[Music] all righty so I think this is a good point to start here so want to say a quick thank you to everyone for joining us we have gosh a couple dozen countries represented here today and folks keep coming in so I have to keep mailing letting them in so if you bear with me I'm letting more more people join now we' like to hand it over to our steam's EK instructor team uh we've got Tatiana here we've got Sarah May Fernando and Elliot and I'm going to pause and hand it over to you and um thanks for hosting today and thank you everyone for joining us thank you ER I appreciate that let me start sharing the screen yeah excellent well good morning or good afternoon everyone depending on where you are um thank you so much for joining us today in this exciting session um about km and Enterprise AI we're going to be talking a lot about the intersection between these two key Concepts and we're going to be discussing as well how knowledge managers can succeed using artificial intelligence methods uh my name again is Tatiana vaki and I'm going to be your host for today's webinar um as we all know the landscape of Knowledge Management is evolving rapidly and artificial intelligence is really uh becoming super important uh in this transformation so we want to make sure that um with today's webinar you understand what this upcoming class of KM and Enterprise AI is going to include what you're going to be learning out of it um and each of our facilitators today are going to share with you um a little bit of what's coming up in the class um so you can um really get a better idea uh of what um you can learn for yourself for your own organizations as well as ask us some questions if you have about content of the class we are inviting everyone to attend this um uh class and in particular we're focusing on U knowledge managers data managers we want also um knowledge and content engineers taxonomists and people working potentially in the public sector or like highly regulated industries that really need to ensure that um people access um information rapidly and you know under compliance um IT project managers as well that want to have a better idea of how km and Enterprise AI intersect and how they can better manage their projects around those Concepts and then really anyone who is practicing Knowledge Management and would like to learn more about Enterprise AI so welcome everyone to this exciting session we appreciate you're taking the time to be here with us and um let's get started we're going to introduce the team of facilitators I'm going to be one of the facilitators as well for this class so again my name is saana vak chaki I am a senior Knowledge Management Consultant with Enterprise knowledge um as well as a senior taxonomist I've been with the organization for eight years but I've been working on km and taxonomy for um couple decades probably or so um it is my passion and I really help um helping organizations understand how they can better organize content how km plays a key role in um finding information discovering information and so on so we'll be sharing with you um some of those key Concepts methods best practices and so on during the class with that I'm going to let my colleague Fernando introduce himself Fernando thank you tattin hello everyone thank you for joining us my name is Fernando agil I have been in the data analytics data science space for about 10 years now last of them uh well a little bit over four uh at EK um specializing in machine learning and uh building machine learning pipelines uh doing some data engineering for our customers currently with that I'll pass the microphone to Elliot thank you very much Fernando my name is Elliot R I'm a technical consultant with Enterprise knowledge and I specialize in semantic AI Solutions those are solutions that utilize gener ative Ai and then semantic layer assets I have a background designing and implementing Enterprise semantic layer Solutions as well as a little bit of laboratory automation I'm a recovering academic um and was a professor of philosophy I'll pass it on to S May thanks Elliot hey everyone I'm Sarah May I'm a senior semantic engineering consultant um I specialize primarily in ontology um both from the modeling aspect and also helping our our clients think through strategy and how to um go about you know developing implementing and maintaining uh semantic models for all kinds of solutions um I also work quite a bit in km strategy so thinking through how do we um how do we use these kinds of solutions to bolster km uh purposes at organizations so very excited to be here today speaking with all of you excellent um thank you everyone so as you can see we have a team of experts in many different areas that will be sharing with you all what they have learned throughout the years uh based on like Hands-On you know um project I've been working on with multiple clients on various different Industries and so on we'll be happy to share um our knowledge and expertise with you during this class um I want to mention as well that um we encourage you all to share any questions that you may have in the chat we're going to be addressing those questions towards the end of the session so let's get started um our agenda for today is going to include having a quick overview of the class and then we're going to get into each of the four modules that we have planned for you all we'll give you an overview of what each of those modules include and then as I said at the end we'll just get into a Q&A session in case anyone has specific questions for us about the class so let's uh get started with the overview this class is going to be um delivered over a couple