Name: Revenue Enablement Trends for 2026: AI & Acceleration for Revenue Teams
Uploaded: 2026-01-28T18:32:22.289Z
Duration: 1 h 16 s
Description: Revenue Enablement Trends for 2026: AI & Acceleration for Revenue Teams

Transcript for "Revenue Enablement Trends for 2026: AI & Acceleration for Revenue Teams": Awesome. Good morning, good afternoon, everyone, and thank you for joining today's webinar. Really excited to talk about, you know, just revenue trends going into this year. How are we thinking about things both from an enablement standpoint, revenue, go to market, and and all things just in this crazy world? So my name is Melanie Follay here, cofounder and CEO of Spuck It. I spend all my time really obsessing over how can we make our reps as efficient as possible, how can we accelerate their learning, how can we make go to market teams really move as fast as possible, especially as we navigate through change. And so today, I'm excited to share a little bit of the research that I've been doing in the market, share some of the insights that we've been hearing from con from customer conversations, and, hopefully, you'll walk away with some great takeaways. So with that, gonna jump right in. So let's talk a little bit about the state of the market. Right? It feels like right now, the entire conversation, whether you're on LinkedIn or X, is just, you know, how fast things are changing with AI. But I think it's important to recognize that, you know, while right now it feels like it's really important, the last years have slowly been getting us there. Right? Whether it was, you know, really the the first entrance of generative AI really hitting the market in 2022 with, OpenAI launching ChatGPT, more so on the consumer side. We've slowly seen more and more companies, whether it's Salesforce, AgentForce, even, you know, Google that felt like they relate to the game, really, take a front seat in the conversation. And it's really moved from, hey. We should think about this, you know, in 2023 to, you know, last year, almost every corporate executive board was, hey. This really needs to be a strategic, company priority, both in terms of how it shows up in your own products and services to deliver more, you know, value to your customers, but then also how can you continue to grow with more efficiency by potentially identifying parts of your internal processes that you can automate. Now my first, impression of AI, just given a little bit of background, was actually in 2017. I was working in San Francisco, which many consider the kind of hub of technology, and I attended my first conference, that really centered on AI. And it was just fascinating. And at the time, you know, there was a a few different there was a few different, sessions, but the one that I was most fascinated by was how, basically, they were training models early on. They were showcasing the example of training models for self driving cars and, basically, how, you know, they would take pictures all day long of every single, you know, corner that the the car was on and and street view that it was on and basically run it through the models to annotate, you know, this is a stop sign or this is a red light, and then send it, to a team across the world to do then human annotation and mark whether it was correct or not, send it back, etcetera. And so, you know, while right now it feels like things are, you know, really at the forefront, everyone's trying to figure out the strategy, you know, artificial intelligence itself is not new. In fact, the keynote speaker of that conference was Gary Kasparov, who is a chess master really known for having beat, IBM Blue, the first kind of chess machine where a machine beat, a chess player. And so with that, you know, while it feels like things are really intense right now, you know, it has taken quite a bit of time to to get here. And while I think it's really important that we go into the near really thinking thoughtfully about what that looks like, I do think it's also important to recognize that, you know, not everything you see on LinkedIn and Twitter is a representation of of how companies are actually operating. Now with that, you know, while companies have been investing a lot in AI over the last few years, the growth results, aren't necessarily following. Right? So Gong actually just, launched their impact of revenue report over the last few weeks, and I read it yesterday. They served 3,900 companies. So 3,900 companies is a pretty decent sample size. And on average, growth actually decelerated last year to 16% average across their company portfolio. They also saw a decrease in the amount of reps hitting quota down to 46%. Now similarly, Salesforce does their annual state of sales report. And in 2022, the stat that they found was that reps spent approximately 28% of their time selling. Now they did it again in 2025 and found that reps still only spend 30% of their time selling, which I think is important to recognize because while we think about all the investment that companies have made in AI, right, whether it's internal technologies, whether it's horizontal tools, which we'll talk about, we're still not necessarily seeing that impact. And so when we look at, you know, just over the last few years that have shifted from kind of experimentation to then really starting to make investments, right, buying technologies in 2025 to now realizing like, hey. This is the way forward. We really need to be identifying how we now operate as an organization, how the functions change across the business. That's definitely a conversation we're having a lot internally. What we haven't seen is rep performance and impact grow at that same pace. Right? As we just saw, we've only seen a 2% difference in how much time reps are spending selling. We'd love to see that leap, bounce forward despite that increase in spend and and despite the impact that all of this change has had on the organizations as a whole. And so today, we're really gonna focus on this gap. Right? How do we close this gap? What are some of the trends that are, unfolding in the marketplace? And, you know, of course, if you're a SPEC IT customer, how are we thinking about it from a technology standpoint as well at the end of the presentation? So with that, today, I'm gonna talk about four trends that I'm kind of seeing both in customer conversations as well as in the market. So one, I think we're actually gonna see more of a shift back to buy versus build. Two, we're gonna talk about the importance of, go to market knowledge graphs and the importance of documentation becoming a core part of your revenue infrastructure in order to automate things. Three, we're gonna really talk about this emergence of the content reasoning layer, and I'll be unfolding a little bit what that looks like. Then finally, four, we're really gonna talk about content creation as the window into context. Now all of these ultimately tie back to your rep performance, to your revenue, and growth, and I'm excited to dive in. If you have any questions during the webinar, feel free to jump in and into the chat. We've got a few folks monitoring that can, stop me. So with that, let's talk about the first trend, which is really the shift back to buy instead of builds. But it's not just, buy anything. It's really the curse the the case for vertical AI, so very domain specific AI and really the idea that it must be both open, meaning, like, your data must be accessible to whatever other tools that, you want it to work with. It must be deeply interoperable, and it must be AI first. And so when we think about the last year, companies have invested a ton. Right? And so on day one, it starts with good intentions. Hey. You know, whether it's someone in IT or on your rev ops team or go to market engineer, maybe some from marketing team that decides to go experiment with a new tool. Right? Hopefully, you guys have had some budget to do that, whether that's ChatGPT. It starts with good intention like, hey. Can we start on paying this? And then pretty quickly, you start building out an agent, and then you start changing the context. And then little by little, you start accumulating, well, there's these use cases and these edge cases. And all of a sudden, you start realizing that it's it's actually not that simple. There's a lot of additional work around that. And before you know it, you've really created this massive accumulation of productivity tax on both the team involved with building the solution that was, you know, rightly so ambitious versus, you know, the folks that have been involved in the process to help do that. So let's break down kind of, like, where are we seeing some of these pains, and what am I what am I hearing