The Agentic Demand Blueprint: From Marketing Automation to Agentic Demand

Our topic for today's presentation is this concept of building an agentic demand blueprint for your B2B go‑to‑market organization. We want to really talk about the future, where we're headed in terms of best-in-class agentic demand engine. But we also want to talk about where we've come from, the current state of marketing automation, and really what this hybrid transitional state looks like. And so that will be the the goal of our conversation here. Our focus is really on that 2.0 state of agentic demand and building agentic demand engine. We're going to first take a step back and talk a little bit about how do we get here? Talk a little bit about marketing automation roots. What has changed? What hasn't? And then we're going to talk about where we go next. And particularly, as I said before, what this transitional hybrid state looks like over the next year, year and a half, 2 years. So I think the starting place is really marketing automation 1.0. And for me, I feel like that really hearkens back to earlier in my career. Before founding Anuitous, I was actually the B2B demand generation marketing automation evangelist Silverpop. And so as I was putting this presentation together, I took a look back at what were we talking about in 1.0 marketing automation? What were we promising? And what's ironic is that we were promising exactly what we're talking about today. This idea that marketing automation could deliver dialogue. That that was the key to nurturing. We also talked a lot about this idea that nurturing was a non-linear type of operation. So marketing automation 1.0 absolutely had this vision of demand dialogue. But here are the challenges. In particular, and many Anuitous clients know, you know, we've built some really sophisticated demand engines and engines over the years. But the challenge is that it was always really a highly manual build to get to perpetual. And you know, think about Visio-ing all the steps, mapping those out, building all those programs in Marketo and Eloqua and HubSpot, and then mapping together all these engagement channels, right? The the work that we have done has always been multi-channel, not just email, but email plus web, bringing in paid, other. But a lot of complex steps to integrate all those channels from a mapping standpoint and also from the standpoint of keeping track of all the parameters. Also, with some of these channels, real-time API'd into the system. But in some cases, actually not. And all these complex batch uploads, the governance of that data, of that tracking, etc. And so let's talk about the fact that you know, we really hit a wall with 1.0 marketing automation. Some of the challenges we couldn't overcome. Those manual automation builds that we talked about also meant constant adjustments. Optimization in the engine was literally going in and manually making changes. So let's say you learned that different content performed differently at different stages of the buying journey. And once you had that live, you had the proof of that, to then go on and make that change, sometimes would be a significant lift. Also, I mentioned this before, but when we talk about a lot of the engagement channels and other pieces of the mix, we frequently have this batch integration problem. What I mean by that is that you might have a field event that's a critical part of the quote engine, but actually is not fully integrated. And so somebody would go out, do an event, there'd be a list upload after the fact. And so one, you have this delay. Two, you have this data that's coming in in a non-ideal way. And three, oftentimes you had gaps in the information that was collected. It was very fragmented. And so that's where you saw engagement channel activity and data very much in silos in a lot of our demand engines today. Also, you know, our efforts to qualify were very much using forms. And for our clients, we'd always try and do progressive forms. But we still found that they were a highly inefficient way to profile and to qualify. You know, we'd tune the heck out of the quality, but there were a lot of gaps from a qualification standpoint that we'd see. Also, all of this, particularly the paradigm of producing a lead, handing that to an SDR, that SDR or BDR or LDR, whatever you call them, having to then turn around and set a meeting, brief the seller. There were just too many gaps and breaks in that process, and it was not ideal end-to-end. And so you often had a state where you know, there were so many chances that that early stage engagement, which might be really significant and strong with the buyer, couldn't actually make its way to a strong seller engagement simply because of the demand process that was involved. We in fact often had sales disconnected, but we also had this double-down effect, which is that sellers always acted disconnected, even as we would try and do all the change management and training and get everything aligned and get the SDRs and sellers and marketing working together, there was still this sense that sales was like, "Oh, well, you know, whatever marketing is doing, that's a separate thing." Disclaimed some of the knowledge about the state of the leads. And it was just a change management that I don't think we ever fully overcame. And often led to sales turning to their own cold outreach tools, tools like SalesLoft, like Outreach, to go out there. So a lot of walls in the 1.0 version of a demand engine powered by marketing automation. Perhaps the worst part of this was the CAC issue. Is that all of these elements really meant that the cost of acquisition and the steps it took to turn initial impressions and interactions into pipeline often were very costly. And a CAC issue for demand programs is in fact a CFO issue. And we see that, you know, on the right is an example of an Anuitous client, an actual Anuitous client. And for our clients, we've always done incredibly well and had 4 to 10X improvement in our demand engines. But unfortunately, for a lot of the marketplace, their model looked like the left. And so when I say that a CAC problem, it was because they would spend a lot of money at the top and very little that would convert, this extreme martini glass. And so the cost of acquisition, the cost of demand programs often times would be equal to or exceed the actual outcome that they were delivering on the other end. That CAC issue was a real problem for how CFOs saw a lot of 1.0 marketing automation powered demand engines. Amid all of this, we were trying to make some improvements in the marketplace. And you know, our marketing automation demand engines started to see competition. ABM as a concept, ABM programs, ABM platforms started to really be an alternative parallel. And sometimes they were integrated, sometimes they were not. The demand engines were not meant to be account-based, but there were definitely some alternative approaches that were out there. Intent data represented an alternative approach that often for sellers work around an in-run around demand programs. You know, "Hey, I've got intent on these accounts, I'm going directly." And then finally, we mentioned sellers doing cold outreach, sales cadence platforms. So I want to just frame up where we've been with marketing automation, with building demand engines, building perpetual demand over the last decade, and certainly, you know, history for Anuitous. A lot of great wins over that time period. But we've got to really acknowledge some of those challenges, the wall, and you know, this competition from alternative approaches.