of days um that is about like 12 hours of instructions and you know potentially a little bit more time if you want to uh dive a little bit more into specific Concepts or some of the you know um fundamental kind of like um ideas that you've been learning during the class but there isn't any like homework or anything like that it would be just more like time on your own if you want in addition to that we're probably going to have some kind of uh discussion board or maybe a Blog to help you all uh talk to each other and then talk about your experiences in relation to km and Enterprise AI as you're learning the different concepts throughout the class during day one we're going to be focusing a lot on like an introduction to km and Enterprise Ai and a few other Concepts around that we'll talk about it in a minute as well as understanding what are some of the top AI use cases and applications that we've seen uh with our clients I was seeing in the industry and potentially um probably getting into more details about what they mean and what they could mean within the context of your own organization so we really want to equip you with um that kind of information and day two is going to get a little bit more into like handson with conceptual model design um and also understanding what Enterprise AI uh Solutions approach would look like what are the different approaches that we recommend depending on different variables and again how potential that could look like for your own organizations depending on the complexity of the content depending on the use cases depending on uh you know some technical um differences and things like that or requirements and so on and and then during day two we also get into understanding um if you wanted to move forward with an Enterprise AI uh implementation for a particular use case that you have in mind what does um that implementation road map uh look like you know if you need to develop a plan what are some of the key elements that you need to keep in mind for that and how does the road could look like um so we'll get again into more details with each of our facilitators in a few minutes we want to present you with the class learning objectives just to better understand what you'll get out of this at the end of the class um so first of all let I said really understanding fundamental concepts and understanding how you can transfer that knowledge internally for your own organizations um we want you to develop a high level understanding of KM and Enterprise AI how those two concepts intersect together and and also understand what are some of those common use cases and applications and what is really the business value that it brings to an organization um so we'll explore that into into a lot of detail we want you also to be able to identify what is the level of Readiness of an organization it could be the organization that you work for uh to move forward with an Enterprise AI solution and will explore different kind of like perspectives on what that Readiness means before you move forward with AI and as well we want you to understand if you're planning for that kind of project then what do you need to to keep in mind and and take into account and that's L you know throughout the class you're going to be learning about industry best practices and tools and methods and approaches to really Implement Enterprise AI so in the first module which is the introduction to km and Enterprise AI we're going to start by defining um what km means and some of you who may have taken already some classes through KMI uh you may be more familiar with this but in any case we want to make sure that we start with the fundamentals so we'll talk about why we see km uh from like five different perspectives or including five different pillars people process content culture and the enabling Technologies to really capture and man manage and share um knowledge and and information so we want to make sure that we all have like clarity about what that means conceptually we also um get to Define what enterprising I which again from our perspective it's really leveraging the machine capabilities to find knowledge to discover knowledge and not only knowledge but also data and information and see how that can align in a natural way with how we would look for information or process information or intuitively so that's what um you know both km and Enterprise AI um you know look like from our perspective we're also going to get into a lot of definitions around these two concepts including you know what is content management what is data management what is a semantic layer um what is a Knowledge Graph um what is machine learning or you know natural language processing and what are the large language models and how they're used so we'll get into a lot of different foundational Concepts that will be necessary to better understand how km and Enterprise AI intersect together um we're also going to be talking about why knowledge managers should use Ai and in this particular slide we're leveraging some key statistics that um can like help us emphasize the importance of um you know Enterprise AI so um over 85% of the content and information that we work with is an instructure that is huge that makes it very difficult to find it makes it very difficult to organize so again we'll be looking at why is it important that knowledge managers can understand how to leverage AI to organize content to you know go through content and be able to make it more uh visible and available for end users um also it looks like um artificial intelligence is set to contribute to 45% of total economic gains by 2030 so a lot of organizations are going