RX18 - ABM in 2026: From Account Investment Strategy to Autonomous Orchestration | David Potter, ForgeX

In this solo episode of the Revenue Xchange, host Davis breaks down what Account-Based GTM actually is in 2026 and why AI is maturing the ABM deployment models rather than breaking them.

Key Takeaways:

1.) ABM is an account investment strategy, not a personalization tactic. The real work is allocating budget, resources, and human capacity across a target account portfolio so the ROI justifies the spend.

2.) AI lifts the resource constraints, but judgment still matters. On high revenue potential accounts in Enterprise ABM (1:1 ABM and 1:Few ABM), human review stays in the loop and freed budget shifts toward executive engagement and field marketing.

3.) Growth ABM becomes autonomous orchestration. In 1:Many ABM, teams stop building static campaigns and start architecting an always-on machine that prioritizes accounts, enriches buying group contacts, and activates campaigns dynamically.

4.) Mature AI programs are built, not bought. The organizations pulling ahead staff a designated AI practice, favor hackathons over one-off workshops, and consolidate toward a single pane of glass.

For the full data behind this episode, read ForgeX's 2026 AI in ABM Benchmark Report (189 respondents) and join the June 30 webinar with lead author Eric Whitlake --> https://research-hub.forgex.ai/2026-ai-in-abm-benchmark-report

This episode is supported by Propensity, the only contact-level ABM platform with AI that automates personalized B2B campaigns, and by Folloze, AI can draft your ABM campaign in an hour. Folloze deploys it live, per account.

Transcript

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What's going on everyone? Welcome back to another Revenue Exchange. We're doing a solo pod. Today, I have two main topics that I really wanna chat through. They're incredibly important, very timely, and before we really get into it, if you haven't already, go check out our new 2026 AI and ABM Benchmark Report. Huge shout out to the Forge-X team, especially the lead author, Eric Wittlake, who, uh, produced an in- an incredible piece of research. It has everything that you need to deeply understand where your organization currently falls across your AI in your ABM programs. Check that out. We'll be dropping a webinar on June 30th as well to go very much through the report in detail, but then also answer all of your questions. Check those out, and let's get into it. So for today, there are two main topics I wanna talk about. One, which is not as obvious as you would think, what is ABM in 2026? There are so many conversations going on, LinkedIn, so many, uh, discussions across all of the events that we've been going to, from some of the large traditional analyst firms to some of the tech vendors, just across practitioners and go-to-market leaders in general. What is ABM? How is it evolving? Especially when we think about the resource constraints that are being of-uplifted with AI. We're gonna get into that. We're gonna talk through the deployment models. We're gonna talk through how AI is shifting ABM, and then we're gonna end it on something that ties into our benchmark research, but also is something very close to me because I've been personally in the weeds on this, having over 30 conversations with solo ABM practitioners, CMOs, multi-billion dollar organizations, to try and uncover what the most mature AI practices are doing inside of these companies. What does it look like? What does it feel like? How is it staffed? How are they making their investments? How are they thinking about their tooling? And most importantly, what does the enablement look like across those? We're gonna chat through it all. We're probably-- This is most likely going to be an hour-long episode, so rock with me here, and- First, before we get into it, today's episode is supported by Propensity, the only contact-level ABM platform with AI that automates personalized B2B campaigns. If your team is generating account lists but struggling to turn them into pipeline, Propensity closes that gap by activating buying group contacts inside your existing workflows. See how it works at propensity.com. We're actually deep in the weeds right now of building something unreleased. I'm not sure if it will be released by the time of this episode, so Sumner, forgive me on this, but they have this new component called Propensity AI. It's incredibly cool. Uh, just wait till you get your hands on it. We're going to be doing an AI build session, and you'll actually be able to build agents within the, the tool itself. So huge shout-out to Propensity. Additionally, today's episode is also supported by Follows. Follows, with their platform, AI can draft your AI campaign in an hour. Follows deploys it live per account. And just a side note here, we've been Follows users for over two years. When you go and you click on our actual reports, the pages that they go to are Follows board pages. They also dropped an MCP, and what we've been doing, more specifically the incredible Yael on our team, is through Claude, not only building custom landing pages that are, uh, able to deploy immediately, but we're also building things like ROI calculators, assessment tools. Y- whatever you think and whatever you want to create, you most likely can. So go check them out, follows.com. And with that- Let's, let's start talking about