Good news, obviously, is with AI coming into the mix, there's a huge opportunity. But I think there's also an important step to provide some context around what type of AI we're talking about and really differentiating generative from agentic. Generative AI, obviously, was first on the scene and really does have a lot of applications to improve demand. Certainly helps us as marketers, as demand leaders, rapidly analyze large sets of data, rapidly get to better suggestions about segmentation and approaches, to rapidly synthesize talking points which can help with developing strategic plans, which can help with developing content. All of this represents a productivity boost and that's great. But I want to position that I think the real game-changer that we're seeing and that really plugs into this concept of agentic demand and building a 2.0 agentic demand engine and a native agentic demand engine is that it's really agentic AI that is the game-changer from an AI standpoint. And so let me address why. When you think about the fundamental challenges in B2B demand, in particular, these upper funnel gaps, these, you know, higher order pain points, lack of content, lack of programs that are out there, there's definitely a lot of periods of interaction with buyers that a lot of demand programs don't cover. Second piece of that is even when we're covering it, we often have a lack of orchestration across channels, across stakeholders. We might be engaging with one stakeholder in account, but not another one. We might be engaging in one channel in one way, differently in another, and we're not tying all that together. Our content has always been one-way and by that I mean, here's a content offer, here's information on a topic, here's product information. But it wasn't really two-way, it wasn't dialogue. We talked about this idea of siloed programs and data, and we also talked about this idea of overall process disconnects. And so, when you think about these fundamental challenges, these are not fundamental challenges that generative really changes. These are fundamental challenges that agentic fundamentally changes. And that's why AI is more than just a productivity boost. It enables us in our demand engine to move to an always-on state. Enables us to better orchestrate across stakeholders, across the account, across channels. Enables us to get to do two-way dialogue. Um enables us to start to process multiple signals from multiple. So, not just form data, not just intent data, um not just web activity and behavior data, but all of it in a composite view and optimizing what is the best combination that we're looking for. Um it enables us to connect sellers directly to prospects, and this is important. Um instead of having this rickety process where you're going through all these steps, in fact, we want to think about one of the objectives of the agentic demand engine is to create uh seller connects, stakeholder connects, right? Bringing the two together on key accounts. Um either by booking a meeting, creating a direct chat line, number of different ways to do that, but not a lead that then someone else has to look at, get up to speed on, email out, call out, try to set a meeting, you know, go through all these steps, you get the point. We can connect sellers and stakeholders more directly together. And then finally, all of this is more scalable, more rapidly deployable. That manual build, that manual process that we've been stuck with in the past to get the most out of market automation is rapidly fading to the side. The other thing I want to mention is that when we talk of it about agentic, we're not talking about just one agent. So, you take for example Qualified, which is a Forrester leader in the space right now. They've noted that they're a leader in the space. Um the Qualified guys, their Piper um brain, right? It's not just the brain for chat. And I think that's one of the things, too, that we've got to disconnect is that agentic in demand doesn't necessarily equal chat. There can be multiple different agentic brains and agents that are running in real time. So, looking at and feeding different segments, looking at and offering differential experiences for key accounts, overseeing qualification, overseeing data cleanliness, data governance, overseeing tracking parameters, making sure things are coming in directly, dealing with ETL transformation to support your CDP. There's a lot of different elements that your agentic brains can be doing. So, when you think about a demand engine that is an agentic demand engine, you want to think about it's a collection of these agents, these sub-agents in different categories. And we want to disconnect that, as I said, it's not just about chat because we want to think, you know, think about like key accounts. You want to be able to make sure that if someone comes to your website and chats, they get a differentiated experience. But what if they're on your website? How do you personalize and enable them to prompt browse? What about nurture emails you're sending them? What about invites to specific events? What about follow-up SDR emails? And then also this idea of, and I think this is an important piece, the ability to open up the channel directly with your sales team over team chat. And so, you think about every single one of these agents on the left could engage with multiple different engagement channels on the right. And that's why we want to think about agentic demand and agentic AI through a lens where we disconnect it from the engagement channel. Last thing I want to mention is that we're definitely seeing a moment in time where agentic AI is not a homogeneous thing. You see platforms like Qualified that are native agentic, and then you see platforms like Marketo that are really rapidly working to upgrade, but actually are largely leveraging generative AI today. And so, a lot of the things they're doing, there's still lots of automation in the mix, but the steps are not agentic steps, they're generative steps. And then you look at platforms like HubSpot, which are kind of a hybrid of the two and that are heavily agentic, but also their breeze capabilities leverage generative as well. So, it's also not a homogeneous thing when we talk about agentic demand platforms.