to be relying on AI to derive insights to make key and strategic decisions um for their own organizations very important to um ensure that Enterprise AI is going to help an organization from many different perspectives and in the class we're going to understand what those are perspectives eyes what those different use cases are and and why this is all important and and essential and um towards the end of the first module we're also going to get into um an overview of the common Enterprise AI categories so we're going to um take a look at various different types of AI as we see them as we see they being you know evolving basically in the industry um and we'll take a look at how some of these focus more on analyzing and modeling data to derive some insights you know how other um categories help with like different kind of like approaches and use cases and purposes so these are some of the uh categories that we'll be taking a look at um as we move forward with with the content in the module with that I'm going to pass it on to my colleague and Elliot to continue with module two thank you so much St uh in module 2 we're going to focus on three things primarily common km challenges that have ai Solutions or that we propose have a AI Solutions uh a number of categories and use cases that we've identified in the space and then a few AI Readiness um considerations so on the first point of KM challenges or common km challenges with AI Solutions we've identified at least six so you know if any of these uh stand out to you as particularly relevant um you're not alone uh so application overload there's so many different information systems that are all doing very specific things and they all hold information within them how do we uh address the problem of being able to retrieve the information that we want at the time of need so being able to utilize knowledge grafts in conjunction with AI systems allows us to provide the AI system with the set of most relevant information at the time of need that it can then deliver to us in ordinary language moving over to content explosion in the last 10 15 years we've seen just a tremendous amount of content most of that content remains untagged um processing that content now is kind of more relevant than ever to allow it to be able to be processed by these machines uh generative AI specifically is really really good at working with unstructured information but being able to figure out where that unstructured information is and retrieve it and Pull It in to the context window and feed it into a generative model becomes very very important uh task knowledge uh structure and capture so a lot of information especially unstructured information doesn't have any structure being able to use the models to impose a little bit of structure on them we're seeing a lot of graph rag uh capabilities here to kind of um extract information and then reimpose structure on that information that can then be utilized later um in answering questions uh collaboration silos one of the things we've been seeing a lot is um with all the special ation in the different fields people kind of get silent we don't know who has what expertise uh one of the things we've been seeing is being able to um use knowledge graphs use atic layer aets to be able to figure out who has what information or who has what skills and then feeding that map into a generative AI model that then can tell us like oh you know I don't have the information but maybe ask saw the person uh resource crunch we always have the pressure pressure to do less do more sorry with less uh and consistently even right so being able to um utilize generative systems to uh you know save ourselves a little bit of time or specifically reserve the time that we have to do the things that are uniquely human that we kind of have to be there for allow the models to give like everything a first pass that's kind of what we've been seeing uh institutional knowledge drain one of the uh accounts I'm working right now we're trying to uh figure out ways to Leverage and um identify and extract information for um an aging um and retiring uh Workforce so that has been another thing that we've been working on moving on to some of the uh categories we've identified for the use cases um so we've kind of identified two streams here right there's an inside track and that consists of describing use cases um some diagnostic use cases even predictive and prescriptive use cases um we're also seeing an action track one of the things that has been happening last year is we're uh being allowed to give the models specifically the generative models access to certain tools and certain like API calls that they can make so they can actually uh act on information so they're be they're able to identify something that's coming in and then act on the basis of that information so that's something that's still developing but these are kind of the categories that we're um uh operating with right now uh moving on to Enterprise AI Readiness um so we're we're seeing uh these are the things that we're running into as we like first uh start working with the client one thing is a lot of uh a lot of clients we come in and they really really excited which is really awesome right but there's not a specific thing that they want to do I think that getting ready for any sort of uh um AI implementation in your Knowledge Management Systems you really want to come at it intentionally right you want to figure out what are the things that I do want to offload what are the what's the busy work that I'm doing that I would love to offload to um kind of an automated uh system and what are the uniquely uh human