Docebo Inc.

All right, good afternoon, everybody, and thanks for joining us here at Doce Will Inspire in Miami. Got a great kickoff this morning with a nice release. I trust you've all seen the information by now. A couple of notes are out there circulating. I've got an awesome presentation for you here today. Our speakers include Alessio Artufo, our CEO, Scott Peacock, our SVP of product, and Brandon Farber, our CFO. So before we dig in... Let me remind you that during the course of the presentation, we will be making some forward-looking statements. Please refer to the Safe Harbor Statement here on the slide deck and in any of the other materials that you might go to as part of your research for notes that follow. Quick agenda, unless you're able to kick it off, we'll do some product demo work with Scott, hit a financial review with Brandon, and then we'll go into the Q&A. As part of the Q&A, My colleague Jason and I will come to you as you get pointed by Alessio and Brandon for a question. We ask that you hold it to a single question, let it work around the loop, and then we'll take second rounds of questions. We will be available afterwards for additional questions once the session officially wraps, if you have something further you'd like to check out. So with that, I'd like to bring Alessio up to the front, and we can kick off. Alessio.

Better now?

One, two. One, two. Yes. Perfect. Awesome. All right. Hello, everyone, and thank you for being here. Thank you to those that flew in from... Far away, I know some of you were on the right eye, so I appreciate you guys making it here. For those of you that have been in the keynote, there will be some concepts or info that will overlap, but the material is slightly different. And those who haven't been, I think particularly during the session with Scott, you'll get to see some of the concepts that we're going to be talking about. applied in real-life demo. Let's get it started. First of all, a little bit of background as far as where we stand in the state of... Every 15 years or so, enterprise... And every time, the same companies win. In the phase of the on-prem era, yeah, so... This early 2000s. Many of you remember those days. It was all about Oracle. It was all about SAP. They owned the data and the workflow. That's because Salesforce owned CRM and the sales and marketing workflow. Workday owned HR and the payroll workflow. ServiceNow owned the IT business. Those companies that just treated up an interface really never broke out. Got it?

Got it. Want me to turn this off? Sorry, guys. One, two. All right.

Now we are in the agentic era. The narrative that we were hearing is that SaaS is dead. I think that's lazy.

What's affected is SaaS that's built on shallow data and commoditized workflows.

What's thriving are companies like Vachevo that own the system of record and run the work on it. Vachevo's point sits on two decades of learning data. Compliance records, learning histories, customers certification history. What's important to underscore is that none of this data can be fabricated. So, just to recap, we own the data, we run the workflow, and now we're shipping the agents of it all. Now, let's dive deeper into what we're building, what we've built, what we're shipping. What I said earlier is that today Vachevo is shipping at an unprecedented pace relative to its most recent history. I did bring up a stat relative to our growth in usage of Locebo AI, which we've seen go up 300x over the past few months. And that's just the beginning, because the future that we are headed towards And the things that we've announced today make us ecstatic about where we're going.

So let me talk to you about AgentUp, MCP, in the context of the Chevo, and enterprise knowledge.

I'm going to start with AgentUp. These are proprietary agents that reason, decide, and act on our skills graph. on evaluation signals and that use our enterprise knowledge which captures the wide net of company knowledge that exists in an organization. These agents build courses. They triage compliance. They nudge learners. They run the work that the L&D teams used to do manually. They may do that independently Or they may do that with humans in the loop. And eventually, this is my prediction, and something that we're preparing ourselves to, they will work with other agents coming from other platforms. The second thing that I want to talk to you about, even though I go to the next side of the slide, is enterprise knowledge. I'm going to have more on this topic in the material later on, but for now, simply put, to give you context, we have effectively connected 20-plus enterprise-grade systems that live in most enterprises so that our customers can get that knowledge where it lives. whether it's SharePoint, whether it's Confluence, whether it's Notion, whether it's Google Drive or Slack or Microsoft Teams, whether it's different CRMs or HRISs. The point here that I need everybody to understand is that SharePoint is no longer just a course catalog. We make knowledge available so it can feed real capability building. And on MCP server, I had a couple of conversations with some of you earlier in the all, asking about it, whether this is going to make us stronger or more commoditized. The fact that we go natively into Cloud, the ChatGPT, Copilot. You know, when your employees... when the employees of our customers ask a question about trading skills or certification in one of these environments, the answer comes from the table. What it means is that every AI assistant becomes a distribution channel for us. And when the learner interacts with our data, that becomes an outcome. And the more the learners engage with our data, the better it is. When you think about the combination of agent hub, MCP, enterprise knowledge, in the context of the LMS combined with skills, this is a unique proposition that nobody in the market right now can replicate. Not sun, not water, not cornerstone.

This combination that I'm presenting is unique. The market wanted us to pick a lane. We built the lane.

Look at this image. I think what I just said will become more clear for you all in this animation that is not anymore an animation because it went fast. So, look at the left side.

The legacy LMSs live there. Nothing wrong with it. Primarily, they check the compliance box.

On the skill side, you have the skills intelligence platforms. Great to manage signals, but no way to act on gaps. So, without a learning engine to close the loop, if you know that somebody has a gap in a given capability, and you don't close that loop, and you don't connect it to any knowledge or learning source,

There still is a gap.

And on the right side, you have the knowledge platforms. The wikis, the eco-pilots. They are helpful for just-in-time knowledge retrieval, but disconnected from, number one, what people need to learn, and number two, from any recorded and auditable validation. Because the fact that you do a search and find that you looked for a policy using a tool like Lean, it's not recorded anywhere. That's not auditable.

It's not a data point.

The chamber closes the loop by sitting in the needle of this, by capturing the opportunity that exists to combine knowledge, learning, and skill in one closed loop. That is the magic sauce. And we haven't made this up, by the way. This is what our customers have been asking for. It's just incredibly hard to build. So, AI without data is a demo. Well, let me tell you this. The table is not a demo. Here's what we own. And I know this is an important topic for the models and for the understanding of Docebo's position in the current AI market. Let's talk about compliance records. These are legally required and very scrutinized in pharma, in banking, in insurance, in manufacturing, in aviation. They are auditable. They're tied to a specific person and a specific date. And the thing is, an LLM cannot hallucinate a record like this. It must come from a system of record. And that system of record, that is us.

Skills graph.

Thanks to the investment in 365, now we hold the canonical map of who has what skill at what level proficiency across the entire workforce of a company so every agent that touches learning talent or workforce planning needs this data to be valuable if you don't know who is at what level how do you provide training that is coherent with it

And we control that data. We control that data that is made available via API in the way we decide. Learning history.

100 million learners are part of the Docebo ecosystem. 100 million learners. And this is the training material that makes personalization actually work. You can't synthesize it and you simply earn it over time. And then to the external training data. This is the core business of the companies. The data about the customers, who completed which certification. This is sitting really at the intersection of compliance, revenue, customer success. It's really invaluable data for any company. And just for reference, nearly 50% of our ARR is with companies that use Docebo for internal and external training. In a genetic world, it's very clear. The companies that are being squeezed are the ones with no data or light data and a pretty UI.

And we're the exact opposite of that. But we're the pretty UI.