from customers? Well, the first one is if we think about the possibility of kind of, like, these horizontal AI solution, the the first one is like, hey. In an ideal world, you're able to just hook up, you know, your Slack and your Google Drive or SharePoint and, you know, all of your customer data in Salesforce to, you know, Copilot, Glean, Gemini, your tool of choice. But the reality is connecting those tools is actually quite easy. Right? OAuth in. Boom. The hard part is actually making sure that the answer is the one, right which we're gonna dive into in a little bit more. Right? Actually, trying to make sense of all of that data, trying to figure out which one of those is the right answer is really, really tough. And we're hearing that continuously from customers, which is without the understanding of the context, without the the the knowing whether that answer is correct, you end up with a grammatically correct answer, but not necessarily the right one. So we'll talk a little bit about that in the next trend. The other one is really that maintenance that I talked on touched on. Right? The idea of being able to create, you know, custom GPTs or custom gems and custom agents and and building that yourself is really exciting. For those of you who don't know, I come from a RevOps background. I was a Salesforce admin. I loved tinkering. I loved building out automation, and I also learned pretty quickly that there's a reason why the Salesforce AppExchange has 5,000 apps. Right? Turns out building everything from scratch is a lot harder than you realize. Right? If you've been in a RevOps seat, if you've got if you worked with RevOps before, you'll learn that pretty quickly. And so what ends up really happening is, yes. Can you build that first agent? Great. You've uploaded a specific document, and then now you've created a few more agents. And below before you know it, you're now managing prompts across a ton of different agents. You're now managing different contexts. Meaning, if you uploaded your sales playbook to feed into that agent, but now you make a change. Now you need to reupload that document. Or maybe you launch a new product. Now you need to go update all your individual agents to make sure that they're feeding off the right context. Then you're also realizing once you put it in the hands of your users, like, hey. We didn't account for this edge case. Hey. I'm not getting the right answer. Why is that? Oh, shoot. We don't have the observability. We don't have the telemetry. Oh, crap. We didn't actually put in place the test that we needed to to to validate that. Right? Having worked with our own engineering team, our own data science and AI team over the last two years building our own AI capabilities, I can tell you is that from a vendor's perspective focused a 100% on this problem, it is very, very complex. And so I think companies are just underestimating the burden and the lift and the cost of actual maintenance of all these individual agents and tools across the workflow, especially when it comes to really carefully designing them. The third one is really the change enablement. Right? At Secut, we talk a lot about change management. I think an organization's ability to drive change is the ultimate competitive advantage in today's reality. Right, how fast you can actually push change and get your good market teams to absorb it and then actually bring it to market. And so the possibility is that it's great. You can really quickly iterate and experiment. Right? The reality, though, is that all of this continuous change creates an immense amount of training and rework and ret fatigue. Hey, guys. Can we test this real quick? Just made a change. Hey. Can we get some feedback real fast? Hey. Let's post in Slack. Let's post in Teams. There's a new change. There's a new agent. There's a new thing. Right? And so all of this task tax, while important and valuable, creates a lot of tax if it's not right. And especially if these first versions going back up here aren't actually carefully thought through, tested, and validated to make sure that the the solution is actually not just delivering on the current value but sustainable. And then last but not least, the possibility is that you can have AI as part of every workflow. You can have it in every tool, and that sounds really exciting. The challenge is that now every single chatbot that you have is trained off of different datasets, has access to different datasets, is curated differently, and it becomes really, really difficult to understand, like, hey. We've launched all this stuff, but, like, what's actually making a difference? How do we actually measure the impact that any of this is having on the day to day without that central source of truth? And so I think this is what a lot of companies are are coming to recognize as they really think now more thoughtfully after, you know, the last couple years of experimenting, testing, buying a couple different tools that, hey. We really need to think thoughtfully around process design, what we choose to automate, what we choose to own. Because if we wanna be innovating on our own core products and services, we probably shouldn't try and reinvent the wheel when it comes to building, you know, search rag and basic agents. And so that's why I think, ultimately, what we are gonna see is that most companies are gonna have that kind of general purpose AI tool choice. I call that kind of the horizontal solution, whether that's, you know, TragicPeak, Glean, Gemini, Copoly, Claude, you name it. There's gonna be a kind of, like, core platform. The value of those platforms is that they can, like, integrate with all of your different tools. You can build some agents, and there's gonna be a process of identifying, like, where is that helpful versus not, etcetera, etcetera. But then I do think what you're gonna end up seeing still you know, SaaS is not dead. I think at the end of the day, you're still gonna see domain specific, vertical specific solutions, right, like in go to market, where these solutions are really automating, thinking through that end to end workflow, thinking through all of those edge cases for you, thinking through all the use cases, thinking through the right user experience, really doing all that fine tuning of the different AI models and orchestration to get to the right output. And the level of skill and expertise there is just not something that companies will be able to learn overnight. Right? Yes. You might have the ramps of the world that are kind of, like, in their unique bucket, but the average customer is still gonna recognize, like, hey. The opportunity cost and actually the just the cost internally of having to hire all the folks and manage that internally just doesn't outweigh the cost of Mercedes products. And so I do think vertically integrated AI solutions will win. And as we think about it from a go to market standpoint, right, and this could be I'm I'm using here, but, really, this could be any solution, right, be it any conversation intelligence tool, etcetera. As we think about it for go to market, right, the first one is having just bit ready to use agents or quick actions. Right? So these are agents where the workflow is already automated. It's already talking to the right tools that you want. It's already fetching the right data from those tools, not all the data. So in go to market, right, you can think about that, for audit automated content personalization. Right? Hey. We wanna create this business case for this deal. It's already pulling the right calls from, you know, whatever call intelligence tool you're using. It's only pulling out the right information. It's already pulling from Salesforce, but it's only pulling the right information from Salesforce, etcetera, etcetera, so that in a click of a button, you can quickly create really tailored custom content. Another example would be, you know, really use space use case specific context orchestration. So as we think about you know, a key topic I'm hearing a lot of customers think about is like, hey. How do we really start, predicting what reps need? How do we really proactively coach them on what to do next or how to prepare for their calls? And so all of that, again, requires really use case specific context orchestration. And so in go to market, that's really like, hey. Based off of everything we know about both, you know, what's happening in the deal as well as, like, what the rep's performance is, their knowledge, etcetera, how can we surface the best possible coaching for them in that given moment or that next best action. Another example is specifically making sure that it's got, you know, the analytics that you need to understand