and interesting things that I'm doing that I'd love to have the extra time to spend on uh when we've kind of broken things down and identified it like that and kind of set it out like that we usually find a pretty good use case uh assumptions that like so the second part the assumptions that it's a single technology it isn't a single technology it's not just generative AI there's all these different systems there's inductive systems and deductive systems so a lot of the reasoning that we do with our graphs and our anies those are also a form of AI and they're really really helpful and uh they can be utilized in conjunction with generative AI models as I was noting earlier um third thing and this is kind of going back to the first thing which is that like automation requires subject matter experts it's less of like a robot that's going to like take over everything and take your job and more like a really fast car but you have to be going somewhere and you have to have a driver in the seat uh the last part is that um most uh Enterprise information is not ready for uh AI we can't just dump everything in the middle and expect AI is going to sort it out again this comes back to intentionality being able to intentionally Mark your information in correct ways feed it into the models very intentionally those are the sorts of practices that we're seeing that are giving us the best results so start now start pulling things together and that's going to be a set of the things that we're going to be uh talking about in this class I will now hand everything over to Fernando thank you Fernando I'll actually step in here ahead of Fernando sorry sorry all good Elliot um so yeah we want to talk a little bit about you know Enterprise AI Solutions and the approach to those as well as a bit about knowledge mapping which as Elliot was discussing Readiness for AI Solutions this is one one component that goes into that um so if we go to the next slide um again Elliot set up you know the the kinds of solutions that are out there why you might be seeking out one of those so the question is how do we get there um in in the course upcoming we'll talk about the phased approach that we take to implementing AI Solutions overarching we're looking at user centered design and taking an agile approach so you know iter iteratively working through your use cases and so forth to expand into you know an Enterprise scale solution um so as Elliot was mentioning we we do Center the work around use cases that gives us a good definition of what is the challenge that we're actually trying to design around so it gives us some good guard rails we're understanding what the user needs are um and we have a good sense of what our solution will need to solve based on that um and so the the uh slide that you're seeing here shows that iterative approach that we take we always begin with a pilot or first implementable version uh the lowest level of producing something that is sensible and valuable and kind of demonstrates how does this work and how can we continue to build upon it um from there we go through operationalization this might be expanding to additional use cases um we're looking at integrating with with actual systems typically we have some priority systems that we're working with initially and then as we go to Enterprise application and scale we're uh looking at our Advanced use cases we're growing the infrastructure to be able to um you know expand the reach of our solution and then the next slide we have is a little bit more uh expanding upon that AI Readiness aspect so um content of course is is key uh for any AI solution uh you need to ensure that your input data and content information is of good quality so you can ensure that the results of your AI solution make sense and are useful to you I think we've all heard you know garbage and garbage out this is what we're talking about here when we talk about AI ready content uh it's like what it looks like and how to ensure that it's in the right state to support an AI solution so we'll get into more of the specifics around that in the upcoming class uh one example though is content standardization we heard a little bit about that already this is what enables AI to read answers more effectively and it involves adding structure and context to your content by applying knowledge models such as taxonomies or ontologies so these are some of the favorite things that Tatiana and I like to do as taxonomists and ontologists um so what are these knowledge models they're they're basically standardized models for describing the important things in your business and I'll I'll show an example of that in the next slide but I do want to note that these are part of a semantic layer which is uh you know part of many AI Solutions and this is a layer that sits between your organization's Source data and content and the applications where you or the users are actually interacting with the content so it could be an internet for example um the semantic layer's job is to contextualize and connect all your content so it can be easily understood and acted upon so wanted to give that context as we move into you know what is knowledge mapping this is an example if you go to the next slide of you know of one of these knowledge models um that you would find in a semantic layer and again these are tailored to describe and relate the concepts that are important to your business and used to you know add structure organize the content that is going to be used in AI Solutions um and then we're you know sort of wrapping up your content and data with this so it's cleaner and better understood by both you and and the AI solution