One more thing before I pass the baton to Scott and the team. I got this question a lot. And And I think it's going to be relevant to understand better how we think about knowledge in the context of the recent announcement that we made today. The question is, what is the difference between knowledge retrieval and what you call workforce readiness? I think it's a very valid, astute question, and I'd like to handle it head on. It's In simple terms, knowledge is not learning. Retrieval of information is not capability. You know, there's tools like a question asking cloud or a question asking Glean. You certainly find an answer. Now that we are having access to our enterprise knowledge, in Docebo, you can find the answers that come from different sources across the company, and we make sure, as a learning platform, that you need to come back and ask it again. That's the difference between a knowledge system and a workforce readiness system. Claude can certainly surface on the compliance side a policy document, assuming that it's connected to some system. But Cebu, differently, can prove that the employee Mike, McCarthy, was trained on it. On what date? For which regulation? With the auditor-ready record available for the regulated industries.

That's the ballgame. You can swap a search tool tomorrow. Nothing changes. I know it because we're doing it at the table now. But you cannot swap the compliance system of record. And then scope.

I think this is an important part to understand as well. Scope intended as in audiences. Um... A tool like Glean or a tool like Cloud used in any company is for your employees. The table, we designed it to address audiences that are both our employees and our customers, our partners, our franchises, the distributors of our customers. And that is revenue generating learning, not internal productivity only. I want to then close on the agent side. You know, a knowledge agent, a knowledge agent, which we use, I use, writes a great summary. Very helpful. A learning agent moves a person from novice to expert and validates it and records the information. One Convenience. The other one is tied to revenue, to risk and to retention. The value is profoundly different. Hopefully that addresses up front some of the questions that I know you will have on this topic. But I'm looking forward to the Q&A. I hope this is helpful and I also know that the most exciting part is yet to come because Scott's going to give a A little bit of a deep dive preview of what he showed today. For those that missed it, and for those that didn't, by the end of today, you'll learn of the Demo Decebo yourself. Scott, on to you. Thank you.

Lovely sound. Perfect. Well, let's hope I don't need both of these mics, so I'll turn this off for now. And I guess before I get into my kind of core of the presentation, I think it's worth highlighting... how we structure this and how we think about the different components of the solution that Alessio was just talking about. When we think about the workforce readiness platform and how we structured it right now, I want to highlight that learn obviously remains at the core. It is the majority of our SKUs and, of course, the majority of the revenue. but we're building around it as well, and everything is going to be integrated, right? So although 365 was, you know, months ago a separate platform, we already have organizations that are using both systems integrated and passing data back and forth. So I can show you a little bit more about what that looks like in practice. And, yeah, so we'll get right into my kind of first component. Before we do that, for those of you who attended the keynote session earlier today, you notice that it's not just the story of agents, it's a story of us deepening our platform and improving the core experience that all of our customers experience on a daily basis. And for those of us who were in the room, I thought it was pretty telling that some of the most simple or basic concepts were the ones that got the biggest cheers. I think at one point I said you could now cancel a certain type of ILT session, and I literally got a standing ovation. So I think it's important to keep our eyes on the future and on what we're building and all the innovation that we're bringing to the table, but we must remember the people that we're really serving and the challenges they face every day and the hours and hours and hours that we can save and how happy we can make them with some of these core improvements. One of which is the Table Companion. The reason I call this a core improvement is not because it's not innovative, but it's because we want to get it in the hands of every single one of our customers. This is... an answer to the customers who didn't have the ability to leverage a massive IT team to build a complete headless experience. It's something we're hearing a lot more about, this idea of embedding all of the information and doing direct API integration so you don't have to touch an interface. For our customers who wanted Companion, all for them was to say, hey, I'm in Salesforce now. And my folks need training in Salesforce. My folks need training in our intranet. My folks need training wherever they happen to be. And I don't have a 30-person IT team to build an integration, so we'll turn on Companion. And Companion is Docebo's capability of servicing the right training for the right person, no matter where they are on the web and no matter how they access their work. So this knows who I am, it knows what I'm trying to accomplish, and it knows where I am. And, you know, Alessio rightly pointed out that, you know, data is at the core of everything we're doing. The more we rely on AI to answer our questions, the more we use AI on a day-to-day basis, the more important it is that data is relevant to me. Companion has another reason that we're putting it in the hands of all of our customers. It allows us to understand how those learners are navigating the web, interacting with their systems, and it allows us to build signals so we can say, hey, You know, 40% of your workforce watched a YouTube video on MCP servers recently. You should probably create some training on that. And in fact, we have an agent who saw that and has already made a draft for you. This is the kind of stuff we're doing by giving more access to more of our customers for things like companion. Another really core system of, you know, improvements that we're making recently is our enrollments rules engine. And again, this is one of those, you know, standard parts of the platform that people are so excited to see coming out. And this allows our enterprise organizations to scale massive, massive executions of enrollment in the hundreds of millions. When you have a system like ours that is so connected to all of the infrastructure, all of the data, all of the compliance records, making a single change can cause a cascade that affects 100 million different components of the system. We needed a way to do that for the largest organizations in the world. And so now we have it. And I wanted to highlight another core component that we're building upon. For those of you who are tracking, AI Virtual Coach is how we've historically referenced this. But as we've expanded our capabilities with AI role play, we've changed the name to reflect what you're really doing. And fundamentally, it allows people to create custom rubrics where they can define exactly what good looks like and execute against it. So content marketplace, last one before I go into more physical demos. I know we want to see the products. But I wanted to highlight that the content marketplace is getting upgraded, which allows us to add tons of new partners into that marketplace and give access to our customers very, very soon. So we talked a lot about the future of workforce readiness. We were sitting on the stage about this. And the agentic system that we're building here, I want to show some of the components that flow into this, and 365 is a good place to start. So I'll jump over to my 365 environment. It's embedded into Docebo. And we're going to start from the learner's perspective. There's an agent calling. So we're going to start from the learner's perspective. And Ashley Anderson here, who is in the system... we are already hooked up to all of the sources of truth that feed the information into the platform. So you can imagine we're pulling in her LinkedIn information, she's uploaded her CV, we're hooked up to the talent management system that the company used to hire her in the first place, and of course we're hooking up to SAP, Oracle, and Workday, wherever her performance records exist. This is step one of gathering all of the information for all of the learners in our system. Based on all of this, we can understand the type of job she has, the skills that she's building, and fundamentally how her job description lines up with the skills that she actually has. So she's able to say, hey, look, I'm a software development engineer. What's the path that my career could take from here? And ultimately, where are my skills in Java or C++ compared to where they need to be? And this is powerful for an individual because they can build their career and plan what they're going to do next. But more importantly, it's powerful for the administrators of our system. Because an admin doesn't necessarily care about that individual or the individual who's kind of building their career. It's important. But fundamentally, we're building a huge database of all of the roles that exist across this organization, how they relate to each other, the skills that exist within each of these roles. the expected skill level of each of the people that hold these roles in systems engineering, digital business analysis, software engineering. And we can take... Oh.

All right. Can you hear now? There we go.