what ABM looks like in 2026. I was on with a rev ops leader earlier today, and she was asking me, "I've seen ABM across multiple organizations. I've worked with the ABM leaders to actually build out some of the measurement and reporting infrastructure. We've done the campaigns, and I still don't fully understand what ABM is." And it's so easy to think about account-based marketing in a tactical lens. ABM is just building something that's hyper-personalized for one specific account or a cluster of accounts, and that's all it is. You conduct some additional research, maybe you build some custom content for that account or subset of accounts, and you release it into the wild, and that's it. Which is incredibly, uh, misleading when you think about and talk and look at the, the top organizations in market. And so I just want to set the record straight on what ABM is and how you should be thinking about it. Now, account-based marketing is essentially an account investment strategy. When you think about the account, it is essentially the atomic unit of your revenue-generating efforts. You are working to get accounts to close. They are the entities that are in-- that are paying the contract. And inside of the accounts, that's where you have the whole notion of there could be multiple business units. In those business units, you can have specific buying groups. There could be constellations or crossovers of buying groups across all of those business units. And across those buying groups, you have individual people. Those people have different perceptions of you. They have different roles. They have different responsibilities. They have different things that they're looking at throughout the buying process. And so it is so incredibly complex, especially for those who have large enterprise deals. But what I really wanna focus in on is you are ultimately going and selling to these accounts So the account at the end of the day is y- the way in which you think about that is what's the revenue potential that I could possibly gain from closing this account, from retaining this account, from growing this account through expansion efforts? Actually looking at, okay, I have in my back pocket for my account investment strategy, I have three things that I can leverage. These three things are your capital investment allocations. So you have your budget, you have your resources. So what do you already have existing? Maybe it's agency contracts or tech. What is already in your tool stack? And then you have your human capacity, or my great friend Josie, she came up with this great analogy of calories. Human capacity is basically just calories spent. So you have budget, resources, and calories or human capacity. How do you strategically allocate that? And those three being your capital allocation, how do you allocate that towards a specific subset of accounts? Where is your company's investment going to yield the highest ROI? And how do you build that target portfolio of accounts and invest strategically? And what I mean by that is, for example, when you think about these ABM deployment models that we've been talking about for the past three years, you look at enterprise ABM, which is comprised of one-to-one and one-to-few. You have growth ABM, which is our subset of one-to-many in a modernized fashion, using a tiered approach to cover hundreds or thousands of accounts. And when you think about the way in which they're structured, all it is at the end of the day is determining what your capital allocation strategy is going to be, what your in- account investment strategy is going to be. How much budget, resources, and capacity or calories am I going to be putting into these accounts? Because if I do so, the revenue potential, or even strategic potential in some cases, is going to yield the ROI that justifies the investment. So, for example, if you have one person, we're, we're gonna go a, a very in-depth scenario here. Let's say you have one person who's gonna cover one account. You have an ABM practitioner. Let's say they're making two hundred K a year. Let's say their budget, j- just making this up entirely, is five hundred K So you have $700,000 that you're investing into this campaign and that you're investing into the calories in order to curate, build a strategy, design it, execute it. And so if you are not realizing a strong ROI multiple on that investment in, oh, one-to-one ABM in one account, again, th- this is a pretty extreme scenario, that account's revenue potential must be pretty damn high in order to actually execute. That's why we say for one-to-one, you wanna have a threshold of at least a million dollars, and you're probably going to be covering more than one account, especially with AI. And so essentially, when thinking about ABM, it's the way that you deploy your capital investments across the accounts in order to yield the ROI that justifies that investment. That's all that ABM is. You're investing in accounts, and you're working to yield the ROI. And oh, by the way, you're not looking at 30,000 or 50,000 accounts and trying to take this very finite or scarce amount of capital investment that you were given. You only have so much of it, so if you're gonna try to spread it across 50,000 accounts to generate MQLs, you're not going to be as efficient. There's gonna be some waste across it. And without that efficiency and without that concentration, think about what