So with all that background on you know where we've come from in 1.0 market automationdriven demand engine when we talk about bringing AI into the mix let's talk about our approach to building a native agentic demand engine. What does that blueprint look like? The starting point for us at Annuitas and our lens and we've been at this you know for more than a decade is really four key points. We're always looking for our demand engines to be buyer centric, orchestrating and engaging across the account, across stakeholders, but putting them at the center of the demand process. Building an engine that is optimizable, not a collection of tactics and random acts and quote campaigns. Making sure that we see these buyer journeys in a 360 degree way and that we're bringing the data science, the visualization to bear. And finally that every interaction, every dollar in the top of the funnel, every point of engagement is quantifiable and we can see what is the key mix so that we can optimize that engine. So this lens is really important as we look at agentic and think about an agentic demand engine because it's the same fundamentals that we've been at now for a decade plus. So what's largely the same in an agentic demand engine from a charter standpoint, right? Number one, we're trying to orchestrate, you know, buying unit stakeholder engagement for target accounts. That hasn't changed. And we're trying to drive predictable lift to pipelines and revenue. That hasn't changed. But what has changed and what I call some evolved paradigms are a couple of things. Number one, this idea that um we're moving to a real two-way demand dialogue, not impressions. And that's a substantial change here. Again, for the longest time, we've had and tried to build an interaction where you nurture and you offer content, you progressively profile to find out more, and you try to make that two-way, but that's not really a dialogue. In an agentic demand engine, we're talking about those agents, those brains actually driving to a dialogue, chopping up and using content in different ways. Sometimes formulating a synthesizing response, sometimes pointing to a content piece. Um, but that's very different than our classic impression. The second piece of it is that there's the true potential for progressive qualification. I will say that none of the platforms are where they need to be yet on this. Um, I think platforms like One Mind and Qualified are certainly coming along and leading out there in terms of their ability to progressively profile and qualify. And I think that we'll get there. But in all of this, we're rapidly moving beyond the static form state and being able to have more of an interrogative interaction with prospects. Next piece of this, and I mentioned earlier, is this idea that, you know, it's not about generating a lead anymore. It's really about creating these stakeholder connects. And by that I mean substantial moments where it's the right place, right time, and the seller armed with the right information is able to make that connection. We're also starting to look at a variety of different demand signals across channels, across points of interaction, not merely anonymous intent data, and obviously not merely classic form data that was coming in. Um, and then finally, because we're starting to look in this multi-touch state, we're starting to look in this more orchestrated state, we can finally start to have a conversation about an attribution portfolio. So, whenever we look at CAC, whenever we look at closed ones, we're not looking at a singular lead source, but we're actually looking at a true multi-touch, not, you know, a classic attributed, but like something that actually makes sense and shows all the points of interaction and the inputs. Because really what we're trying to do is optimize that portfolio. So let's translate this to an agentic demand engine. The first thing that I would say is you know obviously you could drop in marketing automation here and a lot of these flows would look very similar. So so what's so different here right? One of them is that first and foremost we're not just talking about a chat agent. We're talking about Agentic intermediating your website experience, delivering differentiated experiences for different segments and actually eventually enabling you to literally prompt browse. Um, I was really impressed one of the players in the space, One Mind, uh, if you go to their website and engage with Mindy, their agent, when you go to their website, there's no website like you're just interacting with the agent. the agent is allow you to prompt browse the information about the company. And so it's a great example of where we're starting to head with that experience. Um you also obviously see a number of different classic pieces that are out here in terms of fulfillment emails like that was a marketing automation thing. Nurture emails absolutely but what's different here is that you're setting the stage for a lot of these emails to be two-way and that's a big change. And then finally um you know couple of other points here. you've got uh progressive qualification armed with the questions that you want to qualify. We're increasingly starting to enable the agent to then nonlinearly build a picture of you, right? And so that's a very different interaction. Last piece in in this overall view is this idea that and I mentioned earlier, we're not generating leads off of this, we're generating connects and those connects are either