what's working and what's not. Right? I think, you know, a lot of these kind of general purpose solutions stop at, hey. Here's how many searches you got, or here's what people are asking. What you lose, though, because they tend to point you back to the sources of of data for you to go to, like, hey. Yes. ChatTubity or whatever it is can pull up this slide deck in, in ChatTubity, let's say, from, from Google Slides, but then you actually have to go to Google Slides, to take action for the most part. You don't actually know, hey. Was it shared with the customer? Was it not? Did the buyer look at that content? Did they not? Did it impact whether the deal was closed won or not? To really understand how that ultimately impacts your revenue. And so I think, you know, both from a measurement standpoint, context orchestration, and then having these built out agents, you're gonna see more and more companies going and really carefully analyzing what's in their tech stack, which one of these kind of fit that vertical use case or AI first in nature, and really go back to to buying after realizing kind of the extent that going from zero to one is possible yourself, but going from one to a 100 is really where you start hitting that limit. So with that, the second trend we're gonna talk about is go to market knowledge graphs. Right? Everything we just talked about really is predicated on the idea that you need to have the right information in order for you know, whether it's your humans on your team or AI to be able to take action. Right? You can't train an agent or a model on something that you don't understand yourself. And the challenge is that when you think about most go to market teams, that information is scattered all over the place. Right? You've got parts of your sales process document and decks. You've got parts of your motions and PDFs over here. You've got lots of conversations happening in Slack. And so at the end of the day, the challenge with AI is that it's not gonna fix that problem. In fact, it scales it because what it what most LLMs are trained to do is to find an answer. Not the right answer, but an answer. And so they're gonna go look for all this information and pull whatever they can find. And to the user, it's gonna seem like a grammatically correct answer. It's gonna seem well written. It's gonna seem cohesive and comprehensive, but there's no way of knowing whether that information itself is actually true. And so, ultimately, I really believe that this is what's gonna separate the best companies from the ones that just quickly kind of throw AI here here and there is getting to faster answers does not lead to better outcomes. Right? LLMs can make the right answer, excuse me, can make the wrong answer look right. And so it's really important that you've got the mechanisms, the governance, and ultimately, the underlying foundation to ensure that you've got that source of truth that's feeding these agents both in the platform of choice and across their entire AI experience. Now I think it's important to kind of break down why this, why this happens. Right? And I wanna give a couple of quick examples. Right? When AI is fed, like, every single doc in Slack, it's really hard for it to make sense on its own of what is the truth. Right? Let's think about it for a second. Let's say that you, let's say that you, your team is working on new content. Let's say that the PMM team someone on the PMM team just created a new draft of your demo deck, and it happens to be in the Team Drive that everybody can access. Right? It's technically more recent than the last one, but it's not ready for use. Reps haven't been trained on it yet. Do we want that to be the first response that surfaces to the reps? Right? And what matters? Is it recency of the content, or is it the, you know, authority of the content? Right? Should we value something that's created by the CMO over the PMM on the team? Right? So there's all these different nuances to think about, and I think that's really where that signal to noise ratio really gets messy. Right? Similarly, sure. That's great. Let's connect to all of our calls to see if we can get answers. But the question is, like, just because one of your reps said something on a call, is it true? Right? I've got a lot of reps that are amazing sellers, and, frankly, I actually think that they're they're very authentic in in in terms of what they sell, but people make mistakes. Right? And that shows up in your data, in your calls, in different places. Just because something is said or something exists does not mean that it's true. And I think the difference between something existing versus something being true is something that's really, really important. And as we look at these different, you know, AI systems, they're really just trying to figure out, like, what is the most likely to be right versus what is determined as the right answer. And so all these conflicting sources degrade confidence. So, really, what I believe is gonna become really critically important is to avoid having AI improvising answers. You really are gonna see more companies putting more emphasis on codifying their go to market playbooks, codifying their sales missions. And that's what technologies like knowledge graphs are are designed to support. Knowledge graph is a type of technology that allows you to create, like, you know, with very little effort, associations between things, associations between your products and competitors and between your, stages, etcetera, and allows you to organize and structure your data in a way that can, that AI can basically consume and respond, well to, but it does require making sure that that information is there and structured. And so what I think we're gonna see a lot of companies place a ton of emphasis on this year is making sure that their core go to market motions are documented. Right? The days of just, hey. Let's have sales manager train reps on our sales motion, but not actually have it documented in a centralized place that all of our agents that we're building, all of our AI, you know, tooling, and our reps can access is quickly gonna hit a limit. And so I think it's really important that companies document their rules of the road. Right? If you want people stopping at stop signs, if you want them doing certain things, there's a reason why rule books exist, and that's true for your go to market as well. And so for those of you that have been a SPEC IT customer, in the past, our original logo, looks something like this. Right? And it was really designed to to represent this this concept of knowledge graph. And if you, if you ever looked closely, the central, dot in our logo was turquoise, and it was actually Salesforce's turquoise very specifically. And it's because, ultimately, you know, as we think about building a knowledge graph and trying to understand how a company sells, my belief is that rather than going and relying on any document, you should look at your Salesforce data model because that is ultimately what codifies the truth. Right? Your stage names, the competitors, the products, like, all of that in your sales process is likely the best reflection of, like, how you actually sell versus any document out there. And so really building that knowledge graph, building that knowledge base around your Salesforce data model, and then connecting that to all your plays and messaging, etcetera, is really important. So, you know, regardless of how you approach it, I think it's really important that companies take a serious look at, like, what is our source of truth for go to market knowledge? If I'm building an agent and I don't wanna have to keep uploading new context every single time something changes in our business because it's, I mean, quite literally unsustainable, and we don't want agents out there going off of, you know, outdated information. What go to market source of truth do I wanna point all of these agents that we're building, all of our teams back to? And how are we really going to start governing that? And so whether it's a solution like Spectre or another, I think it's really important that companies make that a priority. The third trend that we're going to talk about is the emergence of the content reasoning layer, Right? And this is really something that I feel very strongly about, which is I think for a long time when we've talked about content, we've talked about the contents of content. Right? So, hey. What's the information in that document? What's the information in that slide? But I think the layer of information around that content becomes increasingly important in this new age of AI. Now let's dive into what that means. So the first one is really that context