would happen if you took your marketing and your sales and your customer success efforts and you said, "These are the accounts that we are going to pool, and we are going to focus our investments on." That's how you yield the ROI. That's what ABM is at the end of the day. That's why you have all of these demand generation and integrated campaigns teams that are converting into growth ABM or one-to-many, that form of, of a tiered approach to covering hundreds or thousands of accounts. That is ABM. At the end of the day, you are not merely just building some personalization or building some custom content and pushing it out to a few accounts or, or merely taking a list of accounts and running ads toward th- towards them. That is not a holistic account investment strategy. And so I, I really wanted to touch on that. And when we get into AI, how is AI impacting the deployment models? We had a great event in Chicago this past week, and someone asked, and I was so excited that they did, their very question was, "How is AI democratizing ABM?" And when we think about it- The, the word democratization is merely getting into how were, how is AI, how is it lifting the resource constraints that were historically bound to some ABM deployment models? And we're gonna take a, take an extreme here of one-to-one ABM, because what you are probably seeing out in market is conversations around things that historically were only leveraged for one-to-one ABM because of that strong or high capital investment, which are now actually democratized with AI. And there is truth to that, to that. And a few examples of this are research with Claude or Perplexity or even ChatGPT. Perplexity is, is one of the best at this right now. You can conduct very, very granular and in-depth research around a specific account, people inside of those accounts. We're seeing organizations build either these account research agents internally. There are agencies that have spun up really, really great tools behind this. You also have ABM platforms who are integrating this into their existing platform, and not only that, but you're layering in all of the signal data that these platforms have as well. So it's getting very, very rich in terms of what research can be conducted, and the time in order to get that research is compressed. And that is the same thing that rings true with different tactics when thinking about building custom content, messaging, when building, uh, or even doing one-to-one direct mail. There are companies like Wildcard, GTM, HyperGTM, Sendoso, ReachDesk that are all building very strong AI recommendations to build one-to-one direct mail campaigns or at least deeply personalized ones. And so when thinking about this, the question goes to, all right, if I'm able to do things that historically took a lot of time and they took additional budget, think about how much you were paying for an agency to go out and conduct really in-depth research on a specific account and the people in those accounts. Oh, and by the way, you were probably paying that agency or a different agency to go out and take that research and curate options of direct mail, build the messaging houses. And if AI is compressing the time to be able to do that and compressing the cost, and you could do it at scale, what the hell happens to one-to-one ABM? What even is one-to-one ABM at that point? And what are these deployment models? Are they entirely cracking? Are they breaking? Well, we have had a lot of conversations on this, and my point of view, which is very strong at this point, is that no, the deployment models are not breaking, but the way in which they are operating, it is maturing, and it is starting to shift, and we're g- we'll, we'll talk about how, how it's shifting. So when thinking about enterprise ABM, one-to-one and one-to-few, those accounts, the reason why you even had that practice in the first place is that those accounts had the revenue potential Or maybe you had a, a very small TAM, or maybe the account had s- revenue potential, but also it was a really great logo and a very strategic importance for your company. Those are, those are some of the pieces of the criteria that would go into it, and, and I would really lean on revenue potential, obviously. But when thinking about enterprise ABM, you're one-to-one and one-to-few. With that revenue potential, yes, you could leverage AI, but you should be thinking about the risk that if you just take your AIS... Do you really want, on a $100 million account, do you really want an AISDR going out and prospecting some of the executives or some of the people inside of that account? Probably not. Does your designated sales team that your company is still investing in, maybe that one, two, or a full team, just to prospect, grow, and retain an existing client, do you really want that team to be leveraging AI and just spewing AI slop across all of those people? Probably not. So when that revenue potential is there, and when we think about enterprise ABM, one-to-one, one-to-few, you have to think about the risk that is associated with AI slop. So hu- calories or human capacity, the judgment component, has to be layered on. You can't just entirely automate it, so that is where focus is going to be placed. And also, when we're thinking about this concept of the account investment strategy, those accounts, because the ROI is there, warrant more investment. So yes, AI