So we can take a look, based on all of the information we know about this entire audience, to understand globally, across all of my software development engineers, where are their skills compared to where they need to be. C++ is right on the mark. Team is a little bit lacking in the debugging skill that might explain some of the regressions. And Java is another core skill that we need to grow on. I also want to highlight the ability for us to now do benchmarking across this kind of job. And so we can do this from an internal perspective and see who's declaring the skills at what ratio according to the role. But importantly, we can take a look at benchmarks from outside the system. So we can take a look at all the different organizations that are hiring software engineers right now and how that has changed over time and how people are being found and brought into the system. So these are some of the smaller components that overall drive all of the data that we now have access to as we get into what we are calling the evidence engine. And the evidence engine is our ability to say, we understand the distinction between a signal, saying this person is interested in this topic, we should build courses, we should have an agent take an action, versus, as Alessio was saying, evidence and validation. Our true understanding that someone has done the actions that we classify as that person being an expert in that particular skill. And, of course, all of our customers are different. We serve a huge range of industries, organization sizes, and complexities of organization. And so what one company says is the prime example of a manager validation. My boss says I'm an expert at presenting. Maybe so-so. But if I get a proctored certification for an AWS practitioner exam, that's the gold standard, and I'm considered an expert. We gather all that information, and we can allow our customers to set up campaigns to close the gaps where we know that they exist. So people are getting used to vibe coding with Claude. We need to expand that. We need our team to be better and faster with it. So we can launch a skills campaign and see who, is actually successful. So in this case, Maria Chen, James Liu, Priya. The individuals benefit because they build a body of evidence that proves their capacity, that proves their skill. And we are able to say, okay, let's feed all of this information into the agents that this person has access to. When I first got ChatGPT, the reason that I moved from Gemini to ChatGPT was because they introduced memory. They introduced an understanding of who I was, and I said, you know, refer to me as Captain Kirk, and, you know, here's all the information about me. Most people, when they're configuring their AI that they use on a daily basis, you're using an MD file in Cloud or a couple paragraphs to explain who you are. We have thousands of data points to understand who someone is. We have every time they watched a YouTube video with Companion on. We have every course they've completed and every score they've achieved. And so we can use all of that to make sure that when our AI answers their questions, it does so with them in mind and their best delivery system in mind as well. Okay. So let's go back to our slides here for a second. And I want to also talk about Agent Hub because I think it's obviously deeply relevant to the way that we're moving forward here, connecting to this peck stack and knowing the people. I slide all the way over just for a single slide. But I wanted to highlight again that the way that we're being able to build these agents and the way that our system is going to connect to other organizations, it's really dependent on the company that we're working with. But we're going to be creating agents that understand exactly how our system works. And you've heard about the MCP server from our perspective, where we expose certain information that we want the web to have access to. But Agent Hub also allows us to act as an MCP client. where we're reaching into other MCP servers, gathering information, and taking all of that data that we have access to, and making courses, delivering training, and setting up learning plans for people at scale. Our customers are some of the only ones in the world that can get 20,000 learners, each their own individual learning plan that respects the way they like to learn, the kind of job they have, and exactly where they are in their career. Okay. And I have one last piece. Oh, that was the last piece. I want to hand it over to Mr. Carver. Thanks for coming up. You might want to hold on to this just in case.

Yeah.

Turn it off.

Good job.

