Right. So my name is Josh Baer. I'm a Software Equity Research Analyst here at Morgan Stanley, and I cover both, Software, SaaS, and EdTech stocks. Before we get kicked off, I want to take care of some disclosures. So on our end, for important disclosures, please see the Morgan Stanley Research Disclosure website at www.morganstanley.com/researchdisclosures. And, before we get to the rest of the introductions, I want to turn it over to Mike McCarthy, Head of Investor Relations at Docebo, for some words on his end.
Thanks, Josh. Before we begin, Docebo would like to remind listeners that certain information discussed this morning may be forward-looking in nature. Such forward-looking information reflects the company's current views with respect to future events. Any such information is subject to risks, uncertainties, and assumptions that could cause actual results to differ materially from those projected in the forward-looking statements. For more information on these risks, uncertainties, and assumptions relating to forward-looking statements, please refer to Docebo's public filings, which are available on SEDAR and EDGAR. Additionally, I'd like to remind participants on the call that Docebo will be reporting Q4 results before the markets open on Friday, February 23rd. Accordingly, we ask that you focus any questions you might have on this morning's call on the AI discussion. Back to you, Josh.
All right, great. Thanks, Mike. So we're very lucky to have Alessio Artuffo, who is currently President and Chief Operating Officer at Docebo. Alessio, you spent 12 years at Docebo in a variety of senior leadership positions, and starting March first, Alessio will become Interim CEO of Docebo. So welcome, Alessio. Also, we have Giuseppe Tomasello, who is VP of AI at Docebo. Giuseppe was the founder and CEO of Edugo, which was acquired by Docebo last year. So thanks for joining, Alessio and Giuseppe. Also, on the line is Matt Saltzman from our team here, key member of Software and EdTech coverage at Morgan Stanley. Thank you. Good to see you. Thanks for joining. I guess just wanted to start off with some context from our side.
Like, as a software team, and more broadly, Morgan Stanley Research Department, we've written a lot of deep dives and foundational reports on Generative AI. We've mapped a $4 trillion potential impact of Gen AI on enterprises globally, through our proprietary analysis that could lead to $150 billion in incremental software spend in three years, in our view. And we've collaborated globally with all the teams covering education stocks to dig into the opportunity and risks around AI and education sector. And so today, we're focused on the intersection of those three areas: AI, Enterprise, and Education. I'm really thrilled to be here today to moderate the discussion with, with you. So enough from me. Let's hear from the experts, and we will have opportunity for Q&A at the end of the convo.
Giuseppe, I wanted to start with you, and I wanted to level set really, in your view, what exactly is AI in the context of learning, and if you could provide some examples?
Absolutely. So first of all, thank you for giving us the opportunity. We are very excited about the work we're doing in Docebo in AI, so that's a great opportunity to share some of the things that we are actually cooking behind the scenes. So, Generative AI has been, as you mentioned, like one of the big topic of the past year. But really, like, AI in learning has been something that in Docebo we've been investing for many years already. What this has been the huge hype regarding AI recently, or the latest hype, has been thanks to the development of a branch of AI, which is called Generative AI, that really has been powered by a technology of large language models.
And what these large language models doing are basically taking a lot of text, and this text is coming from, for example, all the text available in the Internet, even more from lots of books and libraries of content. And we are able to compress this text into what is like kind of a big zip file. And then basically, what we're doing is that we are prompting these models in order to generate a new language. And the, the language of the models is producing is very accurate. And actually, since the models are producing language, is very accurate, those models have some kind of reasoning capabilities, because in order to produce accurate language, they need to actually reason on what's happening behind the language. And so this really enables us to build a new kind of application for learning.
But the capabilities of AI in itself is not enough. We need to build on top of those foundational capabilities. And really, what I believe is happening is that is the emergence of a new kind of operating system, right? Where basically, large language models are capable of codifying a lot of capabilities. And then, when it comes to building applications that are specific for learning, basically what we need to do is that we need to encode research base and pedagogically sound frameworks in order to steer the large language models, in order to produce language that is actually accurate and is useful for training purposes.