matters. Right? It's not just enough to know that a case study is correct. Right? So as we talked about earlier, I wanna know that that case study is up to date. Okay. Great. What matters, though, from a sales cycle, from a revenue standpoint, is of all the case studies that we have that are accurate and correct, which one is the absolute best one that I could send to this specific customer to convince them that we are the right solution for them? And that depends on the stage of the deal, the buyer persona, the industry, the competitor, the objections rates on calls, what the rep's motion is, how similar that content has performed, historically, what's the task at hand. Are they reaching out to a senior person in the call? Are they reaching out to a junior person in the deal? Right? So all this context matters when it comes. Now from a kind of pre AI world, that matrix, we expect reps to do. We expect them to deduce or to go in and, okay. Great. We've made it possible maybe for them to filter down content. Right? And now you can get a better answer. But, ultimately, we expect reps to try and, like, do that matrix for them. But I think, ultimately, this is something that, you know, AI can do really, really well if it has the right context, both of what's happening in the deal and whether that information has been useful and helpful in the past. And so that's where I think the reasoning layer is gonna emerge, where as you think about content, it's not just maintaining the accuracy of the information itself. It's about making sure that it's surfaced at the right time. So in this example, right, looking at what is the activity that the rep is performing. Right? Are they preparing for a call? Are they writing an email? Are they sending a business case, etcetera? Right? Then what's the context of what's happening in that deal? What can we glean from, you know, their CRM, from their call intelligence tool like Gong in terms of what's happened in the deal recently? Right? What do we know about the buyer? What do we know about all that context we talked about on the last slide? Then what is the best information, right, that we could coach them with? What is the recommendation? Well, ideally, then you're looking at your rules engine, your go to market playbook, and you're saying, hey. Based on all this context over here, here's the best information we should we should we should surface. Right? So if you have that information documented, you have that in a you know, whether it's a knowledge graph, whether it's just a more traditional knowledge based format, if you have that information, pointing that back so that, basically, the the the the role of, you know, whatever AI engine that you're using, to perform that task is gonna be able to ideally match those two together. Right? Here's the context. Here's the content. What is the best answer here? But then what matters is, okay. Great. Was that successful? Did that lead to an outcome? And that's really where I think understanding, like, what did the rep do with that information? Right? If we surfaced coaching, did they click on it? Did they review it? If we a piece of information for them to share with that buyer, did they send it? And then looking at, well, what was the outcome of that? Right? If we told them to review a battle card right before that call, how did they handle that competitive object objection on that call? If we told them to share a resource in that deal, how did the buyer engage with that content? And then did that deal end up closing? Yes or no? And then feeding that same data back to your recommendation engine. Because that's really where you start going from, hey. Here's information that our marketing team is creating or that our enablement team is creating to here's the right information that we should be creating that we should use consistently in every single interaction that we have with buyers. And that's ultimately what we want. We don't want lots of information. We want the right information, especially when it comes to reps. And so to me, that reasoning layer is really that real time navigation. If you've got your your knowledge documented in that go to market playbook or whatever you wanna call it, those are kind of the rules of the road. That reasoning layer becomes that real time navigation of like, okay. Based on all these different factors, where do I go? Right? That's kind of like what Waze did back in the day, where it's like, okay. Great. You've got a map, but then Waze helps you like, hey. There's traffic over here. Go in this direction. That's really what you wanna make sure that you're helping your reps with. And so that's really where I think content's gonna move from just kind of a static piece of information to more of an execution engine. And, really, a new kind of system of record is gonna emerge. So stay with me here. So when I think about, you know, what that system of record is that really matters in a world of AI, there's, of course, the content itself. Right? So this here is designed to represent you know, it could be a Google Doc about, you know, a process. It could be a a a deck about something, and this is really the contents within that information. Right? What exists, etcetera. Then you've got the metadata around that content. Who created it? When was it created? Right? Who owns it? What's the title? What's the version? Is there custom information that we add? Right? So most content has that layer of metadata. And so you wanna make sure that that layer of metadata is also being fetched by whatever content engineer you're using to use as additional information to help you to do so it's the right answer. And then you have usage and performance data. Right? So if you're using it in a technology that allows you to track that kind of performance, then you wanna know, well, who was that content used by? When was it used? In what deals? What was the impact of that? Etcetera, etcetera. And then finally, you've got more of that recently around the content is like, hey. When should you use this content? How should you apply it? When is it effective or not effective? And that information all in one kind of, like, record is really what you want AI looking at. You don't want it deducing. Right? Going back to, like, being probabilistic versus deterministic, you don't want it deducing these things on its own. You're leaving too much to a, you know, general purpose trained LLM to do. What you want is to have all of this codified in a way that any, you know, technology that you're using, or or building, right, whether it's an agent to be able to access and make sense of. And so I think this is really as companies kind of start shifting around, like, what is data? What is content? What is information? Right? Almost every company I'm talking to is like, we need a source of truth because, with the pace of change, things are so hard to maintain. Yes. Governance is important. We can talk about that all day. I've done entire webinars on that. But I do think that as we start thinking about, you know, what does the future of information look like, right, I think you're gonna start seeing a lot more emphasis not just on the contents of the content, but on all of this data as well to help you figure out what content to create, what to use, etcetera. And so with all this talk about content, I'm gonna end here on this, fourth trend before, going in to share a bit more thoughts in terms of how we're thinking about things. So I'm pause here. When you look at this screen here, you've got Canva, Cursor, Figma, Gamma, Lovable. If you spend any time on Twitter, X, or LinkedIn, these names are probably very familiar. You could add a number of other companies here, Revlet, etcetera. But what do all these companies have in common? I can't see the chat. So hopefully, you're hopefully, you're participating in the chat. Well, one, they're an investor's dream. Right? I think any board is very excited when they see that kind of hockey stick growth. Right? They're also putting most, of any company that's not in that, or any CEO or founder that's not in that category to to shame. I'll admit we are not quite a lovable, today as a company, but we're we're on a good track. But the reality is there's something more than just that growth that that's interesting about these companies. You know, Figma, Canva were kind of the pre AI that they're they're doing a good job on that track. But what all these these companies have in common is they're where work is actually created. Right? Cursor is where developers write their code. Lovable is where marketing teams and others create website. Right? Figma is where designers create their designs. Gamma is where pretty much anyone, founders, create