So roughly three and a half years ago, I'd inspire Nashville. We reposted our first ham ever, $25 billion. You know, we're coming here today updating that to $40 billion. And that's driving by organic growth into the government space and inorganic expansion into skills intelligence. When we look at corporate learning, it's really split into two different pies. It's the internal audience, onboarding, compliance, talent development, sales enablement, and it's the external audience. It's customer academies, whether it's monetized, unmonetized. It's franchisee workers. It's partners. It's memberships, you know, memberships and not-for-profits is the word I'm looking for. And, you know, from a TAM perspective, slightly less than 40% is internal audience, Slightly more than 60% is external. Let me walk you through why the external opportunity is so large. I'm going to give you four examples of real Docebo customers, how they use the Docebo platform. First two are non-traditional corporate entities. First one is a large sports organization in the U.S. They use Docebo to train their internal employees, referees, parents, everyone in youth hockey. They have 1,000 internal employees. whereas they train 100,000 annual users on Docebo. Their external audience is 90 times the size of their internal audience. Another non-traditional, independent regulatory body that monitors all the brokers in the U.S., they have 7,500 employees, and they're training 650,000 external audiences. 85 times the size. Now, let's go to some more traditional corporate entities, Databricks, AdInspire, long-time customer of Docebo. Finding out their internal audience was a little bit harder. They're a private company. I asked Claude three different times, got three widely different answers, so they have anywhere between 5,000 to 25,000 employees. I know it's large. They train 1.5 million users annually on Docebo. That's about 65 times the size of their internal audience on the upper end. They also have one of the most successful unmonetized and monetized customer academy on Docebo. A new customer we signed in Q1, Fortune 100 company, can't name the logo, so I'm going to say global technology infrastructure company. They're using Docebo just for a partner use case, 100,000 users. More than 50% of their revenues is serviced through partners. If you think about a manufacturing company, you have the company handling part A, part B, part C. They have partners in over 90 different countries. They're using Docebo for the certification of their partners, three different levels. As you move up through training, you get better discounts. This is just one external use case. We don't have the internal. We don't have their customer academy, large expansion opportunities. This is why we're so excited about the growth in corporate learning. This is why we're so excited about the growth of the external opportunity. And where Docebo plays really well is when we're doing hybrid use case, combining the internal plus external. From a U.S. scout perspective, we updated the TAM to $3 billion. And that's really just revised data on the updated employee and contract accounts, multiplied times the average realized sale price. Very simple calculation. There's three reasons why we're just really excited about the government sector. Number one, we have the right partners, from Deloitte to Kerasoft, even niche learning partners such as eSkills. Number two, we're seeing the pipeline. Three quarters in a row. We talked about it in Q3. We talked about it in Q4. In Q1, our pipeline continues to exceed expectations. We have the right pipeline. Now it's on us to execute. Third, we're really just leveraging the innovation we've been bringing to the corporate learning segment for the past two decades into the government sector, and the government sector continues to be the least competitive market we've planned. From a skills perspective, this is the first time we're showing TAM post-acquisition of 365, and it's third-party validated sources on three different use cases, skills intelligence, internal mobility, and talent marketplace. The best thing about 365 is that it's already an enterprise-grade tool. There's already large enterprises such as SMCF, Craig's Agricol. These are companies with hundreds of thousands of employees, multiple different geographies using 365. It is an enterprise-grade tool that's ready to sell now. And I'm pleased to say that 71 days after the acquisition, in Q1, we cross-sold 365 to a Docebo customer. 71 days in, and our acquisition thesis is already playing out. We're not only expanding our TAM, we're also expanding our GTM. And you may look at this slide and say, you know, hey, Docebo, you've been talking about the enterprise space forever. You know, you were in there in 2021. And my answer would be, you're right, but you're also wrong. In 2021, we had eight We had roughly eight headcount in the enterprise space. That's a combination of count executives, count managers, and a manager of the team. Conveniently enough, every one of those sellers was previously mid-market or commercial sellers at Docebo. We treated the enterprise space like we treated mid-market and commercial. We had no different motion. We had no partner network. Zoom forward to today, we have 25 headcount, combination, count executives, managers, VPs, managers, we have a completely different motion. 50% of our closed ARR in the enterprise space touched a partner in C1. We have a completely different partner network. From a skills perspective, we inherited a standalone selling team as part of 365. We're going to continue to sell 365 to any organizations, no matter what LMS they have. If they have SAP, if they have Workday, we're going to go in there and sell 365. Now, there's been one investor question over the past four years that we just haven't had a great response to. And that question is, when is ARR going to stop decelerating? You know, we're pleased to be here today to say 2026 is that year. You know, we showed the acceleration in Q1. You know, understand there's acquired ARR in there. But when we look out to Q4, no matter if you look at with acquired ARR or without, we're going to be accelerating ARR. We're seeing it in the business. We're seeing it in the levers. We're seeing it in the expanded TAM. Everything's in place to accelerate ARR. Another thing is that, you know, when I compare Docebo today to Docebo 12 months ago, we're significantly de-risked. In Q1 of last year, we had AWS at 2.8% of our error. We had Dayforce over 9%. Today, that AWS use case is at zero, and Dayforce is slightly more than 3%. Our top 10 customers excluded in Dayforce is less than 8% of our revenue. Our customer base is significantly diverse. In 2026, we simply have a larger CAM, We have a larger GTM motion, and that gives us confidence that ARR is going to accelerate. If there's one number on this slide that I want you guys to take away, you know, we've talked about enterprise, why we're going to the enterprise, and the one number that we've never shown is when we compare our NRR for customers who pay us $100K or more compared to $50K or less, there's a 9 percentage point difference in our NRR. That's even including AWS, Thomson Reuters in the past two years. If we exclude that, that number is even better. If we look at that on a five-year basis, it's even better. But, you know, we have small numbers, so I didn't give us credit for the full five years. But naturally, as our business becomes more enterprise-focused, you are going to see NRR move up. We're also still early days in the enterprise space. We only have 524 logos paid us over 100K. We're only 5% penetrated in the Fortune 1000. We still have a large room to grow in the enterprise and compound growth. From an EBITDA perspective, we've gone from 8% EBITDA in 2021 to slightly over 20% forecasted for 2026. This is really just a testament to the sustainability of our business model. We've never sacrificed our growth levers to get here. It's been methodical. It's been deliberate. It's been a stepping increase in EBITDA every year. And, you know, we're not only disciplined from a spend perspective, but we're disciplined from a dilution perspective. You know, this morning I was doing some last-minute research, changing some slides, driving my friend Mike crazy over here. And I was looking at some research from an analyst in this room. Thank you, Josh. His firm looks at roughly 80 different SaaS companies, posts all these different SaaS metrics. And conveniently enough, they had stock-based comp as a percentage of revenue. I sorted that data from largest to smallest. And Docebo was at the bottom end of the 80 names. There wasn't a single name below Docebo. When we talk about best in class from stock-based comp, there is just not another company you can find at our scale. It just doesn't exist. We're not only doing that, we're decreasing our share count. 7.2 million shares decreased over this time period. $277 million returned to shareholders via buybacks. And we're going to continue to buy back shares of these valuations. You know, this is old news by now. eight hours old. I'm not going to talk too much about it, but you know, the thing I love the most about this being raised is that it's not because day force revenues were higher than we expected. It's not because we magically acquired more error than we expected. It's not because FX benefited us. It actually hurt our guide by about a million bucks. It's because our core business accelerated Q1, you know, On the conference call, you asked Celestia and I, what's it going to take to being raised? What's it going to take to raise expectations? And we're very consistent. Enterprise, enterprise, enterprise. And in Q1, our enterprise team delivered. Here's our target operating model. 10% to 15% of subscription revenue. We talked about the growth levers. It's really clear. External use case opportunity. growth in the enterprise, government, product expansion. From an R&D perspective, it's gone down 1% since our last target operating model, and that's really efficiency from AI over a period of time. While we're not seeing that in 2026, you know, in 2026 right now, we're actually seeing costs really shift from headcount to compute. So while we're getting more efficient, we actually need less heads, But that cost has just moved from one bucket to another. Over a period of time, we do expect compute to come down. It's realized some savings in R&D, but not in 2026. G&A is unchanged. You know, our track record speaks for itself in G&A. We've come down from, in 2021, we were 27% of revenue in G&A. In 2025, we were 14. If you just use the midpoint, we still got 4% of leverage. just through G&A and EBITDA without sacrificing our growth levers. Sales and marketing is actually the biggest change since our last target operating model. You know, we previously had 28% to 32%. We're down to 26% to 28%. What are we seeing? We're seeing improved performance from a corporate perspective. And, you know, there's certain areas within sales and marketing that will certainly benefit from AI. You know, we're talking about more of the rev ops, sales enablement, you know, the more kind of the groups that need to scale up with the quarter carriers. You know, and I'm just going to leave you guys with a hypothetical but a very realistic scenario. If you take our 2026 revenue guide and then you use the bottom end of our subscription revenue growth, 10% for the next two years, so 27, 28. From an expense perspective in 2028, let's assume we get to the top end of all these ranges at an 80% gross margin. My math tells me that's roughly 24% EBITDA margins. We're talking about a business that's generating nearly $80 million of EBITDA in 2028. I'm going to repeat that one more time. This is a business that will be generating nearly $80 million of EBITDA with best-in-class stock-based comp, and a declining share count.

If you would please just give your name and firm.

Thanks for doing this, guys. Matt VanVleet from Cantor. I guess when you talk about not only the growth in headcount going into the enterprise, but maybe walk through some of the mechanics from an operational perspective that are changing. What things are you doing differently to attack the enterprise market, but that can still leverage down to the mid-market and commercial for more of a shared services approach?

So... Brandon mentioned this in his talk track. I would say, operationally speaking, the changes that we need to the enterprise engine are across a few dimensions. By the way, we also have here Mark Costoblo, our CRO, that is implementing a lot of the things I'm talking about. And Mark, feel free to jump in and take the mic if you want to add anything. Let's see if I... I know enough about the enterprise business, okay? Don't be shy. First, to me, it starts with the people. Brandon mentioned it earlier. We were running the enterprise playbook with folks that used to do commercial and mid-market sales. The first thing we did, we were intentional about upgrading our skills profile at all levels. Sellers, managers, directors, all the way up to VPs. The entire organization of enterprise sales has been rehauled in order to get to where we are today. We brought on board people that were experienced enterprise leaders and sellers in the industry already. The majority of people that we brought on board had sold very large deals. We had the cornerstone workdays of the world, and they knew the enterprise game cold. The second thing we did. And this is not a, I would say, a short-term initiative. It's the work of... being very intentional over the past couple of years, we built a very strong partner network to really be involved and be ahead in these accounts. When you want to enter in Google and similar companies, it is undeniable that the Accentures of the world and the Deloittes of the world have a head start because they live inside those companies. They run transformation projects. They run large consulting and RFPs for these companies. And so having established different degrees of partner program with these companies is something that for sure gave us, is giving us a lot of return. The third thing that I would mention is the way we also approach demand generation and the way we speak to the market. I would say our enterprise demand business has been invested in the way we go and approach the top of the funnel on the enterprise side is very different from how we did it in the past. So when you now look at these three levers alone, demand, demand, Partnering, so surrounding accounts with the right partners and the upgraded execution in people, those are three key ingredients that come to mind. Mark, Brandon, Scott, anything else. Sorry, actually no, very important fourth lever. We were very honest with ourselves about what we had to do in the product. to be truly respected from enterprise organizations. We identified, over the past two years, areas of the product that were not acceptable in an enterprise environment, that maybe were not sexy. They were not AI forward, but they needed to be done to satisfy the likes of really large banks, of really large insurance companies, really large healthcare organizations that are running businesses that are so sensitive to regulatory environments where you can't mess up. And so we put a lot of work into that as well.