So that has been our focus in Docebo, is to build this layer that is living on top of large language models, that enables us to encode, research-based, pedagogically sound, principles, in order to give us the possibility to produce high-quality content that we know is very, very important for our clients. And so what you can expect, from Docebo, from 2024, is that we are going to create. We already actually have a beta program, and Alessio will talk more about, the release dates. But, what we're going to release is, Generative AI-powered chatbot interface, where instructional designers can have a natural conversation using natural language. And, this, AI system is capable of producing content, that, already useful in order to fill some content training, needs.
Moreover, we're going to create specific templates that are going to be verticalized for different use cases. For example, for sales enablement, onboarding, and other use cases. Therefore, those templates will kind of codify a lot of the heavy lifting that usually structural engineering needs to do, that will be codified within those templates. Just to summarize, what is the future of AI and learning is going to be the integration of a lot of reasoning capabilities, but as well, a new natural language interface, where instructional designers can leverage their creativity in order to let the, the system, the AI system, do all the heavy lifting, and therefore creating very accurate content easily.
Perfect. Great, great overview. You touched on some of this, but in thinking about the lower barriers to entry for around content creation, just given GenAI breakthroughs, like, wanted to dig in more specifically on how Docebo's platform is positioned. You mentioned, you know, you've been researching and you have existing AI offerings out there. So how do you build and expand upon those and incorporate all the latest AI innovation, you know, into your products?
Yeah, absolutely. So, in Docebo, as I mentioned, there is a lot of AI capabilities already embedded in the product, and we are building on top of those existing capabilities. For example, we have a very robust measurement analytic platform that leverage a learner performance to improve content creation. So the fact that we are capable of stacking on top, content creation capabilities on an existing ecosystem is what is giving us, an advantage. Because, it's. You don't get only like a, simple content creation tool. Right now, like, the market is full of new competitors or new, products that are popping up every day of, companies that are capable of, doing content creation.
What is really important is to create a closed loop, a learning ecosystem where the content creation capabilities are plugged into a robust data collection strategies. Therefore, we're capable of creating very accurate content, and this will lead us to having a true personalized learning experience for our customers.
Okay, that's great. Wanna also ask another one on data and sort of thinking about your moat and just broadly competitive differentiation, you know, how are you positioned when thinking about, you know, what you have versus competition out there?
Absolutely. So as I mentioned, that in Docebo, we're using foundational large language models. And I talk about the models because we're not just using one single provider, but we build a flexible architecture that allow us to use multiple foundational models. As well, we are also training our own internal models that are specific for a learning and development use case. And we use data in order to protect and create a moat, really, because Docebo is home of more than 30 million learners across the globe. And this give us a plethora of specific data that are for...
That speaks about the learning history, about the learners in the organization, and therefore, we are capable of leveraging this kind of knowledge and data in order to build applications that are very specific and really give us a competitive advantage and a moat.
That's great. So, what role will people actually play in designing content and sort of managing an organization's learning process, just, you know, considering AI's broadening adoption?
Absolutely. So of course, this is actually a very hot topic right now because everyone is asking: When is AI going to take over my job, right? And, of course, humans still have a very important skills that are not replaceable by artificial intelligence. And, we talk about creativity and the core creativity of humans in not only thinking about innovative ways or innovative, you know, content that can be created, but as well in empathizing with the learners, understanding what are the needs of the humans. This is something that is very important, that needs to be actually human-centric, and, we need to use AI in order to amplify the human capabilities.
This has been our principle in order to design our content capabilities, in order to put the instructional designer at the core, and give the instructional designers tools in order to be able to, you know, automate all the repetitive and time-consuming tasks, for example, from multimedia production or like boring content creation, or even analysis of huge amount of documents and that is available in the company. We're using actually a technique within our system. There is a part of our technology stack, which is called the AI Brain. And really, the AI Brain, what it's doing is encoding a large amount of unstructured data. Those can be, for example, PDF, PowerPoint, even transcripts of sales calls, for example. And we're able to extract the juice, the really the important content that lives inside this data.
That is very important because once you have access to this continuously updated feed of data, we are capable then to generate training content on top of this. So it's very important, the knowledge management component, that is going to become even more important. So instructional designers needs to be able to manage the knowledge that lives in the organization and then use Generative AI in order to produce content that is pedagogically accurate. So it's the engine that we're building in Docebo is transforming this content that exists in the knowledge management into pedagogically sound content and ready to deliver learning for our learners.