so one key takeaway here is that these knowledge models are understandable by both humans and machines so it leads to Enterprise standardization connection of disparate data and content these value statements that we have here for reusing um being able to explain your data and the AI outputs as well as scale across to new use cases okay so I I jumped in front of Fernando with that I'll pass it uh back over to him for Enterprise AI Implement implementation plan and Road mapping yeah and I just wanted to mention something U SAR made now that you were talking about the knowledge mapping is important to to mention that we're going to be conducting some Hands-On activities right for people to better understand how knowledge mapping works and how to better identify certain like you know entities and how to really build an knowledge model so that's one of the hands- on activities that we'll be working on great than jemy now let's pass it on to Fernando thank you taana well uh by this point we will take everything we've learned uh so far and try to figure out a strategy and what tools you would need to uh realize Enterprise AI across your organization or across your teams next slide please so uh we uh we we'll go over uh the tools and strategies that will help you assess uh Enterprise Readiness either within your team or throughout your organization so first off we can we will start by discussing um how well with uh getting you what you need to get a strategy that assesses your organizational AI goals and make sure that they are in alignment with business objectives and with tools to ensure that uh you have leadership buying uh into this Enterprise AI initiatives the second point is uh measuring the state of data and content in your in your systems so we'll we'll go over tools that evaluate the quality accessibility and structure of your data for AI use and um also look into what is currently in place or what you need to establish a strong data governance and uh content management uh best practices the third point right there uh going into skill sets and Technical abil capabilities well uh it's no it's not um it's it's one of the most relevant uh key points here because people are usually the strongest asset in your organization and will'll help you either identifi guys gaps in AI expertise um we'll also go into how to prioritize uh training for Relevant roles and we uh will help you um get a strategy ready to ensure there is sufficient infrastructure and tools to support uh the people working in these um Enterprise AI initiatives and of course uh we'll go over strategies uh in change Readiness to make sure we uh you are a able to foster a culture supportive of AI innov uh of AI adoption in your organizations making sure that stakeholders engage early to drive acceptance and Adoption of your AI initiatives in uh as part of your km strategies next slide please well uh as we said on the latest on the last slide um people are are biggest asset when it comes to developing and deploying Enterprise AI Solutions so we'll go over uh certain key roles that uh would make any team be the Dream Team in your organization you would have a business team for example that uh makes sure that all the goals uh are relevant to the business and validate any designs uh that are proposed you have a business analyst that acts as the middleman between the business team and the technical team translating all of those uh business goals into technical requirements and right on your uh technical team will'll go over uh some other roles besides the ones you see on screen but usually uh we uh tend to see a lot of knowledge Engineers that um work with ontologist and taxonomist to make sure they're building the data models and that they're mapping the right entities to the right data sources and data Specialists that are always analyzing the currency state of the data to make sure it's um fit for uh Enterprise AI consumption we'll go into why uh having these roles are is important for your uh project and some of those are specialization and expertise um efficient communication of course accountability and uh most important all making sure that everybody is rowing in the same direction and at the same pace talking about rowing in the same direction we'll go over to the next slide please so that takes us to uh the importance of having kpis and success success measures as part of your Enterprise AI initiatives well um first off they help us define success uh we talked about translating business goals into technical um specifications so kpis actually uh help Translating that broad abstract uh business goal into specific measurable outcomes that uh make sure your technical teams and business teams are aligned and uh that drives focus and Alignment between the teams uh making sure that they are both uh growing in the same direction they can track the same goals uh even if they're in a in different languages business and Technical it also helps quantify the impact of your Enterprise AI initiatives providing visibility not only to the team that's uh driving it but also to stakeholders and uh leadership and it supports continuous Improvement uh by uh realizing what kpis are important for your for your initiative you have ongoing optimization of uh the Knowledge Management projects this results in Clarity and communication so now uh stakeholders whether they're in the technical side or business side or or even leadership looking from the outside in they have a clear way to communicate uh how the how the project is developing and how it is uh providing impact it also makes uh informed decision making easier on team members since they know what's priority