direct live chat handoffs. So like if you have a real key account, your enterprise seller is ready to go, they're online, that's showing in Teams and Slack, that their presence is there, we can actually connect to them directly. And that's a big change overall. But if they're not at that point, then we can go in and we can uh book a meeting and we can brief that prospect. And I think that's a big change as well. And so you see the connect that we're making. That's a live interaction. If we overlay some of those evolved paradigms we talked about, we see a number of things that are starting to play out through here. One is this idea of real demand dialogue that is multi-channel that is two-way that is across inbound and outbound that we are not doing lead and funnel management anymore. We're doing progressive qualification and you see that piece coming in through here that we're creating those connects, right? Not handing off leads that we're aggregating all of these demand signals, bringing those into CRM, making those available both for real-time qualification, but also for sellers to be able to see um and also to get briefed on and be able to dig further into. And that all of this changes the nature of our reporting and our analysis. And we've been all moving towards the CDP concept for the last couple of years. But Agentic enables CDP to look completely different to flow in a much more robust CDP view and uh to also allow agents to operate inside the CDP cloud to actually improve ETL to improve the parameters that you've brought into the mix etc. Let's do a little bit of a comparison again back to what's the same, what's evolved when we talk about how we get to agentic demand. A couple of things here. Number one, from a nudity standpoint, we've always talked about conversation track architecture. And so this idea of understanding your buying stages, having an approach to segmentation, having content segment stage by stage, that hasn't changed as you're planning. And in fact, I want to stress that in an agentic world, your planning is so much more important because that's what we're going to be using to train your agent. So getting this right is really important. But the nature of your conversation track architecture is what's going to change. You're going to see a much more behavioral, ethnographic, less titlebased, more behavioral u journey, right? making sure people who have different patterns of needs, different pain points are getting different information. Um, thinking about your content differently. Um, not making a bunch of downloadable white papers and webinars, but starting to think about a way for that to be more of a dialogue substrate. Not every answer is going to be a content offer link. So, enabling the engine with content a number of different ways. And by the way, I think all of this starts to blur the lines between product marketing, lowerfunnel content, and demand content marketing, upperfunnel content. Um, second thing is, you know, you would have seen the words lead management or funnel management in here in the past and we are still and, you know, do care about some funnel stages. We want to be able to manage the process end to end. We absolutely still care about ICP, but there's some fundamental things that are changing. not lead management anymore, right? Not leads. We're driving to connects. We're going to start to see the process language change. And anus, we're actually working on some new definitions of what an agentic demand process looks like and how that changes. Um, we're going to see criteria become more dynamic and have the agent be able to address it and pattern it and fill it out in different ways. And that's where criteria could be completed both by dialogue and also by data append. We also see the state where it's not about leads, it's not about accounts, it's about both, right? It's about stakeholders on key accounts and making that change strategically. Um the last piece of this is that obviously we still care very much about the demand experience. In fact, arguably the demand experience is more important in an agentic paradigm. We want to see the um uh nature of the interaction, the the value back to the buyer, the support and and stewardship of their journey to be higher. We want demand experience to go up. So we absolutely want to address all of their stages. We want to blend and bring online and offline, inbound and outbound together. But some things are going to fundamentally change. Like for example, fundamentally when you hit the website, whenever you respond to a demand email, it's going to become two-way, which means that it's going to start to become what I call a promptbased journey. your prompts, your interrogatives are what's going to actually drive the next piece. And so, we're going to start to get to more of a dialogue based state in nurture and in profiling. Um, we're going to start to expect that we actually are able to get outcomes with sellers sooner, faster. And I think that that's an important piece and will make sellers more productive. The last piece of this is that, and this is, I think, sort of for me still mind-blowing, this idea that we're going to think about optimizing our demand experience, not only to be browsed by humans, to be nurtured by humans, but also to be browsed by AI agents and make sure that those that are looking for solutions can also get the answers they're looking for. So, we see these elements coming together. conversation track architecture, agent qualification model, domain experience in a demand engine in a very just different way than than in the past where all of this composits to really define a couple things. one agentic demand use cases flows and then ultimately the translation of this is to then training the agent for these use cases for these flows for these situations instead of a static experience static journey static