I think, Giuseppe, on top of that, I think there's obviously the impact on the instructional designers that are the most well-known actors in the value chain of production of learning content. In the way we think about the paradigm of creating smarter LLMs, we think a lot about empowering also a broader network of individuals, such as, for example, partners, and effectively give an opportunity for highly specialized services, which can you know really give a credit to organizations that are tasked with making the LLM very smart about a certain use case and/or scenario and/or vertical area of competency.
Just like we have at Docebo spent quite a bit of time refining, we've been refining the data input that goes into our sales enablement use case for our Virtual Role Play. You can abstract this concept to various use cases in whichever large corporation that requires it to become really sophisticated, at any given capacity or skill. While doing that and creating sophistication of the AI brain requires a back-end type work that cannot be left to just random ingestion. There needs to be strategy behind it. And so we think about collaboration with large system integrators and giving opportunities not only to our customers for this type of work, but also for intermediaries that can actually help the engine become smart for our customers.
That makes a lot of sense. Wanted to ask how you see the actual function or the role of learning evolving when you just consider broad AI strategies are being developed and implemented into your enterprise customer base and into their tech stack. So, you know, how is the role of learning evolving?
Absolutely. As I mentioned, knowledge management will become an even more important part of the Learning & Development team responsibility to ensure success of a hyper-personalized AI system. So, that is going to play a super important role in the organization. As Alessio said, this is going to be the really core of the strategy. The learning strategy of enterprises is going to be what kind of data and the quality of the data that are provided into the system. So that is going to be foundational for all the rest. But the role of learning will change and is already...
Our customers are already telling us they're adopting, for example, skill-based organizations, where the role of learning is going to be even more important as we move forward, as the world is changing more rapidly, because also because of AI, right? AI is changing the way we are working within the organization. The role of learning in the organization is going to become fundamental, and we all people working in the all organization in the world needs to constantly upskilling or reskilling because the transformation, the person within the organization is going to change role more quick, quicker.
Therefore, the role of our learning development strategy is going to become foundational because it's not, we're moving almost towards like a productivity enhancement tool, because basically, we are giving the possibility to people in the organization to, you know, learn how to use new tools, how to upskill with new skills in the organization. One thing I want to add here is the importance of our backbone technology that is actually the skill tagging and skill mapping. We are capable in Docebo to get any kind of skills ontology of an organization, and then being able to map that to content requirements. This actually is going to be foundational, because organizations are going to be embracing like skills as a foundation for, you know, managing their workforce.
Having a system that, an AI system, is able to understand what are the skills gaps of that organization and automatically generate content in order to fill those skill gaps, is going to be a very important tool for, as we move forward.
Really interesting. In our, in our latest Morgan Stanley CIO survey, Chief Information survey, AI predictably, maybe jumped to the top of the CIO priority list. And we had a question that said basically more than half of CIOs expected to have their first AI projects actually in production by the end of 2024. So I wanted to ask two questions on that. One, I mean, does that sort of timeline for... Like, thinking about your own customer base and some of the technology projects that they're working on, does that make sense to you as far as the timing? And then also, when it comes to learning, like, should we expect a sort of more emphasis or need for learning that coincides with projects actually going into production? Or is it in advance of, or is it lagging?
Like, is there any connection between learning and then actually, like, companies that are bringing AI projects into production?
Well, I think, I think learning projects, whether it's, let's say, incentivized by the opportunities that AI offer or not, are going to become more and more central, even in the, CIO mandate, because, like Giuseppe was alluding to, we are actually moving, from, the concept of, learning intended, as a, knowledge improvement concept to a real productivity gain. So in a lot of ways, we believe that AI, what it will do, it will, further strengthen the priorities that CIOs will give to learning investments. Because, because the impact and the point of value itself of learning is going to increase, as a result of the opportunities that GenAI offers.
In terms of timelines and timing, when I hear that many CIOs predict investing in 2024, this aligns pretty nicely with our plans, because our plans that are around the evolution of certain assets, like the Docebo Shape, as well as the creation of new capabilities, the two rotate around Shape as a platform. But there are distinct capabilities, like a Virtual Role Play management, a capability that Docebo today does not offer. We are talking about, you know, our goal is to start having some high-quality testing with exclusive customers around April.