their decks. I think the question is, like, what's that answer for reps? What's that answer for reps? Reps create content all day long. Right? They write emails. They draft documents. They create decks. They take notes. The challenge is that that happens in a ton of different places. And, yes, while Google Science, as an example, has added a Beautify slide, and you can create an image, I think we can all agree that no technology has actually nailed rethinking what content creation looks like for reps. And I would actually go as far as to say the same for enablement, and I put ourselves in that bucket up until last year. At the end of the day, I think that, like, we have not made the lives of reps or enablement teams easier. And so while the amount of information we expect them to master and learn and create and tailor because now this is your personalization. Right? We wanna stand out in every deal. We wanna make sure that what we share with our buyers is as relevant to them as possible. We have made that task impossibly difficult. And I think this is the year that that changes. There's no answer today, at least not one that I'm aware of. If you know, please put it in the chat. If anything, you can potentially try and bring gamma together, etcetera. But I think, honestly, if we're really being realistic, those answers suck today. And I think there's no excuse. Right? Technology has come too far to allow that to remain. And so I think the answer is gonna become really clear. I think you're gonna see more and more technologies focusing on helping you take data and translate it into actionable content. I think Gong's AI Builder, if you guys are following Gong, I think is a great example where they're starting to help you kinda take the context from all your conversations and turn that into reusable information that can be used across your organization. Right? And and that's why we're really excited about partnering with them because I think we can use that context to create really great content, which I'll share in in a few here. And so I think you're gonna see a huge, huge emphasis now because as companies realize that, like, hey. We need to have the right information. We need to have information up to date based off of all the trends we just talked about. I think you're gonna see more technologies, more vendors putting emphasis on the ease of content creation because it's the front door into context. It's the front door into making sure that you've got the right information that you need the, intelligence in those systems to rely on. And so with that, just to recap the four trends. Right? I think because of the increasing complexity and while there's a ton of possibility, most folks are learning as they fly the plane. It's expensive. I think companies are gonna realize, like, hey. If we actually want to achieve our outcomes, which is faster revenue velocity, higher growth, more efficient reps, us trying to make sense of all of that internally, both identifying the processes, automating them, orchestrating all the context, etcetera, we don't have the time to figure that out while our competitors are building purpose built buying purpose built solutions that automate all of that for us. So I think you're gonna see that shift back to vertical AI that is AI first integrated with the rest of your tech stack. Two, I think companies are gonna realize that if they really care about making AI a priority, they can't do that on a crappy data foundation. I think we've always talked about it with, you know, data governance and the importance of of having the right kind of clean data in your CRM. That's why a lot of companies are investing in technologies that kind of automate moving call contacts into CRM fields as an example to less to to have less reliance on reps. I think the same is true for your go to market playbooks. If you don't want your reps for AI guessing what you want coaching to happen on at different stages and what you want people to be doing, you're gonna need to make sure that that is codified in a centralized place that all of your AI systems can read from. Three, it's not gonna just be about content. It's about understanding all the information around when to use it, when it's useful, what impact this has so that you can really tailor that experience to each customer. And then finally, as different vendors race to really deliver the best experience you're gonna see a huge emphasis on content creation, which is gonna benefit well, this is my hypothesis, but I think it's gonna benefit quite literally everyone, especially on the go to market side as it becomes easier and easier to create really amazing content for your buyers or even keep your own internal information up to date as your product's competitive landscape and market continues to change. So with that, I'm gonna talk a little bit about, you know, the growth levers that we're hearing the most from our customers and ultimately shift to kind of how are we thinking about all these trends that I just talked about. These are things that I'm thinking about all the time as I'm testing different technologies, looking at the market, and thinking about, you know, like, where can our technology go to really drive the most impact? And these are just some of the trends that I'm hearing from most customers. Right? If we were to oversimplify how companies are thinking about growth in 2026, I think it's pretty clear that it comes down to four. The first one is new markets. Right? As companies are innovating faster, they're looking at potentially new products. They're looking at new verticals. They're looking at new use cases. Right? So they're looking at metrics around, like, new ARR growth, new pipeline, etcetera. They're also looking at, you know, growth from existing products. Hey. How can we do more cross selling across our products into our existing customer base? How can we shift from kind of selling individual products to doing more of a platform sale? If you're in a if you're a SaaS vendor, that's kind of like the name of the game right now. Hey. How might we think about pricing and packaging? How do we think about potentially moving to usage based pricing to better align incentives? But, basically, how can we grow from our existing customer base both by delivering, you know, more value, but then also by making sure that they're utilizing the maximum amount of our platform, especially as companies are continuing to look at consolidation. The third one is really all around just, how do we execute better? Right? I'd say when I talk to CROs, that's a large part of the focus. I'd say a lot of it is is new products, but a lot of it is is better execution. Right? How do we get our reps doing better discovery? How do we roll out a sales process that is more effective that our reps will actually follow? How do we actually want reps having conversations and identifying conversations with the right methodology? What technologies can we use to, like, ultimately help them be more effective? And, of course, new positioning. Right? Every company that's hired a CMO, is thinking about how to now position in this new AI world. And last but not least, I think the fourth growth lever that we hear the most is increasing capacity. You know, I think while a lot of companies are paying a lot of attention into how they ultimately grow efficiently, I do think a lot of the talk is around, hey. How do we grow without adding as much headcount? And all those things are true. I do still think that companies recognize that in order to grow, they need to have the best of the best talent, and companies are continuing to hire. So as you think about increasing capacity to to hit more more revenue goals, a lot of the talk is like, what needs to be true about how we now onboard our teams in this new world? Right? If they can access information on the go, what needs to be true about onboarding? How do we really make sure that they have more time selling versus that 28% average today and focusing there? And so with that, you know, this is what we're hearing the most. I think if we kind of break it down, there's a few different use cases. I'm gonna I'm gonna talk a little bit about some of the use cases that I've been thinking about, especially around product launches, because it's definitely like, if there's one theme I'm hearing from every single CRO I speak to is our development teams are increasing their innovation rate faster than our go to market teams can absorb. And when I say go to market teams, it's not just your reps and your CSMs and their ability to communicate the right messaging, the new value, the right demos to your customers, but it's also the ability for your marketing teams, your enablement