can go in and conduct deep research on all of these accounts, and it can also come up with examples of one-to-one direct mail to send to the executives. But for the accounts where it warrants it, this is where you're going to be spending more dollars. So maybe those gifts are going to be more expensive, or you're going to do more gifts. Field marketing is so critically important here as well because for those accounts, it... The expense to actually go and make the investment in a in-account event or VIP dinners for a cluster of those accounts, the spend is there. So there are still calories being spent from a human capacity standpoint, and you're expending more of them on these accounts. And also, when it comes to the investment, the budget allocation, and existing resources, you're spending more on these accounts as well. So what AI is doing is it's compressing the speed and it's compressing the cost for some elements, but how do you take that budget and reallocate it into other areas? And we're also seeing areas like executive engagement. Top performing companies, we found this in our benchmark report, they're using personalized landing pages, direct mail, and executive engagement at such a higher rate than all other organizations or average or lower performers. And so how do you think about that for one-to-one and one-to-few? And where it gets really interesting is in one-to-many, or what we call growth ABM This, this has the, the most radical change. What we're seeing is companies are building this autonomous orchestration machine engine, and what they're doing is instead of having multiple people who were going in and actually building these static campaigns that are running, they're working inside of the tools or the AI-augmented tools, and they're building the system. They're building the machine. So when you think about one-to-one ABM and the personalization at scale through ads, landing pages, uh, other digital tactics, even other e- uh, tactics that have a little bit more scale because you're covering hundreds or thousands of accounts, the AI component is the automation. It's the machine. It's the intelligence. How do we conduct research at scale? How do we inform these, uh, our sales team? How do we build a stronger revenue engine? And what this looks like in actual practice, go back and listen to the podcast where I had, uh, Justin Lopez from Bonterra. It's two podcasts back. It is-- He is building this machine. He has built the machine, actually, and me and him, we, we had a conversation, and we actually went in-depth on the, on the tangible results that it was producing, and it's producing. I can't share 'cause I'm under NDA, but it's producing. And so when you actually go through and think about one-to-many, that's where you're building that machine that's always on. It's dynamic. And what this-- Uh, we'll, we'll walk through Justin's because I wanna make this come to life. I, I don't wanna be like the analyst firms who are just using these buzzwords. I've seen a couple keynotes at some of these events, and it's like they just flash these freaking buzzwords, and I, I'm just sitting there, and I'm like, "Okay, why don't you, why don't you tell me or why don't you show me what's actually happening?" So we'll walk through it. All right. Let me pull up a visual. We won't be able to put this up on the screen, but rock with me here. So I, I wanna go through the real tangible example we have, and this is, quote, unquote, AI autopilot. Go back and listen to Justin's podcast. I'm gonna walk you through how he's doing it. It's a 1,300-person company, so it's not some small, scrappy startup that has two people on the marketing team. This is actually, uh, you know, something that was implemented in a sizable, smaller enterprise organization. So he has his ABM platform. Think Demandbase. Think Propensity. Think 6sense. That ABM platform is prioritizing the accounts based off of buying signals. It then goes into Clay. Clay is populating and enriching contacts who are most likely to be part of the buying group directly into the CRM. Think Dynamics, Salesforce, whatever you have for your CRM. And also for Clay, you can leverage these tools, think propensity, think ZoomInfo, think demand base. You know, the, uh, what you're working to do is have something that is going to enrich even Claude. We've seen Claude work there as well. Then goes into the CRM. From the CRM, there is a component of, uh, autonomous orchestration here as well, where it's then getting pushed out, all of those contacts who are part of the buying group, it's getting pushed out into an ads platform, and this ads platform, think propensity, think Influ2. These, this ads platform is then pushing ads directly to those contacts. Contact level is really critical here. What Justin was seeing was an over sixty percent match rate on those contacts. Every time someone would click on one of the ads that took them to a custom landing page, it-- the sales team would then get notified and had a, a less than twenty-four hour SLA to go and follow up. And so after that, the next layer here is they were using a tool that was building custom landing pages for each one of these individual accounts, uploading it autonomously into Salesforce, writing personalized marketing emails from Marketo for each contact, personalized emails for SalesLoft, for sales outreach. And so this whole, this whole machine or AI