By September, by our next annual users conference, Docebo Inspire, being live with a marketed offer on both the Shape V2, which is our Shape on steroids, as well as our VRP or Virtual Role Play, the highly specialized, pedagogy-first experience in which an individual can learn more about a certain use case or practice by interacting with an AI agent.
Great. Looking forward to it. Giuseppe, before we shift a little bit and talk more about strategy with Alessio, just, you know, you brought up reskilling and upskilling. It's a theme that we're pretty excited about, and not just for enterprises, but really, governments and their citizens, as well. Anything else that you wanted to add on, just thinking about reskilling and upskilling, and-
Mm-hmm
... and how we can expect AI to be integrated into your platform, to enable your customers to meet their goals?
Absolutely. Like, in recent years, we have been seeing two big changes in Docebo usage trends. More and more businesses are using Docebo for external use cases, and more and more industries are using Docebo in order to execute their education programs. So if we focus on the second trend, so in the education programs, while we see a high level of usage across a broad array of industries, actually, it's very interesting to observe incredible growth in the manufacturing sector. And so, as more and more companies are beginning to ensure their processes, the need for skill matching and reskilling is massive. So they really need to upskill and reskill the workforce. And this comes to play, the AI-powered skill management system.
So I like, like those ones we're building in Docebo. We are helping companies to simplify and streamline how businesses are handling these challenges. When integrated with AI content abilities like those we are releasing in 2024, as Alessio was mentioning about those exciting timelines, organization can combine those systems in order to be nimble and responsive to meet their goals, to retain talent and also develop the new skills that the organization need today.
Got it. All right, Alessio, so you mentioned... I think I heard in the fall, Docebo Shape version two, the Virtual Role Playing. Just on that topic of monetization, I guess it would be helpful maybe to review how you're monetizing AI today, and you know, what to expect from the monetization perspective of those exciting innovation that you just referenced.
Listen, we are in a, in a call in which I know that the monetization is a very critical, component, to many of us, and it is for us at Docebo as well. Absolutely. We are equally focused with monetization. We believe monetization is, a synonym with value, giving value to customers. There is monetization when this monetization, meets the point of value for the customer. So our focus number one, and a prerequisite to monetizing, is creating products that solve real customer problems, and the more expensive the problem, the better. I would say philosophically, this is, the starting point. Then, in terms of what Docebo does today, and, Giuseppe alluded to this, we're not new to AI. This is...
We haven't jumped on the AI bandwagon because everybody started talking about ChatGPT. There was experimentation as well as production of real capabilities in the core product for years. But frankly, monetization in the initial years wasn't the end and the primary goal. The primary goal was to create a system that was smarter and stronger in certain areas that required a ton of manual work otherwise. So really, the first couple of years of AI application for us were about reinforcing that core capabilities. You know, when you use it, sometimes I feel like end users these days take it for granted.
An example of that is semantic search, the ability to search something in the system, whereas in behind the scenes, the AI is stripping out, transcribing the content, outside of PDFs, as well as videos, and allowing deep search in all sorts of assets. This is an incredible productivity gain because before doing that, if you had a certain statement or a keyword or a concept that you wanted to retrieve inside a video, you may as well never have had the opportunity to extract that information. So I would say this is a good example of something that we've built over time, strengthening search capabilities. Giuseppe alluded to another one that is, you know, not as sexy as VRP, but that saves hundreds of hours of work in...
Take a large manufacturer, where you have hypercomplex, and very custom, oftentimes, and legacy, a lot of times, investments that have gone into creating Custom Skills ontologies. This is. I'm referring to examples and experiences with real customers. Years of work with the system integrators to come up with an ontology, and now your job is to work with a system like us, that allows you to reuse that something without having to redo the work. And so our skills ontology management with AI matches an external ontology and allows this on-the-fly translation with an insanely accurate output.
This again, if you remove this capability, you are back at the drawing board when you land in the new LMS, which is a system of record, and potentially you would have to redesign or redo manually your ontology. And we're talking months of work and a lot of money invested. So those are two examples of core capabilities that we have strengthened and that we continue to invest on. Then, other things that we've done over time are automatic skills tagging for content and automatic content tagging. So when you create a learning object or a piece of knowledge in Docebo, the system inspects that piece of knowledge and automatically categorizes it against a taxonomy, so to speak, of learning content. Imagine doing that.