team, the rev ops team, all the folks that you've got supporting those individuals and their ability to keep up with all that change, to document it, etcetera. And so with that, I'm gonna go through kind of how I think about the future from a technology standpoint as we go we go into the year. So as we think about kind of the three pillars that we're innovating around, it's really around scalable go to market readiness. Right? So it's a lot of what we just talked about, which is how do we help teams scale faster with centralized go to market knowledge and content that keeps reps up to date and aligned around all the latest new information around products, competitors, etcetera, and ensuring that you get faster change adoption, right, less mistakes from using outdated information, whether it's in your talk tracks or in customer conversations, dealers, etcetera, and really ensuring that you've got increased bandwidth because keeping that information up to date, that governance problem becomes easier for everyone involved. The second pillar is really all around rep efficiency. Right? How do we help reps learn and just move faster? Because we believe that they can do that when they have the full context, when they have the right automation and coaching in their workflows to help them move fast. And so that's really all around, like, faster ramp times, easier discovery of the information they need in deals, and, of course, you know, automating the steps for them, reducing some of that dependency that you have on those hero reps that that drive the majority of your pipeline. And all of this only matters in service of revenue velocity. At the end of the day, like, what matters is making sure that you're able to hit your growth goals. And so revenue velocity is really the metric that we are spending the most time thinking about both from a, technology standpoint as well as, honestly, our own teams. Right? What can we do to help our own, revenue velocity, move faster? And so we ultimately believe that your deals move faster when reps are empowered with the best possible information to create the most scalable buyer purse personalized buyer experience possible. Right? So we wanna treat we wanna make it possible so that every single one of your buyers can have the most custom experience possible where they every interaction, every piece of information that comes their way feels specifically tailored to them, what we know about their industry, what we've heard about their pains, etcetera, and paints your product as that ideal solution. And that's really where we think you can have higher win rates compared to, you know, reps that are just sending templated information out there or irrelevant case studies, and where your reps can have a lot more confidence, going going into those conversations. So with that, I'm gonna show a few examples here, in terms of what that looks like. So as we think about scalable, go to market readiness, right, what I spend the most time talking to customers that are moving over from kind of more traditional knowledge or or content management platforms is really how do we make it as intuitive as possible to create and unify and manage content. Right? That's where it starts. Because at the end of the day, you're in marketing, if you're enablement, if you're in rev ops, right, making sure that the information, going back to the first trend, that your team relies on is up to date is really, really important. So that's a huge focus, for those of you that read my book. Right? I talk about that in three chapters, how you unify the information, making governance and content upkeep, dynamic and intelligent, and then making sure that you're having all the visibility you need to understand that. So we'll talk about what that can look like from a product launch standpoint and why we're doubling down so heavily in our knowledge and content engine. Next, we'll talk about, rep efficiency. Right? We really wanna help reps get to the answer as fast as possible that they need so that they can get back at doing what they do best. And so when we talk to reps that are moving from kind of more traditional content platforms, we wanna see them see the kind of results that Anthony had here. Right? 90% faster content access. Because we don't wanna measure how much time people spend on our tool. Our tool is as effective as their ability to sell more. And so that's why, again, in the book, I talk about why we believe that information should be contextual to what the rep is doing, and it needs to be impossibly simple to man to to navigate. So this year, we're really gonna make a huge, huge emphasis on coaching and automation, of course. And then last but not least, how does all of this ultimately work together to create the most scalable buyer expanses for your customers? At the end of the day, you know, as we think about how do we make sure that the customer, right, the buyer, whether it's post sales or presales, is getting the right information to help them extract more value from a product or be convinced that you're the right solution for them, that's where the efforts we put around our unified deal intelligence, right, bringing context in from your CRM and Gong and really carefully orchestrating that to make it as effective as possible is really important. And so I'm gonna go ahead and showcase a few quick things in terms of how we think about driving this efficiency for our customers if you wanna take a quick look. So with that, I'm gonna actually start with the scenario, and we're gonna kind of follow that story through of product launches since that's the kind of key theme that I hear from everyone. Like, this year, we need to get better at the ability for our teams to both, you know, manage all these launches, absorb them, etcetera. So I'm gonna go here into the Spacket platform here. So this is our centralized, content repository here. You're looking at a high level page here of all of our spec capabilities. Actually, just to show you here, this is our high level platform page. If you scroll down, you can see a lot of information. This is an internal facing page of, you know, what our platform can do. You can see here that we've got another page here on all of the capabilities that our platform has. And down here, we've organized it around all the different products that we offer. Right? And so whenever it comes to a rep learning product, they can go into an individual product. So here, I could go into learning paths, for example, and find all the information I need around learning paths, including videos, etcetera, etcetera. Now you might be asking or looking at this and being like, oh my goodness. Sure. This looks nice. I know you have created this before, but this also looks like an immense amount of work. And this is, I think, what holds back so many teams from actually just getting this content out there is just the lift that it takes to create content that your reps can actually consume and manage. And so I'm gonna show you what it looks like to create one of these pages internally, showcasing something that's new. Oh, and it looks like I've been demoing and not showing the information. My mistake. I am so sorry about that. Gina, Kendall, feel free to come off that. Can everybody see my proper screen now? Right. Let me just go. I'm sorry, everybody. Let me go ahead and share the page again. Give me one second. I'm gonna go ahead and share the full window here. Alright. Can someone from my team confirm that you can see the right information? Alrighty. And we're gonna restart that real fast. So what you can see here is the, is the page I was talking about here. We can see all of the capabilities that our product has to offer. This is an internal page for our team that has a high level, you know, what's second, what capabilities do we offer, etcetera, etcetera. And down here, you can see kind of, like, another double click you can go to into our different capabilities that our team can go learn about. Right? So this is more of your centralized repository. I'm not gonna talk about all the technology in the back end in terms of how we relate all this information automatically, at but least you can kind of see what that looks like here in terms of creating a launch page for any new product that you launch. So, for example, we're launching, learning paths here in in the next two weeks, and so we created this page. I personally created this page really, really fast here to put together all the information that our team needs to know about this. And down here, you can see lots of information. Now as I shared, creating all this content is typically where we see teams really get burdened down. And so I'm gonna walk you through what