autopilot, growth ABM, one to many on autopilot, this is running autonomously I, l-let me just say that one more time. This is running autonomously. And so what it's freeing up, for example, Justin, but we've also seen this at a multi, multi-billion dollar organization who's working to build this as well, what we're seeing is you take this machine, this autonomous orchestration machine, and you are effectively working on building the machine. You are dialing in the personalization on all the custom landing pages that it's building, dialing in the personalization on the emails, on the custom ads, on the content that it's personalizing, the experience for each individual person. And so instead of merely building these static campaigns, you're building the autonomous machine that's making dynamic campaigns and thinking about the levels of investment that are being placed into each one of these accounts based off of what tier they fall in, uh, uh, tier they fall under. When you're thinking about ABM in 2026 and ABM for the 2030s, essentially what you're doing is you need to be thinking not in terms of, "How do I build these personalized tactics?" That, that ship sailed. You need to be thinking about, "How do I strategically allocate my capital investments across this unified account investment strategy? And how do I build my target account portfolio of different accounts that fall into each of the ABM deployment models so that I'm efficiently generating a, and yielding a high ROI based off of how much budget, resources, and human capacity or calories that I'm allocating towards each account?" That is ABM, and AI is, yes, reducing the resource constraints. So think about enterprise ABM one-to-one, one-to-few. Think about growth ABM, one-to-many, tiered. And in that growth ABM, the biggest shift here is instead of building static campaigns, you are now building the machine, and you're leveraging AI to do it. It's autonomous orchestration. That's, that's where we are. And it's not BS, it's not woo woo, it's not something that's coming three to five years from now. Yes, you need incredible data. Yes, it is s- it is new, but you need to start thinking in this way because this is the now. And if you don't, and you, you're not trying or at least building a strategy to create this, you're going to fall behind flat out. That this is what we're seeing in the bleeding edge organizations. They're all doing this. They're all thinking about how do I do this? These are the, these are the conversations that we're having with the C-suite or with the VPs who are thinking, "Hey, this is great. How do I do it?" And then conversely, I'm gonna layer on one more piece on this. They're also thinking about, "All right. Here is my entire tech stack. I have all of these pieces that I need in order to build this autonomous orchestration machine. Which ones can I eliminate with Claude or ChatGPT or my AI tools?" The tech consolidation and the thoughts and the conversations behind it is so incredibly real Which is a fair warning to all of our, uh, uh, uh, all of those who are deep in the platform world. Those are the conversations we're having. Next, I wanna talk about something that I'm very incredibly passionate about. I have not had as much fun getting in the weeds around something... Th- this lights me up in a way that ABM did three years ago. We've been so in the weeds on ABM, but this is so... It's new, it's fresh, and everyone's trying to figure it out, and it is just... I am so pumped up around how these companies are building their AI practices or not building their AI practices, what it looks like, what more mature organizations are doing, the ones that are yielding success. I've had conversations, I've had, I've had the privilege of having conversations with people inside of all of those organizations. It... All of the headlines that you see of the companies that are either really going in on AI, I've had conversations with those, and then conversely, with people who are at the organizations who are just not. And so I wanna talk about specifically the things that the more mature organizations are doing. I don't wanna get into the less mature because I don't think we need to. What you all need to know is where the puck is going, and we're gonna skate to it. So first off, let's talk a little bit about a designated AI practice. More mature organizations have a designated AI practice. If you don't have one in your company, in the next 24 to 36 months, you will, and this AI practice needs a designated leader. You cannot merely take your VP of product, who is already stressed out, or your CTO, who is already trying to build and turn your company and become more AI native in and of itself, and ask them to push on your internal AI strategy and design it as well, to get your teams adopting it and think about the tools. You can't. It's not going to end well. We've seen this happen so many times. And then also, in other organizations, you have fragmented pockets of people who are spending late nights on the weekdays. They're spending their weekends learning and diving into all of these tools, and they are the de facto AI experts inside of the company. And what's happening is you have all of the other people who aren't doing that for a plethora of reasons. It, it... We could talk, we will talk about what good looks like in that regard. But they're all looking at these people who have been