Your average customer may have anywhere between 10,000-100,000 assets, and having to do it manually for every single piece of asset that gets populated in the system, it's a massive undertaking, and the housekeeping of that over time becomes an even more massive undertaking. So everything that I said so far, a lot of words are not even things that we believe are necessarily one-to-one monetizable. The way we monetize from it is by strengthening our core, and as a result, increasing win rates, making customers more productive, which in turn should yield better retention rates. So I would say it's an indirect gain that we get out of injecting AI everywhere it can make our system more competitive. That's first and foremost. Then there's the fun and exciting stuff.
Not that this isn't exciting, but then there's the stuff that we can go out there, and I would say, single isolate as a capability, that has a price tag attached. And the reason why it has a price tag attached, it's because, it's what it is, it's a series of workflows, so to speak, that are highly sophisticated and end up with a business outcome. So, the platform of choice for us to bring this vision to reality is called Docebo Shape. Shape started with ambitious, but more humble goals years ago, by becoming a tool that helps and solves the problem of creating rapidly content up to 50 different languages, and AI helps in this rapid creation and curation, starting from very minimal input.
The output of Shape V1, that's our internal code name. We're going to have to figure out this branding thing as we release things, but we will. The V1 or the initial scope of Shape was to really create static content, video-based slideshow logic, and really a big emphasis was on that, productivity gain in scenarios of multinational companies that had to recreate the same learning object 30 times in 30 different languages, which is a massive undertaking. Shape does it in minutes. Okay? So then what, what's next? How are we going to take Shape from a rapid content creation technology to marrying even further, AI's capabilities? Comes in the picture, Shape V2. Ha! Sorry, I omitted something on V1. We believe V1 is going to have an impact.
It's going to become the answer also to more standard authoring requirements. So, you know, in the content creation world, you have your more regular authoring, the more standard content creation via templating and more static. And then the announcements to that concept are gonna be in a more GenAI chatbot format. We want to increase the adoption of Shape V1, which already has sold nicely, has had beautiful attach rates over the past two years, but we're gonna be including it in one of the tiers of our product, and it's gonna ship with our LMS. And this is a strategic decision because we want also more data to continue to refine our flywheel and our engine, and because we believe that the capabilities of standard authoring in the modern LMS economy tend to be commoditized.
So we need to provide our customers with this capability while reserving the advanced creation of content, chat GenAI focused in this V2 version that we're gonna release in the fall, with early access in April. In more detail, this output, the output from Shape V2 will also support the vertical page outputs, in addition to our current format, which is more slide-based, and this comes from kind of overwhelming demand from the market. From a monetization standpoint, our views are that annual licensing models will support our commercialization. So very similar to how we price today, our LMS itself, Shape V2, will be sold on a price logic that is gonna be annual, and very likely with some logic around usage and users.
So then, there is an additional layer to the Shape platform, that Giuseppe alluded to, and that is changing the way... We believe that there is an opportunity to interact with the learners in a different way. We believe that the so-called typical asynchronous learning object experience, right? The one in which we're in front of a slide that we can click, click, click, eventually gets lost on folks, that certain use cases are not very apt to that experience. So, what we're doing, we spoke about it in technical terms before with Giuse, we're really focusing on this concept of specialized AI brains. In the concept of the sales enablement use case...
By the way, just to be clear, Virtual Role Play as a concept, it's not an extraordinary new concept. There are technologies out there that allow you to interact with a virtual agent in order to get smarter about a certain topic. So this is not a new concept, but the way we're going to implement it, the flexibility behind the logic of feeding the right data and taking care of the semantics and the pedagogy value of the agent is going to be really our focus. We want to reduce the hallucinations by AI hallucination. Sorry, in AI terminology, meaning the risk of having the AI spit out some concept that is incoherent, inconsistent, and not pedagogically aligned with the topic.
You know, we're doing a lot of work by integrating external data sources. I mean, I am, I'm a really huge believer, as I was mentioning before, that in a few years from now, some of the biggest services and consulting companies will go beyond becoming the subject matter experts in classrooms or doing the so-called consulting to humans. Their consulting will have to become highly specialized to empower the AI engines to have the right responses, and to do that, a lot of work goes into that. So for example, for us, we have put a lot of work into translating and connecting Gong-sourced information.