that looks like in a really easy way. So here, I'm gonna click new. I'm gonna go create a topic page. And rather from starting from scratch, I'm actually gonna look for a template. So I'm gonna go into our product launch launching page template. Right? So these are beautifully, you know, HTML rich templates that you can modify, make changes to, etcetera. And then I actually wanna reference existing context. So similar to, you know, chat, GP, gem, etcetera, I wanna actually say, hey. I know that our product team has already documented some of this, so I wanna reference that. So for this, we have a Gong integration, that we just launched. I'll be demoing that in a few here. So I wanna create a launch kit for a Gong integration. So I'm gonna search for Gong integration and see what information our product team has already uploaded here that might be relevant. So here, have an initiative overview. This is content that our team has. Here, it looks like I've got a PDF, etcetera. For now, I'm gonna skip on the technical details, but I'm gonna upload these different PDFs and resources that are in our existing library to reference. This is important because I don't want an LLM going off of pretrained data to create this. I also don't want it going off the Internet. I want it going off of our trusted information here that they can review, and I can always see what that looks like right here really quickly. So now I'm gonna go and accept. I'm gonna say, hey. Help me create a product launch page for our new Gong integration based on the information attached. And so what it's gonna do is it's gonna take this this template as kind of inspiration. It's gonna look at all the content that I've uploaded, and it's basically gonna help populate that template based off of that. And so, you know, customers, you can create your own templates, of course, but it's a great easy way to take existing context and create really powerful information. So here, you're gonna see Gong integration launch kit, your essential guide successfully launching launching and leveraging the new Gong integration. Here's what the Gong integration's about, why it's exciting. I'm gonna look here. Yep. As a proactive just in time agent. Awesome. Key features, AISChic integration, contextual Gmail. Correct. Centralized admin. Awesome. And then here, I can go in and fill out additional resources. So, for example, if I go to this video here, I can grab that real fast. And, you know, actually, I'm gonna add it up here just for the sake of this here, and I can add it. So the whole point is making it really, really easy to take any existing content you have, whether it's your product teams, you know, product brief that they created, etcetera, and converting that into, like, a ready made enablement content in seconds. So I'm gonna go ahead and say, you know, Gong integration. And, for now, if I had more than the three minutes I wanted to spend on this, I would fill this out here or remove really easily. You can use natural language, make a ton of different changes here. It's really, really responsive and easy. But here, I'm gonna go ahead and just say, I wanna add it under clickabilities here. And boom. I can now publish that information. So now, really quickly, I've got that launch kit that came from some of the existing information that we had. All of this information is gonna be really easy for our team to access. And, of course, any information that you pull in from Google Drive, etcetera, that you add here is gonna be really easy. So, for example, let's say now I've got this, partner presentation. This is actually from a conversation we had, with our team. Let's say that I wanted to add it real quick to that topic. I don't need to go into the platform. This, again, is where our automation comes in. I can say, hey. I wanna add that to Speccy real quick. I wanna add that to our Gong integration topic. I wanna make it externally shareable and sync that file. And just like that, it's gonna get added to the system. It's gonna get indexed in the right place, etcetera. So the whole point is to make it really, really easy for you to create and make sure that that information stays up to date. Now for whatever reason, you get new information, something changes at any point in time, you can go in, and you can upload that new context, let's say new messaging, whatever it is, and just say, hey. Please update this existing page to this to this new content. So we wanna make it really easy for you to create and for you to manage that content. Awesome. So with that, now, obviously, making it easy for you to create and manage product launches or new content that you're creating, super important. Right? We wanna help you do this in what usually take hours and quite literally five minutes. But then we also wanna make sure that your reps are equipped with this information in the moments that matter. So let's take the example of a rep here. I'm in my email, and I just got off a call with the Denver Nuggets. I was gonna use the Denver Broncos as an example, but, unfortunately, they are no longer in the running, sadly. But the Denver Nuggets are doing a good job, so I wanna go ahead and sell to the, nickel on the Denver Nuggets here. And so the Nuggets are doing great, but it sounds like they're having some challenges around rep efficiency, etcetera. And I wanna make sure that after this demo I just had, I followed with the best information possible. So here, I can open up Sidekick, and it's gonna do a few things. The first thing you're gonna see here is it surfaced a, deal room that I created already for the Denver Nuggets. I'm gonna show you guys what that looks like in just a few seconds. It's also pulling up my recent call with the Denver Nuggets right from Gong. So here, as I'm looking at my email at or and this could also be an email that I received from them. I can quickly pull up that call. I can see, hey. Here are the next steps here. Here are all the key points that were mentioned. I can reference that. I could even copy that, you know, into our sidekick here to help me create a, you know, response if I wanted to. There's a ton that you can do here. But the point is that you can really quickly access that information without going anywhere. You're also gonna see here that it scanned this email, and it's now surfacing the right information. So here's content it can share externally. Now what it did is it identified a few different themes. Said, hey. It looks like you're focused on deal room automation, the Gong integration, learning paths, and CMS replacement benefits. We found content for you from your library. So just for perspective, we've got quite literally thousands of resources in our library. And what this AI engine did is it went and it looked for just the right themes and just the right content. So if I go under deal room automation, it's showing me, hey. You might wanna share these deal room slides. Looks like they're fresh. They're updated five months ago. They've been shared, and here are some views. You can go ahead and add this to your deal room. Or maybe, hey. You might wanna share some information on our Gong integration. Right? Hey. Here's a PR that you can release. Here's that information for reps that you might wanna share. Or, again, learning paths, you might wanna share this video. So the whole idea is that it's really easy for them to find that information and add it. So here, let's say that I wanted to add this oops. I'm gonna go ahead and click here. Let's say I wanted to add this one pager. Go ahead and see it a little bit bigger here, make sure it's the right resource. Okay. This looks great, etcetera, etcetera. I can go ahead and add it right there to my deal room. So here, if I want to, I can create a deal room. I won't do that in this very moment here, but it grabs your custom branding, etcetera. Or I could add it to that same Nuggets deal room really easily for me. So then here, if I go to my Denver Nuggets deal room here, I'm gonna pull up on my deal rooms. I've got my Denver Nuggets deal room. I can see now that it was added to that deal room right here that I can share. So just to give you guys an idea of what that looks like here, I'm just gonna pull that up. So here, you can see what a deal room looks like. It's a centralized place that you can ensure that all that same information that you want your reps reviewing can also make it to your buyers if it's externally facing content. And your buyers can easily go through that content at their own pace, really understanding that information and making sure that it's specific to them. Of course, you can make sections, etcetera. There's lots of other features that, I will go through on a later date. The But whole