qualification that we're setting the stage for a dynamic model and training to that we see that when we talk about agentic demand engine development right program development man sort of classic again we've been doing this for 10 plus years classic process we go through is blueprinting implementation optimization um couple things are you know the same couple things have changed right we talked about earlier some of these building blocks are very much still in there um but one of the key things that's changing is that the output of a lot of these phases is in fact training documentation training logic and in optimization phases it's all about improving training We also see that some of the dynamics of the timelines change frankly a robust demand engine blueprint phase strategy phase that's not going to change and that's actually more important in this model but we do see that the time to build that is going to go down. We're going to see a faster delivery as we're training not building so much but we actually see a lot more time and energy put into optimization phase. And so that long tale of constantly tuning this is going to really be a um substantial change and represent substantial change management for marketing for demand for field marketing professionals. They're very much used to driving activities and certainly, you know, intaking on how they performed. But this idea of less executional, more optimizational is really a change in terms of the cadence of how they do a lot of their

So, we've talked about where we've come from in terms of 1.0 demand engine powered by marketing automation. We've also talked about introduction of AI and we've talked about what we're headed towards in terms of a true native agentic demand engine, but we're really in this transitional period. We need to really be talking about what we call hybrid models and layering. In the meantime, what do we mean by hybrid? We're in a hybrid state because we're still very much seeing marketing automation 1.0 marketing automation technology which is evolving by the way being in play and organizations still very much have their marquetto their hubspots in place. However, they are depending on them less to think about building perpetual flows. Um and they're starting to look at Agentic and saying hey how could Agentic be doing those perpetual flows, right? And we're so we're thinking about and using our existing market automation differently, using it more for funnel management, using it more for tracking, using it more for broad mass communication kind of initiatives, but starting to lean into Agentic for those personalized for those dynamic flows. And so what we're starting to see and certainly from an annuity standpoint seeing clients really build what are hybrid uh market automation and agentic demand engines. And we see that being the case really for the next 12 to 18 months, but the technology is obviously advancing so quickly and we're seeing from some of the agentic marketing platform providers like Qualified that that are out there um that the state of that is is evolving so rapidly that we do think later on this year into late 26 and early 2027 that we'll start to actually see more complete set of capabilities and more typically actually building your agentic demand engine from scratch based on an agentic marketing platform and maybe leveraging that classic market automation less or not at all in a year or two. So one of the other things to know about the current hybrid state that we're in today as I mentioned is some of these agentic marketing platforms have a ways to go. We obviously still see a heavy 1.0 marketing automation installed base. We also see from the agentic marketing platform providers that they're not quite two-way in all their channels yet. They're largely very two-way in chat, but they're largely still developing capabilities in some of the email channels. So, they're not quite there yet. Also, when we talk about agentic qualification, while there are a number of different ways to implement an agentic qualification model in an agentic marketing platform, we're seeing that the brains are not really able to fully progressively profile yet. So, there's still some manual build in the logic there. We're also seeing some telemetry issues and this is one of those areas where market automation is still very much delivering a lot of value. What I mean by that is when you have an a demand engine, you want to see structured telemetry off of it. You want to know buying stage and qualification state and last content piece andor last dialogue point somebody interacted with at every stage so that when you're tuning whenever you're re revising the training that you can see what were the things that were working not. Well, the problem with a lot of Agentic marketing platform that are out there today is that they're not really throwing off that structured telemetry. And so the data that's coming back through the API to be reported on isn't really helpful. In fact, arguably it's less robust than what we saw from 1.0 mark animation. And so we've got a ways to go. The last piece of this is that we're just starting to move from the concept of this agentic brain plus chat to a state that is agentic brain plus multi- channel and I talked earlier about the idea of thinking about that brain as a multi- aent entity and then separated abstract it from the multiple channels and that evol evolution is something that's just happening right now. Amid all of this, one of the key things we're working on with our clients in advising is to really approach things through what we call a layering strategy. And what I mean by that is crawl, walk, run. The run state is absolutely an aentic demand engine. And you should be moving towards that as the vision. But there's definitely a crawl and a walk. The crawl for everybody is to be leveraging and advancing the