree with Alex, is data… data and analytics is going to be critical. I don’t think the roles that we see today are going to be there tomorrow. So I think, you know, how to build a system, how to kind of build out system of record, context. That’s going to be really important as well. So more of that go-to-market engineer I would be going for if I was going into college. Thankfully, I’m not.

Carilu Dietrich:
And not even college, right?
I mean, I think I had actually… it’s funny the way you guys took it, because I actually was thinking about mid-career, early career marketers that are on the path, but… Okay, so I’ll tell the last one, because controversially, I would say product marketing, because I think, like, data is going to get smarter, agents are gonna get smarter, and from what I’m seeing in AI, AI is still not a fantastic product marketer.
It can scrape the web, it can tell you about Like, what competitors are doing, but it doesn’t necessarily, like, write the best strategy for a new product in a new field that stands out and isn’t, like, averaging. But, and so I think… I think product marketing is one of the places they’re gonna be human in the loop.
And just to say one more quick second about it, when we think about roles collapsing, I think product marketers can now do the research, they can write the materials, and in an agentic world, they could, like, create, campaign assets, and run the assets, and have agents, check on the operation.
like, I guess back to what Catty said, strategy, to run a lot of the, all the different agents seems like one of the most important skills to build. So, I just want to thank everyone, and thank you, Julie, again, for having us.
It’s going to be a wild ride in 2026, so don’t sign long… we said don’t sign long contracts, you know, start on easy things, but build to a large agenda operating system, and as always, I think bat phone the smartest people you know, because people don’t always share all their secrets in the open public, but if you know people and can, hear others’ experiences, you can accelerate your own agentic success.

Julia Nimchinski:
Sensational panel. Thank you so much, Caroleou. Thank you, our panelists. And before you go, Caroleou, what’s the best way to support you? Your soft stack is one of the best in B2B.

Carilu Dietrich:
Oh, that’s so nice of you, thank you. Yeah, I run a substack at carriLoo.com, or you can follow me on LinkedIn, and I, share… I share a number of AI use cases in marketing, because I just can’t get enough.

Julia Nimchinski:
Thanks again. Thank you, everyone.

Esther Katz:
Good, man.

Claire Darling:
Thank you.