Thank you. I'll comment on one thing as well. It's almost like we have two directions of innovation, right? There's innovation in the direction of agentic and forward-facing and in the news and all this kind of stuff. And then there's a reason that the legacy providers still exist and are still huge. And so we're kind of growing in two directions where we're closing some of these boring gaps that are absolutely mission critical at the same time that we're growing in the other direction. So it's a good point. But I see them as both innovative just in opposite directions.

Hi, everyone. Ken Wong from Oppenheimer. I don't want to steal your thunder from earnings, but I think one of the big headlines today, you guys pre-announced more positively than expected. In fact, you guys raised kind of above the beat. And, Brandon, you touched on this a little bit, but we'd love, you know, Alessio, you and Brandon, maybe the tag team, like kind of what else under the covers in the quarter were better beyond enterprise? Was it new? Was it the NRR side finally picking up? And then with the macro backdrop, kind of the way it is, again, surprised that you guys were able to kind of raise by more than the beat. What are you seeing in the business that gives you the confidence to go ahead and lift those numbers? That's a great question.

You know, certainly, you know, when we looked at our guide for 2026, we already embedded assumptions that mid-market was going to continue to improve. And You know, they already had three quarters of great quarters in a row, and we just – we took that. So in Q1, mid-market had a good quarter, but it wasn't the reason we raised our guidance. It was really the enterprise motion. And within enterprise, we actually had $2 million-plus expansions. One was with what I mentioned, the regulatory body for brokers – We initially had the internal use case, one in Q4. We did such a great job, you know, during the pre-sale motion and during the implementation that we self-sourced the external use case opportunity. And at that point in time, when we're working the internal use case, we're actually working two different use cases with two different departments at the same time. You know, the second expansion was in the healthcare space where we owned a smaller subsidiary of that company, and we ended up in, you know, the top dog, you know, the top entity. So, you know, obviously when we talked about our guidance, we initially put out we assumed no million-dollar-plus new wins or expansions. We had two this quarter. And, you know, we not only saw a great Q1, we saw a great Q1 pipeline performance in the enterprise space. So we're not only taking the Q1 and raising guide just for the Q1B. We're raising guidance throughout the year to improve the visibility in the enterprise space.

And as far as the latter part of the question, Ken, I'd say there are a few things that make us realistically optimistic. The first one is... the realization that we have matured a leadership team across the organization that is ready, ramped, understands the market, and has brought in the right leaders below them, and it's creating the osmotic conditions for performance to persist. The second one is more back in the numbers. Brandon stole my thunder a bit, but I will be more emphatic. We continue to see sustained pipeline performance, not only in the enterprise space, but also in other segments where we've historically been present, like mid-market, our bread and butter, where we continue to perform at very strong levels. But we've been working to improve the performance of our international business as well, that in the past couple of years has been choppy, to say the least. And we are seeing very strong momentum even there. Connects back to the comment I made about our leadership. And finally, I think the momentum... on top of the signals of pipeline that exist in product, our ability to ship at a very accelerated pace, our ability to be here announcing something like 20 plus meaningful evolutions of our product between core and highly innovative AI forward. There is a lot of material to go back to our existing customers and build these account plans and expand upon them. Fifth, competitive landscape. We are seeing a significant influx in sheer head-to-head wins with the usual suspects that have a large market share. You know the companies that I'm referring to. And, you know, we at Socebo have had always very clear ideas about how we plan to grow the business, doing things that benefit our customers. One of those things is not getting distracted by, for example, acquiring competing assets. We could have done that a number of times. We have chosen not to do it. Why? Because we believe that creating that magic closed loop that I showed earlier, where the convergence of learning Knowledge and skills get together in a unique solution. That's what is going to make us win the long game. We're seeing that playing out right now. The companies that we compete with are either in the learning segment that we saw or in the skill segment that we saw. And we win on sheer capabilities. And I'm referring even to the more modern ones that have been acquired by larger assets like Workday. We're very excited. Across all fronts, the signals are green, and so we're bullish.

Hey, Ryan McDonald of Needham. As we think about the 10% to 15% sort of target model growth rate over the next few years here, how should we think about what the products are that are driving those growth rates? Because it was interesting to see in the keynote today, it feels like the market's sort of in two different spots. You know, customers are very excited about blocking and tackling core improvements and may be more skeptical or inquisitive about brand new agentic workflows and ways to use the platform. And so where do you think that growth is driven from, sort of core versus some of the new, and how do you bridge the basic customers to go from core to some of the new function?

When we go into an enterprise, Ryan, everyone, we, like Brandon described this very well earlier, We have a number of use cases that we address, whether they are one of the few internal use cases or one of the few external use cases. And all these use cases relate to different capabilities and products and pains, frankly, that customers have. Today, the biggest contributor to our ARR, without a shadow of a doubt, is our core LMS platform. The ships with, roughly, if I recall correctly, there's about 20 modules or so that are attached to it. That, supposedly, is going to be, to continue to be our major driver of growth. For the time being. The acquisition of, but, but, the acquisition of the Skills Intelligence Platform. gives us an opportunity to differentiate ourselves and compete in deals where, despite having such a rich platform feature, we would have not been able to play. There is a very large organization today at Inspire. It is a prospect and it's a big investment group, which I will not name for privacy purposes. And when we started talking to them, you know, everybody in this room would know their brand name. They were very interested in our capabilities, but when they identified the fact that we were light on the skills element, I would say we were about to lose that race. We were about to be dismissed. And we're talking about the potential of, you know, very significant certainly top 20 ARR in the company. And so, more than top contributors to growth, the way we think about it is, how do we create the right product mix to serve these organizations that are going to have multiple use cases and needs that are very articulated? And so, if the LMS is, say, in a $2 million deal, $1.6 million, but there's $200,000 of skills. Not having the possibility to position skills would make you lose the possibility to win the $1.6 million of LMS. So that's more how we think about it. It's less about having a product that wins the race and more about having a platform that gets together and really positions us uniquely against anybody in the competition in the target market. Because it's also a matter of choices. While we're choosing to do all of the above, we're also choosing and telling ourselves that the organizations that are looking to spend $10,000, $20,000, $30,000 over time are not going to be our ideal customer profile anymore. Because we're designing the product, the company, the strategy, the PS, the partners around a different type of organization. And that's why we have great partners that take care of them.

I'll just add quickly, you know, from the AI perspective, with prospects, you know, they're coming to Docebo and they're asking what we're doing with AI without really knowing what they want to do. They want to know we're thinking about MCP. They want to know what's coming. When we ask them, how are you planning on using Docebo through MCP, they don't actually know the answer. They just want to make sure we're thinking about it. They want to make sure we're building agents, even though they don't really know how to use it yet. They want to be a platform that's thinking about the new generation. And, you know, we've been able to demo, we've been able to show. And that's going to result in more LMS revenues. But we're not just thinking about LMS revenues. We're thinking about the whole product suite.