Gong is a technology that we use in sales enablement at Docebo, to inject knowledge into the AI brain so that when the agent interacts with a human that is a sales professional, the jargon, the terminology, the best practices that we have are part of that virtual experience.
... Let me add one thing here, Ale. Just to complete the experience of the Virtual Role-P lay, we're actually focusing a lot on the assessment component as well. So it's not only the part that Alessio perfectly explained about the interaction and the fact that we're able to feed data in order to make the, you know, the agent behave like a real customer of the specific company. But we're able also to create an assessment report that codify the specific rubrics of the sales enablement team, and so, like, the assessment report is completely customizable, and it's also taking into consideration what are the, let's say, successful stylistic output that the successful salespeople have in the organization and give feedback about that.
So which is actually something that is very innovative because we are capable of fine-tuning large language models on specific company data and give this kind of assessment that is super specific and is also empowering our data collection strategy in order to create a very accurate profiling of the learners.
Yeah, and I would say, you know, and back to the original topic of the problem to solve, Josh, I mean, the way we think about it is, and let's get pretty practical here. You know, your average software company has a business problem. To talk about software, the business that we are in, and we know really well. Every company that you go into has a significant issue of continuing to upskilling its sales force or its customer support force with the latest techniques on how to respond to a customer in any given scenario, in any given situation. Humans are a scarce resource. We cannot think of having always, you know, your sales performers interact always with a human to get trained on something.
The technology today allows us to repeat this experience with a high, high degree of quality. Of course, our sales enablement practitioners, for example, at Docebo, have participated in making our own AI brain for the use case highly effective. See, this is a very good example of the... AI is not gonna take your job; it's actually gonna use your intellectual and pedagogical knowledge to make our engines stronger and more repeatable, so you can save time and not scramble, and scale your practice better. That's the spirit of it.
Awesome. So I guess to summarize around what you have, what to look forward to monetization, there's a lot of different features and capabilities which will be embedded into the whole platform to add a lot of value to your customers, and in return, you can benefit from that as far as retention, potentially new use cases and other benefits. You have direct monetizable product coming in Docebo Shape version 2. One clarification on version 1 and embedding that in certain tiers. I mean, would that... Is that gonna be available widely, or could that possibly represent an upsell if you know?
Yeah.
Okay.
Yeah. Our views are that it should belong in a certain category. There is a more, if you will, advanced category of Docebo pricing structure, and it provides an opportunity to, you know, encourage customers into that specific tiering, essentially allowing for an upsell opportunity.
Great. So there's a suite kind of upsell opportunity, and then direct monetizing the product, and then a lot to look forward to around specialized AI brains and, you know, new use cases and down the road. I guess, like, to get down the road, you think about resources. Maybe first on the product side, you know, how are you focusing your resources, and are there any technology or product gaps to fill? First on the product side, and then kinda ask a similar question on the go-to-market.
I let you go.
Okay.
So in terms of resources, we are actually, like, leveraging artificial intelligence as well, in order to speed up the process of product development internally. Of course, those technologies are fantastic in order to create innovative product solutions, but even, like, the potential of leveraging artificial intelligence for internal product announcement is just mind-blowing. So the productivity gains that we get internally, and really, like, the power of having that, this 10x or even 100x engineer is becoming finally to true. Because, like, the potential of a person, like internally in the product team, in order to really leverage a Generative AI to produce a much larger quality and quantity of output, in terms of product development is great.
This is actually one of the focus that we have in our AI internal AI team is not only looking and building products, but as well in enabling our product and organization overall in order to boost productivity.
I'd say, Giuseppe, I don't know if you would agree with this: I think in terms of, like, usage of resources, the end goal for us. I don't know that there's gonna be an end goal. There's probably gonna not gonna be any end to this. It's gonna be iterative, continuous growth. But we, you know, years ago, in learning, there was a lot of buzz around the concept of adaptive learning as a mean to indicate that systems had gotten smarter, and on the basis of who you were in any given learning environment, the learning that sort of made more sense to you was being offered, and in that sense, adapt to your needs. AI has given us an opportunity to a more nascent terminology that we refer to a lot as hyperpersonalization.