spending all that time in AI, and they're just throwing all of these asks and requests onto their already full plates, and it's just breaking. What you need inside of your organization, and this is specifically for you, C-suite, and, uh, VCs or board members, P- uh, uh, financial backers who are listening to this You need in your organization that designated AI practice. You need to hire a designated AI lead for that practice, where the sole thing that they are focused on is building out AI and enabling your teams internally. You don't wanna l-- It's not a, a, a halvesie role. You don't give it to someone and ask them to do something else. You, the companies who are far more ch- mature in this, that is what they have. So first off, staffing, you need a designated AI practice lead. Now, what we're seeing in terms of staffing, like let's, let's actually break this down. So you have your designated AI practice lead. What you additionally need is you need some, uh, you need some people who deeply understand how to build inside of AI, whether they have an engineering background or they're a go-to-market engineer, forward deployed AI expert, whatever the hell you wanna call them. They actually understand how to build the workflows, build the agents, and build inside of your tools and other tools. So you need the builders. You need the lead, you need the builders, and then across your cross-functional teams, you also need to have either specific people who are actually in a AI expert role or something along the lines of a AI evangelist, someone who is given additional training. Part of their work is actually spent on learning and building and enabling team members on AI, and they're almost functional AI evangelists, so to speak. So that's, that's what the staffing looks like. Now- Depending on who's listening to this, we have a-- There are so many levels of AI expertise, and when you're enabling your internal teams or trying to get them to adopt AI, even in the most forward-thinking and the most advanced companies, they are still struggling with this. It's not like you walk into XYZ hyper AI native company, and everybody is just building, uh, uh, multiple agents in Claude Code, and they're just completely crushing it, and they've entirely built their role around AI. This is more so in the larger enterprise. Specifically, that's, that's where we have our-- the mo-majority of our conversations, the larger enterprise, you know, or just merely enterprise companies. So when thinking about enablement and training, you have to bring everybody up to speed. Not everyone has to be a total AI expert, but they need to be dangerous enough and have role-based skills. So some of the things that are not working that we continuously hear is even for the companies that have a designated AI practice, what they're doing is two things that are great in spirit but just not quite moving the needle enough. One is they'll have some form of monthly or biweekly show and tell where someone goes on and highlights a use case that they used AI for, something that they built, which is awesome to have But also what they're doing is they're having these workshops, maybe it's one hour, maybe it's two hours, and essentially in these workshops, they're going in, they're showing them role-based specific training on how to build inside of the context of their AI tools. But the problem with that, and the problem with that show and tell is your-- some of your team members, they are so busy in the actual execution of their day-to-day tasks that they're going to these, they love it. They, they're coming out of it going, "Wow, this was really cool. This was incredibly helpful. I'd love to be able to use this tool for that." But then they go right back into the fire drill that they were working on previously, and it goes right over their head, and they don't have the time to go in and actually re-watch the videos, the show and tell videos, the workshop videos, and it just sits on your internal shelf, and that's it. Now, the things that are actually moving the needle are hackathons or giving space for your team members to actually learn the tools. Give them training, but then give them space. Give them a task or set them free on a use case that they identified. Let them go and have the time, the designated time to do this. That is how the learning is actually codifying in these different team members, and that's what you need to be implementing inside of your organization. The hackathons work really well. It a- it's also great for building camaraderie amongst your team and, and really getting people excited about AI. When you have a full day hackathon or a half day hackathon and everybody is in these different teams. We're even seeing the, the VP and above level do this in some really large organizations, and it's so cool seeing their eyes light up about the things that they built. And so this isn't just merely something that your specialists or your managers or your directors or your VPs or your C-suite are doing. It's, it's across the board. Even the ones who are more traditionally, let's say, ten p- plus past years were just really deeply focused on leadership and managing these large complex teams, they are now getting in the weeds, and they're building with AI, and they are so psyched about it. So those hackathons are incredible. You should be thinking about them too. So for enablement and training, I just