And I'll wrap it up because it's a product question. Yeah, I'll go last though. The interesting thing to think about is the same way that the organizations that we work with are typically mixed in between internal, external, hybrid, the customers that are happy with us and stickiest are using us for the most use cases. Similarly, we want to give our customers the opportunity to grow into their own ambitions. It's easy to think of ourselves as we're innovating, we're growing. But like, they want to innovate and grow too. And some of them can't innovate and grow in the direction of AI right now. So guess what they're doing? They're becoming skills-based organizations, right? They're innovating and growing at a human level. It's almost like a dichotomy. Some of them say, I'm going AI. Some of them say, invest in our people. And like, we want to be there in the situation where an organization says, no, we are really the all-in company. We're going to do all of this. And so, yes, we have that offering. But it is important to note that You know, we'll sell, we'll happily sell our product to an organization that's doing compliance, to an organization that's doing skills and learning, and, of course, to one that's doing an AI-forward agentic future. So it's a bit of we want to make sure that we have what's necessary on the table, but also respect our own customers' goals and ambitions because they sometimes go in skills versus AI in different directions.

Hi, Erin Kyle, CIBC. Just on the guidance, how can we think about some of the other factors that are impacting the guide this year? So if I think about 365 talents, Brandon, you talked about the fact that you cross-sold the solution about 71 days into the quarter. So maybe if you can speak to whether that acquisition has been performing in line with your original expectations and kind of the ARR that you expected to be added when you acquired it back in January. And then maybe on the flip side of that as well for day four. You noted in the press release this morning you're expecting the ARR percentage to be around 3%, 3.2 I think was the exact number. And I think in past quarters we had talked about day force maybe being anywhere between 3% to 4.5% by the end of 2026. So it seems like it's now kind of below those old expectations. So maybe you can speak to where you expect it to be at the end of 2026 and if it does turn faster than expected, what the base business needs to look like to continue to offset that.

Yeah, from a 365 talent, we continue to expect the revenues to be exactly as we previously announced when we acquired the acquisition. So that's roughly $9 million of revenues. We acquired this asset with the expectation to sell it right away. Did we expect 71 days versus 90 days? Maybe we sold it a little faster than we expected to an enterprise customer, but we still have aggressive targets that we need to hit. in order to actually just hit that revenue target, revenue target. From a day force perspective, you know, listen, you know, ARR is turning slightly faster than we expected this quarter. But as I mentioned previously, the actual revenue amount, we're still expected to come roughly in that range that I previously disclosed. If you think about it, the only thing that's changed is that the revenue base is higher. So the amount of revenue contribution as a percentage will be slightly lower. But the actual absolute dollar number is the exact same as we previously expected. So really the only thing that is changing in my guide is the core business improvement.

Hi there, Kevin Christian out in Scotiabank. Just as you're moving from an LMS into a broader learning management platform, learning platform, are you seeing the point of contact in the company, the customer that you're talking to, changing? It might have been an L&D administrator before, and now it's maybe moving into the CEO role, maybe for LSEO.

not just as a byproduct of our product innovation or product evolution and product growth, but also as a byproduct of our growth in the enterprise segment. Even without the most recent announced capabilities, when we sell into companies with 50, 60, 70 thousand employees, we don't have anymore just learning as a buyer persona. The CIO office is very involved. The compliance office is very involved. And this becomes more true, and they take more of a driving seat, we've seen in certain industries. I would say that, for instance, in highly regulated industries, take, again, banking, FinServ, and healthcare as examples, the learning and development teams are effectively our functional champions. But then we have other buyers from the IT and risk teams that are really the ones where we have to do the selling and the demonstrating. What demonstrating? We have to demonstrate that we can adhere with their complex compliance requirements. Whatever those are depending on the industry. Every industry has a different subset. We're learning a lot in that regard. And by the way, You know, you can expect a Docebo over the next couple of years that becomes increasingly more sophisticated in these very complex to navigate compliance-driven environments. And why is that? It's because the barrier to entrance in those industries is pretty high, and it takes resources, which we have, and time, which we've had. And so our win rate in things like pharma, health care, life sciences, financial services is improving significantly. There's a good case to be made about us doubling down on these regulated industries where we have increased right to win.

Hey, it's Gavin from ATB Coremark. Just given the pace of innovation, some of the customers I chatted with this morning were a bit intimidated by the change management. Maybe you can just talk about customer success and how that's going to evolve to help them through that journey and then also kind of uncover the upsell motions.

Look, I think my friend Mark is getting bored. He's a chatty one, and he actually has been leading our efforts in customer success for quite a bit of time. One comment, though, before I pass in the microphone and get him going, I want to address the topic of customers being – I don't think you said scared, but infuriated, I think that's the word you used, about all the pace of innovation. You know, one of the things that we realize as we bring this innovation to the table is two things. You know, if you were in the keynote, we spoke about the story of Mary. And Mary, this fictitious L&D leader, is really actually a representation of an analysis that we ran with AI in the company about a lot of the things that we get from a gong call. We analyze patterns, and the pattern is Mary's understaffed, and the pattern is Mary's overwhelmed, and the pattern is Mary has got to do a lot of, she has to bounce between strategy and getting stuff done every day. As a result, she struggles in strategic because she's brought down a lot to getting things done. Oh my gosh, this new cohort is starting. We need to build a course. I need to oversee the course, even if somebody's building it. So, our average buyer struggles to act strategically. And some of the work that we presented today takes away the manual work that they're used to doing. So they have a yin and yang about our recent innovation. On one end, they're really excited about it. On the other end, they're like, okay, you know, I actually spend 50% of my time creating courses, and I just saw an agent creating an awesome course outline that would usually take me 30 minutes to build. So that gives them a pause. And that intimidated feeling that you may have seen is also the result of that. It's a realization that the technology, AI innovation, is brought forward in the industry, and we're here making it available to them. And now they need to figure out what to do with it. It is a change management. It is a cultural progress in the industry that, frankly, is unprecedented. They haven't seen anything like this before. On the customer success front, we're doing amazing things, particularly because we believe that one of the keys to success is not only becoming stronger in product and selling more and having a better pipeline, but increasing stickiness. Increasing stickiness is through product strategy, but also through customer success. Mark, is that okay if I pass you the mic? Let's do it.

I was very lucky in my career to work for a customer success platform whose job it was to help retain and expand customers. And in that time, about 18 months, I was able to meet with some of the best post-sales minds on the planet on a regular basis and glean from them what I determined were what makes a great post-sales process, what makes great customer success. And there's three things that I found are required that we are becoming best of breed in. One of them is focus. We remove the commercial conversation from our CSM so they can focus on creating proactive value for our customers. And rather than haggling over a $10,000 difference in pricing on a renewal, which we have our commercial reps do that, we have them beat our customers over the head with how we can help create more value and solve the business problems they have. So focus is a first. The second is data. We now have very, through our partnership with Scott and Ricardo, we now have a slice of data that allows us to understand exactly how our customers are using the platform so that we can create those proactive warning signals of churnable behavior so that we can cut them off before they get too deeply rooted. And then the last is process. This was surprising to me. But in my first 50 days at my previous company, I talked to 90 customers that were all CS professionals, and I asked them, what methodology do you use for CS? Do you know what they all said? There is no CS methodology. Well, that means that with the lack of process, reps don't know what to do. So we are instituting a best-in-class process of how do you create a repeatable, predictable way to drive customers value for our customers so that they don't think about churning. And so that creates like a stage-based process that we can run multiple cycles on and measure how fast are we driving additional value for our customers. So those are three of the foundational changes that we're making in customer success that should really help us with retention and even maybe more importantly expansion because by default expansion means that they're happy enough to renew. Not that chatty. So chatty.