Which, you know, sounds one of those phenomenally marketing-crafted buzzwords that sounds so great, like hyperpersonalized. What does it really mean? Well, it actually matches very distinct AI capabilities because, to boil it down to simplicity, a system like ours, when an individual is in the system, there is a presumption that we know a lot about the individual, and that the system, every day, as that individual takes actions, whether it's responding to an assessment, watching a video, taking an instructor-led class, the system grows this flywheel of data. And that flywheel of data is not just used to adapt the pathways that, you know, user A takes to get to knowledge, but rather is capable of creating knowledge assets that contribute to that individual's goals and/or skills gaps.
So it's connecting the content creation with the skills reasoning. That's what we refer to as hyper-personalization, and we distinguish a bit from the concept of adaptive, which in a lot of ways is a lot more static as opposed to hyper-dynamic. And so our resources are really geared in that direction. Now, we don't know if it will take two years, three years, five years, and we don't know how the market will react to this concept of hyper-personalization. It is our view that it's way closer than folks think about, and that it's a lot sooner than we think, and our building blocks of technology are all there to execute on that. So...
Yeah, maybe I can add one thing about the-
Please, you're the master of this.
No, because we actually we're taking a progressive flexibility in our system, so our-
Now he's buying time on the roadmap.
No, no, but, but it's true, because actually, the capabilities we're shipping this year are actually a stepping stone towards what Alessio described as hyperpersonalization. Because if you think about, once you have a content that is not any longer static, but is actually prompt-based, so the creation happens with the prompt of the, of the user that is actually asking the system to create that content. We are actually moving towards the direction of having the content that doesn't exist anymore as a static concept, but exists as more like a fluid concept, is that, based on that specific prompt, the content is being generated. And it's very important here that we have the content quality is very accurate.
As we move more and more towards a road where this flexibility of the system increases, we're able to actually create content that is completely tailored for each single learner in the platform. That's a little bit the end goal, right? We have actually a stepping stone to get there. Not, I mean, for three, four years, probably, we'll achieve this ultimate goal, but we're going there step by step, and we are actually progressively adding more flexibility and personalization capabilities in our system.
Got it. Really, really interesting. Just a couple more from me. So I do want to remind everyone that if you have questions, you can use the Q&A, the Q&A feature here to ask questions on, on, if you're listening to this call. But wanted to ask one, Alessio, on sort of the go-to-market and the sales teams and management. You know, what needs to be done from that perspective in order to leverage all this exciting work on the product side, you know, to get that to your customers?
Sure. Well, the stepping stone, the element of preparedness that is necessary, from a Docebo standpoint. So there are two dimensions of this. There is the preparedness that Docebo, as a learning technology company, needs to do in order to be ready to approach the market with these exciting technologies we're creating. And then there's the market itself and the readiness of the market to appreciate these developments and actually make them at use. And we think about these two dimensions distinctly, but in a lot of ways in overlap, because first and foremost, organizations in general, they need to get educated on AI. There is a...
You know, I would say there is. It's still a relatively early days on top of the buzz and the large initiatives that are happening and the exciting things that are happening in the OpenAI world that has helped, probably for many, cross the chasm of knowledge that was even bigger, even just six, 12 months ago. But folks in most organizations to the question, what's your plan of using GPT and or AI in your learning practice? The answer is not there yet. It's very, I would say, at the very beginning of formulating a strategy that encompasses that. And so there is work that needs to be done, both on the customer side... And our responsibility, our role in this, is to be evangelists of the opportunities.
That's why we think a lot about producing guides. We recently released a buyer's guide for AI technology with this very goal of playing a role in the market to educate our audiences and helping them see the opportunities. And frankly, sometimes folks get the concepts. It's boiling them down to what the job they need to be doing. Because, you know, look, if I were to sit down as a learning practitioner with one of Giuseppe's top AI engineers, I would probably struggle to follow the reasoning and the thinking, because there's so much granularity in the technology in itself, that it needs to be abstracted for learning practitioners that, in general, are not very technical. And so our job is to translate all these technology advance in a consumable format.
I would say that, that is critical, both for the champions and for customers in receipt. Then our other job, the one that we've been, I would say from the very beginning, this is, one of big vision. The cloud is always brought forward, and that Giuseppe is, executing on, in a wonderful way, is it's a... Look, we thought from the beginning that one of the points of contention was going to be the governance models around data management. There's a lot of, conversation right now about, who owns what, privacy standards, et cetera, et cetera.