wanna throw out all the things that these more mature companies are doing so that you could write these down or you can have some concept around what, what are the activities that these designated AI practices are doing for enablement. One is it's the hackathons, it's the AI showcase or that show and tell. They have those biweekly, monthly, sometimes even weekly. They have office hours that are designated and dedicated just so you can go get your questions asked. They have role-based specific training, and they have certifications. We're even seeing some companies build role-based certifications, as in 101, 102, 103, specifically for AI. And so enablement and training, I spent a lot of time here because it's so important. You could buy the Ferrari, but if you don't know how to drive the Ferrari, it's just gonna go... You have a really expensive car that's sitting in your garage. And it's the same thing with AI. You could go take-- You could buy a thousand plus Claude licenses, throw it over at your team, buy some Perplexity licenses, buy some ChatGPT licenses, buy whatever you want, buy some Lovable, some Replit licenses, and if you're just chucking them over the wall and saying, "Hey, everyone, go figure this out. You have AI now. Why aren't you using it?" It's not gonna work. So enablement and training is so incredibly important. Another concept that I wanna talk about with this enablement and training goes feedback and experimentation loops. This is, this is actually a challenge as well inside of the more mature organizations. This is the feedback we've been hearing, as in they're rolling out all of these different tools, but the question then goes, you pass it over to the users, and then they're-- they'll try something for a little. Eh, maybe this agent, eh, maybe this workflow, it's not, it's not quite great. And that knowledge just lives inside of their head, and they don't share it back with the designated AI practice. So a-as an AI practice leader, you need to be extraordinarily diligent about collecting that feedback so you can then take it and either trash some of the experiments that just didn't work, put them on hold maybe for a later date, or refine and improve on what you already have in terms of tooling and in terms of what you built. So feedback and experimentation, you need to keep that loop flowing. Another component here is your tooling and your tech stack. The biggest thing that these companies are deeply focused on is consolidation. They are looking at this massive tech stack that they have, and they're thinking about what can I consolidate or what can AI actually come in and augment or entirely replace? How do I do it? And this is also something that a lot of the tech companies are deeply focused on as well. With the pace of AI, I can now build a lot of things inside of my XYZ platform that I couldn't historically build So I'm going to go take all of these features, or I'm going to build all of these new things inside of my platform, and I'm going to make it so that I can tell my customers they can cut all 10 of these different platforms that they're spending hundreds of thousands of dollars, if not millions of dollars on. They can go cut those and you just buy ours. That's where it's going. The next 24 to 36 months are going to be very fun as we're, as we-- We're gonna get to watch what happens in real time around these. And by the way, for those companies as well, a, a lot of them, the conversations we have is it's, it's, this is, "We are a, we are in wartime. This is w- we're, we're going in the war room." I don't love that analogy, but they, they use those words, and I don't think it's wartime right now. I think this is survival. You have 24 to 36 months. I'm gonna use that specific time window. You have 24 to 36 months to be the AI winner in your category, or you are toast, and some of these companies are not making moves quick enough. They are just not. But conversely, there are some that are making moves, and it is going to be really interesting what the market share looks like as well. We've had a lot of conversations. I... There are some things unreleased that I can't speak on. I'm also under NDA for a, a lot of either products that some companies have made or whatnot, but all I can do is share. Well, we will be sharing, so watch out for any of the posts from FourJacks. But keep an eye out on the market, because it's, it's going to get very interesting Another piece that I wanna chat on in these AI practices, two areas, these are more obvious. One, your AI strategy. The designated AI s- practice should be building the strategy, and that strategy is your roadmap. How are you implementing AI? How are you prioritizing your use cases across the different functional teams? That's what they should be deeply focused on as well. What we found in our AI benchmark report, again, go take a read to, to get the full depth on this, is that having a AI roadmap is one of the most important things that the top performers were doing, and that really set the bar between top performers and all others. So you have to have that roadmap. Use case prioritization again. These AI practices are also building the workflows and the agents, of course And they're also focused on governance. Those are some of the more obvious pieces But the last one, which is really something newer, and there are a couple organizations that have implemented this, and I've