state, moving away from classic chat and sort of older chat providers to true agentic dialogue and conversational. That is a a strong crawl stage. But the walk, which is really where we need to be focusing, is how do we get beyond agentic chat to actually starting to leverage several other capabilities that are mainstream today from the agentic marketing platform. And they fall into four categories. one leveraging and billing on an SDR agent. So let's say you do still have that stream of MQLs coming through having the Aentic technology follow up, reach out, try to schedule a meeting with those people. Another one that we've seen some really good success with some of our larger enterprise clients is leveraging agentic demand technology to be able to drive pre-event invitations. One of the things that sellers and marketers really struggle with is reaching out before a trade show to try to book meetings. Well, the crazy thing is you typically know who was there at the last, you know, for the last couple years at that trade show. So, you've got that data and you typically do know some of the types of companies that are going to be attending. You do have a number of different vectors even though you don't have the actual attendee list. And so, you can anticipate and reach out. Feeding that into an agentic marketing platform enables you to go do some anticipatory um invites and scheduling. And we've actually seen really high rates of meetings booked this way whenever it's used in this way. Obviously, it's an arbitrage moment of you have all this information, you just don't have anybody to go work through all of it and correlate it. Um, two other areas that start to move up the value chain here and increase in maturity, starting use agentic for more progressive profiling. is starting to build your progressive logic into the training of that on-site agent um and into some of the email channels to start to to do a little bit more progressive and then and this is a key area we're seeing again too with some of our enterprise clients is leveraging Agentic to offer a differentiated account-based experience both on the website and then whenever you leave in terms of email follow-up nurture SDR outreach etc even within the same platform even within the same interaction defining different segments, different accounts, you can deliver different experiences on the way to a full agentic demand engine and past that crawl state of having your on-site agent up and running. This walk phase of layering agentic is a really critical way to start to get more sophistication, get more out of your agentic marketing platform, but also build some more maturity on the way to building a full engine. Hopefully all of this has laid out for you some strong POV on how to move closer to blueprinting and evolving your agentic demand engine. And obviously, you know, if you need help with that at anus, we're happy to chat with you more about it. A couple closing points to think about that I think are really important to just reiterate. The first one here is we're talking about a new execution architecture, but we are talking about the same B2B demand challenges. That has not changed. And if anybody's telling you otherwise that AI somehow has changed everything, they should have lived through 1999 2000 whenever people told you that and the internet were going to change everything, right? This is stuff that happens over and over again in the tech space. It's the same challenges. It's just a new execution architecture but a very different architecture and with very different as I said evolved paradigms. Second thing agentic demand technology is becoming market automation 2.0 and so the best way to think about it is to start to disconnect from this idea of it as chat and to really start to think about it in this way as the Marquetto Aloqua HubSpot 2.0 platform. We absolutely have to take a new approach to demand experience and content in this new execution architecture. We've got to better support dialogue and make sure our content our on-site experience is there. I think there's some great opportunities particularly getting to a prompt browse kind of environment. So, I think that we could do some really cool things to change the experience entirely, but we have to really think differently. It's the same buying journey, but we have to think differently about that content we're developing to support it. We also are going to start to see a new process language things like connects in there and other concepts. We need to start to update and move away from this inquiry, MQL, SQL mindset that I think is actually really holding us back at this point. Um, couple other key points. I think all of this is going to deliver a better multi- channelannel, multistakeholder and marketing and sales orchestration. So I think that the outcome here is going to be a substantially improved demand experience and I think it'll be a better sync for the various activities that marketing and sales teams have going on across channels and I think that's good for brands at the end of the day. Finally, this is not something you need to go straight to, you know, full agentic demand engine right away. Um although it's a critical vision and you should be moving there in the next 12 to 18 months. And by the way, you know, if you want to talk about strategy to how to get there, let's talk. But, you know, really think about your crawl crawl walk run approach and think about how you can for that walk phase identify some layering that'll help get you closer to the run outcome that you're looking for. All right, folks. Thank you again for your time. My name is Adam Needles. I'm the CEO of Anuas. I appreciate you guys spending time to go through this. If you've gotten all the way to the end here, love to talk more about where you're headed with your agentic demand engine and priorities for your organization.