On our side, the role that we play in this is we are building, have built and are building and creating more sophistication in an AI control panel to simplify a much more sophisticated concept that allows our customers to make deliberate choices about the level of privacy they want to manage, whether to subject their data to anonymized AI processing or not, and what data. In turn, what does it mean for customers? The customers have the duty to having their AI governance strategy in place. Those that will will have a big competitive advantage. Those that won't will eventually stumble upon having to respond to things that they haven't thought about, and they will be slower when compared to those competitors that did think and plan for it.
And so I think it's early days, but determining an AI governance strategy is going to become a big problem that, you know, legal teams and governance teams and digital strategy teams in every organization are gonna have to face. And we see already customers that are very advanced in that regard, by the way. It's not that everybody's, you know, hasn't thought about it. Many have, but others haven't made this a priority quite yet. So we are encouraging them and acting as evangelists in this regard.
Great. In the last five minutes, Matt, we have some good questions in the queue. You wanna run some Q&A for the team?
All right.
Yeah, absolutely. So just to start, we've got a question here, related to content consumed in your LMS. So what percentage of content consumed in your LMS do you expect to be AI-generated near term? And then is there any opportunity to actually create the content yourselves, leveraging that AI?
That, that's a very good question. Actually, we see in general the amount of content all over the internet that is becoming generative, you know, created by Generative AI, is increasing exponentially. It seems like very soon, if we talk about broadly, just to take a data point, we will have the majority of the content in the next years that is on the internet will be Generative AI-created. So that is exploding. That is also, I think it will be the case for learning management systems. Very quickly, since the, you know, marginal cost for content creation with those tools will drop drastically, we will basically have the vast majority of the content will be Generative AI-produced.
It's difficult, of course, to give me an estimation on how much of this content will be Generative AI-produced, because there are also questions about adoption rates, and also cultural shift in the way that companies are creating this content. We are seeing already that the capabilities for content creation make that so much easier, and that we're talking about like hundreds of times faster, basically, to create the content that I can forecast that a huge percentage, if not the vast majority, will be AI-produced in the near terms.
Got it. Thank you. And as a follow-up, there's another one here asking just around competitive, you know, competitive threats. So the question is: Are there any fears that foundation model providers and the increasing capabilities of their models can impact the value of an independent learning platform? In other words, can GPT-5 being able to create multimodal learning resources natively be a threat to Docebo?
Yeah, I will answer to this question by saying that, GPT-4 or GPT-5, those will be general purpose systems. They are trained all with all the text from all over the all text and in the near future as well with all the videos and audio, because those are not anymore just language models, but becoming multimodal models, therefore able to generate a vast array of content. The thing I want to say here is that what those models are doing, they're building the foundational capabilities, a little bit like an operating system. And of course, if you take like, you know, Windows, that is the foundational operating system, but then you can build application on top of that.
The application that you build on top of the operating system, it needs to have, like, specific knowledge. Also, since we're talking about AI application, also specific data that can give you a moat. The way that we are building this in Docebo is not only leveraging that specific data, but also the knowledge that we accumulated over the years, and the specific vertical knowledge in order to encode those pedagogical structure into the system. So I believe that those will be the new foundational capabilities, and actually, they will only empower us to build a more powerful system.
Yeah, in addition to that, I would say, you know, we think about a lot about learning, as the ability to connect the pedagogical sound data with the workflows in support to those. And GPT will give you sophisticated content, albeit under specialized, per Giuseppe's point, but what really connects the dots is the engine, meaning the workflow engine, meaning the learning platform, that allows for the actual fruition of all of this, to become a part of a, you know, learning experience. That's why we call that. So in isolation, a GPT algorithm, can it substitute a learning platform? I really doubt it. I really doubt it. But for sure, the ability to have the combination of the two in the direction of hyper-personalization is what we're after.
Great. Josh, I think we're coming up here on, right on the hour. Any last remarks?
Yeah. Just wanna thank Alessio, Giuseppe, and Mike and Matt, and everyone listening on the line. This was a great conversation. Really appreciate it. If, if any investors want to dig in on Docebo, on skilling and reskilling or general, you know, GenAI, more broadly, feel free to reach out. Thank you very much for your time. Have a great day, and look forward to catching up soon.