seen it. They have shown me what they're doing, or you-- it's actually, uh, published online kind of sneakily, actually. You, y-you might ac-- you might not be able to get all the details behind this, but I'm going to share what we are seeing because this is the future. If you have heard the concept of headless, Salesforce has spoken about this. You have Follows who has spoken about this. Propensity is who has spoken about this. You have other companies who are starting to think, "How do I build a headless application, or how do I get headless?" And what headless is, is essentially you are going to be working in a single pane of glass. And some of the more mature organizations are already doing this. So you have either a Glean or Claude, for example, and through MCP or API, you're hooking in all of these additional applications, and instead of having your window, uh, ten different windows open with all your SaaS tools, you're operating out of, let's say, Claude, for example. We are doing this at ForgeX, and it's borderline magic. I'll give you two examples that we're doing this at ForgeX with. One is we run our weekly pipeline meetings entirely out of Claude. We have it hooked up to our HubSpot, and through that, I use WhisperFlow for any of the sales calls that I'm on, any of the sales calls or opportunities that our team is on. What they do is they go into Claude, and I'll share how I do it. I use WhisperFlow, which is a speech-to-text tool, push down a designated key that is the command key or the turn-on key. I speak, uh, we, you know, got XYZ update on all of these deals, that this is closed won, this is closed lost, this is deferred. Here is the amount for it. Go into my calendar through MCP and pull all of the contacts from XYZ last meeting, associate them to the opportunity, and then it goes out, and it does an incredible job of it. Then in our weekly pipeline meetings, it goes through all of the open opportunities. It has all of the different numbers that you are most likely looking at in your weekly or monthly, bi-weekly pipeline reviews or whatnot. And we as a team scan through every single opportunity actually in Claude versus going into HubSpot, building these custom reports, pulling the numbers ourselves, putting it into a slide deck. It-- All of that time spent is now completely compressed. Same with time spent for me going in and making any updates. So that's one, one example, and another example is building custom landing pages. We Uh, and even, even building our website as well. Custom landing pages, for example, through Claude MCP, all we do is we have a skill in there and, uh, we are-- or multiple skills. We're gonna need to get Yael on this because she has built some very interesting tooling inside of Claude. But essentially, we are just prompting Claude to build the custom landing page, and then it just pushes it out. So instead of going into an actual application to do so, we're doing it all inside of one single pane of glass, one UI, which is Claude, and the time that it's compressed. And also, for those who have used these AI tools, you can ask them to do just about anything. I was on with a, with a, a director of marketing just yesterday, multi-billion dollar company, and she is using Claude day in and day out from a ops perspective, which I thought was really fascinating. We do the same at ForgeX. And so having these, these headless UI or these headless pieces that connect through MCP or API into your single pane, that's the future. That's where all of these companies are going. That's where your company is ultimately going to go. The most mature are making that transition now or building the strategy to get there. And so when thinking about this from your position, you should be considering: How am I going to adapt the way in which I work? So instead of going through many different applications, I'm just going to go into my Claude. There's a great quote from one of, one of my great friends, Kara Alcamo, who's the founder and CEO of Alcamo Marketing, and this is just going back to what we were talking about in the beginning around one-to-many or growth ABM, the tiered approach to one-to-many. Instead of being the machine, you are architecting the machine, and that is exactly what you are doing as you're building these always-on autonomously orchestrated machines. That is how you are now working on actually architecting that personalization at scale. All of the tactics are now being conducted or completed Using that machine that you have architected and you are strategically apple- allocating your capital based off of them. That is a lot to think about. Thank you so much for hanging with us for the past 45 minutes. This has been a really great and fun episode to record. There's a lot going on here. Make sure to go check out our AI and ABM benchmark report. You will get so much more in-depth data behind what is actually happening this year in these, the leading organizations' ABM programs. We had about 189 qualified responses to the survey, so go and benchmark where your company is. And additionally, we have our webinar again on June 30th. Go register. Eric is going to be breaking down all the findings from the report that he authored. It's gonna be very interesting. Huge shout-out to Propensity and Follows for making this podcast openly available. We will catch you on the next webinar, and thank you so much for hanging out with us