idea is that you can have one centralized place where all the information that you you want your buyer to have access to is found. But taking that a step further, I just have this amazingly rich conversation with Nikola off the Denver Nuggets, and I wanna make sure that I encompass all of that in my conversation with him. I wanna go ahead and actually add an exec summary that's really specific to that call. So I'm gonna go ahead and add content here to my deal room. Now I don't wanna start from scratch. As a rep, I'm gonna pull from our existing exec summary business case. So I've got this template here that allows me to, like, you know, quickly potentially add information. I could manually go fill it out. But in this case, I actually wanna say, hey. I know that I have a Gong call, so I'm just gonna go ahead and search for nuggets. Boom. I just found that Gong call. Since this is a CMS replacement, I'm gonna pull up that battle card that I wanna make sure that it's aware of too. Boom. And now I can say, hey. Help me create a business case for the Denver nuggets based on my last call. And so, again, here without leaving my workflow oh, no. Let's try that again real fast. It works really good. This is why custom demos live on calls. Let's do that. Please help me tailor this business case. Shouldn't be a problem here. So what it's gonna do is it's gonna take that call, and it's gonna basically populate that business case based off all the context of that call within just a few seconds here. So let's see. This is always the suspense. There we go. So here I can see business case summary, Denver Nuggets. Right? The strategic imperative, sounds like they really need to improve their revenue velocity and sales productivity. Those tickets are going down. Right? Here, you can see that they're focused on reducing ramp time. Sounds like they've got some key, content discovery bottlenecks, some key deal execution challenges, some lack of AI powered features with the current solution they're using. There's a reason why SPECT IT strategic value is a good fit for them. Again, our benefits are tailored to their specific pain points and needs. And then, of course, here oh, wow. This did a great job. It called out from the conversation the specific call call out. So it sounds like they're focused on increasing win rates, accelerating rep ramp up time, and improving productivity and accuracy. So all of this is directly translated from their call into a resource that they can create. So now I can create a quick title here for my conversation, and then great. I can now publish that. So now it's gonna be in that deal room. If I want to, I can also create a quick mutual act action plan. So here, can go ahead and say, hey. I wanna create a quick, mutual action plan. I'm gonna do another example. Let's, let's make sure that this one works too. So I've got a mutual action plan. There's a lot of next steps here. I wanna make that really easy. I can go in, grab that, oops, grab that same call, and please create a mutual action plan based on my call. And, again, it's gonna reference that call, and it's gonna auto populate. I should have actually shown the template so you could see it's just, like, all basic steps. And it'll actually add as many steps as the call accounts for. So you're gonna see it's gonna populate the milestones. It's gonna populate each individual step in the mutual action plan too. So here, it sounds like there's three different ones. There's a renewal timeline that's really important. There's a deal closure, and there's a full migration go live that's really required. Wow. You did a great job. Here, it's calling out all the mutual action plan, steps that were discussed on the last call, And here, it's actually calling out the completed mutual action plan. So here, if I wanted to, I could add this as a mutual action plan. I'm rushing since I've got two minutes left here. But here, at least I'm showing you how you can demo and do something really amazing in just a few seconds. Before I send it, I'm gonna do a quick preview just one more time, making sure I'm feeling really, really good about it. So here, I can see, those resources. I could reorder it. Of course, I'm not gonna take that time here, but you can see here that it's now in this document really, really easily here. It looks nice. It looks presentable. This is definitely exec ready. I could reorder it. I could put it in its own section here. I won't bother you with all the the quick details. Of course, everything I just showed you, yes, reps can do in our web app too, but I wanna show you just how easy it is to do right here. And then I could go ahead and say, hey. Please add this to my deal room so that reps can go ahead and send that. And just like that, it's that easy. You can add it to your deal to to that email. You could also use Sidekick the chat to help me generate bullet points for the email. I could have them draft the email, etcetera, but I can go ahead and send. Now as soon as the buyer engages, you're gonna get all those analytics and understand that impact. And so I'm gonna end this demo saying, now if we go into buyer engagement, I'm not gonna show you all of our real data, because I am demoing production because, that's high roll. But here, what you're gonna see is that, we just created content, like, in the last couple weeks for those that new learning path. It's functionality that we have launching here in the next couple weeks. And so I just created that learning path topic with the support PMM. We added a few resources, and I can immediately see, there's a ton of additional information above on actual, like, deals impacted, etcetera. I haven't actually gone I I'm not gonna show that since there's sensitive data there. But here, I can see, hey. Of the resources, what's impacting pipeline the most, what's impacting closed won revenue the most? So these are deals likely that have closed, very recently. What's some of the topping age contents of all the content? What's getting the most viewed by buyers? How often is it getting shared? How much time is being spent? Etcetera. Of course, I can filter by deal. I can filter by region, by team, by user, etcetera. But you're able to really see that full cycle. We launched this product. We put the resources together. It's showing up where the reps need it so that they don't even need to know to remember. It's showing up right in those deals, and then I can actually understand that impact. And then, of course, if you have deal rooms, you can see all the additional information down here of that in of, that impact. So there's a ton more buyer analytics. I won't go into too much detail, but this is really start closing that loop from, hey. Did we launch something? Are our reps adopting it? Are they using that new messaging? You can also see every interaction reps have had with that information and spec it to make sure that they're actually using it, see what questions they're asking, etcetera, but then tie it back to revenue performance. Tie it back to impact so that you know whether your solution is meeting needs. So with that, I'm gonna end here with a few things. The first one is a lot of what I talked about is covered in my book. Can order it at justintimeenablement.com if you're interested, but I talk a lot about the future of AI, etcetera. We were just named a visionary in the Magic Quadrant. It was a really proud moment for me personally as a founder, but really a testament to the hard work that the team's done. A lot of companies have really changed their approach to how they think about enablement. It's been really, really fun partnering with them. So if you're interested in taking a look, definitely do. But I think, ultimately, I'll end on the note that, you know, as this rate of change continues to increase, and I think it is really gonna keep increasing at this exponential rate, I really believe that the companies that learn the fastest win. The companies that figure out how to have their rep performance move at the same pace at that rate of change are gonna win. And for that, you need that right content infrastructure. You need that right revenue infrastructure. And that's really where you can start thinking about, like, hey, is there a world where you can have $10,000,000 quotas? Is there a world where all of these investments that we're making, all of these productivity gains can actually translate to new levels of performance across the team? So with that, I hope that today's webinar was, at minimum, interesting. But I hope that you learned something. I hope that it was helpful as you think about your 2026 strategy. And if you have any questions, feel free to reach out. Thank you so much for the time. Hi.