I love it.

Yes, thanks for this opportunity. This is John Shaw from TD Cowen. Alyssa, you mentioned at the beginning of the presentation that there's going to be a future where your AI agent is going to be working with agents from other vendors. So maybe from a customer's perspective, could you help us understand what's going to happen to your customer given multiple agents from various vendors? Do you see a future of consolidation, and do you see the table consolidating? Thanks.

You're right, I said that. And that's the result of what we're seeing in the market. There's a definite case for agents involved by other agents and agent interoperability platforms. With that said, I think we are early in that game. And I think first we need to ensure that we take step one. Step one is enabling our customers, the people across our customers, the humans, to use these agents and make the best of it, before we make the agents available to other agents. Now, the way we are exposing our agents and our MCP leads itself to enabling other platforms to connecting with Docebo. Riccardo demoed it earlier today. If there is an agent that invokes Docebo VMCP, one of the things that we will be able to control, because we control the data, is what do we want other agents to do with our agents? That is something that we haven't established yet. But certainly, you know, you can expect a world in which we make our data matter. Our skilled graph of our employees, of our customers, the learning history, all the data primitives that are really valuable. are going to be part of a value program that not everybody can just access without some form of, you know, consumption model or outcome model in favor of the stable. That is pretty simple to envision. The technology of implication of agents to agents, I think, we're a little early.

Josh Baer with Morgan Stanley. Alessio, a couple questions ago you mentioned you brought up efficiency and content creation. I wanted to ask one on broader customer ROI. In the past, you've measured that through sales enablement, some revenue metrics or external training opportunity. Customers are always focused on engagement, course completion. How does AI and skilling change the way that customers are thinking about ROI? What are you talking to in sales processes? What metrics are you tracking along the way? And ultimately, how do you frame Docebo's ROI today?

I think one of the biggest dilemmas in the learning and LMS industry has always been the... Truthful ROI calculators. Particularly true on the internal use case side. Easier to calculate ROI the closer you get to the revenue stream. So, you're right in mentioning sales enablement, customer education, anything that touches pro-serve or support because they are very close to an outcome and that outcome has a dollar attached in terms of revenue or in terms of cost. When it comes to internal learning, that ROI is a lot more ambiguous and difficult to establish, particularly related to employee training. You know, technical sales enablement is an internal use case. But it's a lot easier to quantify an ROI based on the productivity that you gain with sellers, accelerating the ramp time, for instance, getting them to first deal, getting them to quota. Every enterprise seller has specific playbooks. that touch on value, depending on the use case of the cell. And in the Chebo, we have a practice, which we actually call value engineering, goes by the name of VE, where when you approach us, and we go through the discovery phase, i.e. we learn about your business, and we ask questions about what you want to solve, then all that data is funneled through a ROI calculator for that one use case. And so when we go and issue a proposal to that organization, it doesn't ship with just the price. It usually aims to ask you to the customer a question, why wouldn't you do it now? And what is the cost of not doing this right now? So instead of just sending an order form, we send a question, which is based on our data and based on the fact that you told us, this should be an operator. And the customers that are the most successful with it are the ones that use that to form the internal use case for purchasing it. That is when we are able to influence the way the decision is actually framed internally using our own data. Because not only you're saving your point of contact a bunch of research work, you're actually influencing the way they go to talk about the problem they're going to solve. We've seen this play out pretty nicely. I don't know if Brandon and Scott have anything more to add.

Yeah, I'll comment on this as well. I helped build some of the initial value engineering capabilities a couple years ago when we really started doubling down on this. And what's interesting to see is that as we move into more of the skill side of things, the situations in which we can save our customers money and prevent risk and do these kind of things have actually grown significantly. The cost of internal mobility at large organizations, which is something that we now handle natively, is extreme in some cases, right? If you have 30% churn in a certain type of organization, certain type of industry, you being able to actually prove that we had, through the system that we now control, 10,000 people apply to and achieve a new role within the company, that's not pixie dust that we're saying, oh yeah, we were going to save this money. This is real 10,000 people. If you find your cost of Hiring a new person is 10% to 15% of the headcount, those kind of things. So we can start pointing at real numbers, especially based on some of the new things that we have access to. And, you know, sales use cases is obviously the easiest one. But you can imagine now that we can connect to all of these different sources of truth and we can understand, for example, a salesperson's – we're not just looking at did they get onboarded faster. We can now look at things like what's their discount rate compared to their peers? And does that imply that their negotiation skill is weaker than their peers? And if we close that negotiation skill gap, can we see their discount rate improve? Those are very outcome-driven, outcome-based things that we now own the data for versus saying, hey, L&D professional, hopefully you're calculating this on the other side because that doesn't always happen. So the closer we can get to giving them the real numbers, that's really quite powerful.

Logan Littlehog from Craig Helm. Thanks, guys. I think the number you gave this morning was 50% more product shift in Q1 if I caught that right. And obviously, Scott, you talked about kind of a big product roadmap coming throughout 2026. So I'm curious if you could talk about the go-to-market, whether it be SIs, resellers, the internal team, and just kind of how you ensure that the pace of internal innovation is showing up in all those different motions as you go into new enterprise logos, federal, and just kind of trying to show up in a unified way.

interesting question. I mean, that's partially a Mark question here, but the good news is we're a learning platform, so we have very good internal education as we ship new things. One of the core things that we're trying to do is make sure that the themes on which we're building against are consistent and easily understood, not just by the market, but by our internal people. And if I'm understanding your question, it's more like, how do you make sure that people are ready to accept the pace of change? Customers have to be ready, but also the GTM function has to be ready. And One of the reasons that I'm really excited to work with Mark is because, you know, it's not product versus sales at this point. It's, you know, we're involved at every step of the building of the roadmap and execution. And so fundamentally, we're using our own product internally. We've become customer zero on basically all of our products. Alessio said in the keynote earlier, we don't just build for our customers. We run our own company on it. And so we're able to educate you know, and enable our team way faster than we've ever been able to. And fundamentally, we're building these themes forward so people understand that it's a small iteration, you know, all of these kind of ILT upgrades. We're able to deliver that message and it makes it a lot more simple for people to understand. So it's pretty straightforward to go there. Yeah.

I recently asked a question to Mark on certain aspects of how far along we are on our enablement and our success of ramp up of knowledge on 365. And I got a really impressive response back recapping what was going on. And so I'm going to pass in the microphone because it kind of makes it feel real and it speaks very much to the question you just asked.

I've been very lucky also in my career to work for one of the fastest growing GTM tech companies on the planet. And partly one of the big challenges of that software was the amount of behavioral change required by our customers that often led to people not adopting the platform. And so I've learned through doing that and through helping some other companies and mentorships and whatnot that usually the biggest reason that GTM fails is because we just try to shove too much down their throat too fast and they can't digest it, can't change behavior fast enough. And then projects and initiatives are just abandoned because it doesn't seem like they're working. because there's just too much for the reps to do. So we have an extremely regimented enablement framework that's put in place. I've spoke about it many, many times on podcasts, on webinars and whatnot. You can go read about it if you just Google Sales Enablement Matrix and Mark Cosiglow. But basically, sales enablement to me is about two dynamics. One, the capability that we want and expect from the reps. Do we want them to just be aware of something? Do we want them to be competent? Or do we want them to be masters? That determines how we certify them in the area and also how big the program that we need to build an enablement is. And then another axis that we use is cognitive load. Is it easy change? you know, clicking another button in Salesforce, or is it a very difficult change? Like how do they do discovery? That's a very difficult high cognitive load change. We assign point values to projects once we score them on those two dynamics, and then my enablement team only has so many points in a quarter that they can give for each role, and therefore what we do is we say once you get to that level, the leader has to make a tradeoff decision. Do they want X or Y? Because if we do X and Y, both fail. If we choose X or Y, one of them will work. And so my field teams have three kind of taskmasters, product with new product innovation, marketing with the awesome and innovative stuff that Kyle is doing with campaigns and whatnot, and then my sales operational stuff that I'm doing and the changes I'm making. That's a lot of stuff coming out of REF, and so we have to have a very tailored way of doing it. So what Alessio is talking about specifically is he asked, hey, how are we doing in transitioning? So we created a very, I think, really great plan when we took over 365 of transitioning. having a period where we shared and they helped and were kind of like a support service on top of our sales team. And then we enabled our sales team to a certain degree. And now at the end of this month, we just go to just we're doing it ourselves. We have what we call an overlay rep, which I'm sure you're familiar with that concept, who's a specialist in that specific product. But when we do that, we now lose that support system of the 365 reps. So how do we make sure that goes on? Well, one, we've done extensive product knowledge on 365. Two, we then said to each of our managers, what do you need? And they say, we don't feel like we're experts on skills-based organizations. So we did a general overall knowledge of how do I just talk like an expert on skills and Then we grade that by having three live call recordings scored by each manager for each rep that shows how good are you coming along in that area. Then we have a measurement that we are doing right now where we look at call recordings in 365 ICP accounts. We measure exactly what percentage of available accounts are we having skills-based conversations and where are our reps avoiding it. Happy to report 84% of all of our ICP that can buy skills right now, buy 365, is being talked about, skills in 365 are being talked about in those conversations, which I think is fantastic. super strong after less than 90 days post acquisition uh that's they just found out about it like 90 days ago that's pretty insane and uh and then what we do is uh then we have uh multiple overlays because we've actually seen uh some on par demand with what we expected so we have like two um overlays and we're hiring a third to help with the specialization and the capacity there so That's just an example of, you know, you can – listen, I've been in companies where I've acquired stuff, and it just kind of gets thrown over the fence, and then, like, figure out how to sell it. And I remember as a seller that happening. I didn't like it. I remember it happening as a VP. I don't like it. As a CRO, I have a little bit more control and a very supported executive team that allowed us to create a really awesome plan, and I think we're working the plan, and As Brandon said, in 71 days to do a six-figure deal on an enterprise customer is pretty awesome.

Yeah.

From a FedRAMP perspective, I think we're really looking at the government sector in two avenues. Number one is the Fed. Number two is SLEDD. SLED is the segment we've been in for roughly probably 18 months, and we're seeing strong demand. We're showing the logos every quarter, and they actually have a different seasonality than Fed. Q2, June 30th, is the most space where the department ends for the budget cycles. And we are actually seeing from a demand perspective in SLED, you know, Q2 will have a contribution from SLED more outsized than Q1, Q4 of last year. And from a Fed perspective, we're seeing the pipeline. You know, the one thing that I'd say is from a Fed perspective, they tend to be less units at higher value. So it's really up to us to execute and hit those units. You know, they certainly will happen on September 30th. So if we think about revenue contribution for 2026, we're really only talking about 90 days of revenue contribution. So from a SLED perspective, it's going to contribute to 2026. It's going to contribute to revenue growth. From a FED perspective, While it could contribute to ARR, it's more meaningful from a revenue perspective in 2027.

We acquired the Zive team because it was a very natural complement to what we were already in the process of building. As I said earlier, Rob, you should think about it in the context of accelerating roadmap. and talent acquisition. Pierce and team, Pierce joins Docebo with a handful of highly sophisticated AI native developers. And what they built is something really strong. They have essentially built an AI knowledge platform supported by agents, designed for the enterprise-grade customer. To simplify it, Zyve built a business that is not too dissimilar in its principles from something like Lean that you guys may be familiar with. In our context, we're not a company that goes and sells as a primary business buyer to the CIO office for IT horizontal projects. That is what, you know, a knowledge management business intended in its own category would be. That is what, you know, we're gluing customers ourselves at the check, but we understand that business really well. Our CIO is here, and he can tell you everything about that. Our intent with Zive was, first, to have a technology that is proven to help us accelerate our ability to expand upon the need that we heard from customers, that the knowledge, the assets, the know-how of the company existed well beyond the database of the LMS. And that was something that we had begun to tackle with our project Harmony. But we saw an opportunity at the right cost compromise to accelerate that implementation. And the second thing has been the agentic play. Zive gives us, thanks to the enterprise knowledge module that we're inheriting, the opportunity to build very strong agents with an agentic store that already is part of it.

Just want to add on Zive, just to be completely transparent, the purchase price was immaterial, which is why we haven't announced it, and it's not going to contribute any revenues to 2026. We're essentially taking their existing product, we're shutting it down, and we're having them focus solely on embedding their technology within Docebo. We have time for one more question.

Yes, thank you, Richard C. with National Bank. In some of those demos this morning, it sort of looks like you guys butt up against kind of HR a little bit. So if you kind of look at L&D or the system of record, is there a path to being system of record for HR in a broader context as a software company? So is there what? Human resources, like, you know, back office systems for HR?

So... If you look at a product roadmap, if you think about skills being a critical part of the story integrated with learning, there certainly is an adjacency with the HR buyer. In fact, Mark was mentioning enablement. One of the... critical aspects of the enablement is how to talk to a chief people officer, a chief talent officer on the topic of skills. It is not something that we were doing before. But learning in most enterprises runs into the HR business. So in that regard, I don't believe that we are in any way, aiming to enter the HR camp. But I would agree that the skills capabilities get us one step closer to the HR by your persona. No doubt about that. We are not intentional about going toward the HR route, if that was what you were asking, Richard. If you think about our roadmap, there's a many other developments that go actually in an opposite direction. Think about the innovations also on the e-commerce side and the desire to amplify that customer experience business and the business of transforming the Cebo in the revenue engine for our customers. But the internal use cases are big. And the more we work with these mega enterprises, like I said before, in order to unlock the magic sauce. And the magic sauce for us is what? internal plus external training to unlock the viability of winning the full share of wallet that is in the customer, we have to have this proficiency and these capabilities that range from the more HR learning side all the way down to the CX customer experience side.

Thank you, everyone, for coming. We'll be around for Q&A after as well.