what's uh priority in terms of of development and it provides a clear return of investment this is really important for uh leadership buying since uh with this return on invest they have continued or even increased investment in Enterprise Ai and Knowledge Management initiatives next slide please now um of course we always want to build uh futureproof Solutions uh so what's next in Enterprise AI what we've been seeing at our um with our clients and in uh the enter the industry as a whole we're seeing an increase in human and AI collaboration so we'll go into how you can integrate U these Solutions as part of your workflows to remain competitive since um other businesses are already already doing so and uh increasing their productivity so how do we talking about more the people in your organization how do we start uh redefining roles that may need to be shifted uh to make way for these Enterprise AI Solutions how do we assign respons posibilities and making sure that the human expertise in our organizations is still leverag in meaningful ways another Trend that we're seeing is uh AI driven Knowledge Management uh so how can we enable personalized knowledge experiences through Ai and how do you prepare your data and content uh to prevent the spread of misinformation which has been a huge issue uh recently and that takes us to our third um trend that we're seeing which is uh the use of ethical and responsible AI right um there are new regulatory Frameworks there are U that are coming up so how do we balance uh a rapid AI Innovation with our firms was to remain compliant with these U upcoming regulatory uh bodies and um what safeguards do we need to implement to ensure that we're treating our data uh with uh the Privacy uh that it deserves but also making sure that all our data is accurate for for Downstream consumption and with that uh we open the floor with questions on the next slide thank you so much Fernando and all of our facilitators appreciate that um we're seeing a few questions here in our chats we're going to start addressing those and if anyone has any additional questions please let us know um so it looks like um Rick had an initial question it look like Rick was um wasn't able to save for the remaining of the call but was still address the question he was asking why we wrote information as opposed to knowledge in the definition of Knowledge Management I think yeah that's a valid point I mean we really are talking about information content knowledge data I mean a little bit of everything at this point um insuring that we're able to capture and um manage and deliver and present to end users any of these different um you know pieces of content data information or knowledge so um yeah we'll make an update just to to make it more specific but uh yeah that's a valid point the next question that we have is from Mario Mona um it's a question for Fernando and it's about um you know whether um you would consider what you were explaining uh specifically for um you know for an Enterprise as a whole or potentially in a large organization uh for a small unit um and you know I'll probably start by saying that these um artificial intelligence like Readiness assessments that we recommend um could be applicable for an Enterprise as a whole but also they could apply to a smaller group within the organization if that makes more sense we've also seen that there are a lot of different um factors that play a role not only from a particular group so even if you're analyzing a group there may be some there's some factors that you need to keep in mind from your organization uh for that but U Fernando I'll let you U like expand on that as well thank you yes uh so definitely um our assessments go from Enterprise or and U at Enterprise level and we uh usually um have strategies to identify key use cases that could result in u the biggest I so that you can have U leadership buying but yes usually uh the way depending on how mature the organization is uh we've had clients that you know already that already have ai implementations across the Enterprise so they have the infrastructure in place to support uh an Enterprise wide uh minimal viable product or even a a proof of concept but um depending on on the maturity of the organization sometimes when they're when they don't have this infrastructure when they don't have these capabilities um then U that's where the roles um I don't want to say mapping but role identification and and the right people then uh we start with with a team right with the team that's going to be driving uh these Enterprise well these AI initiatives and hopefully building up to be at Enterprise scale thank you Fernando um we have another question that says um oh just moved um is there a difference between concept mapping and knowledge mapping um sah maybe you want to address that question sure yeah that's a good question um and it it kind of depends typically when we start an engagement um or are starting a new um phase of a project we might be uh conducting knowledge mapping or or conceptual mapping which is really just uh our opportunity to map out what are the things that exist in this domain uh in this business that will be important components of the solution um ultimately that that translates into um data Concepts or data objects um but first we use you know knowledge mapping to gain a sense of what's out there that we need to start tracking down for for our AI solutioning sounds good thank you Samy um Joshua has a question do you have um oh actually Brett had a question before that do you have offer uh group classes for a large number of participants within the same organization yes we've uh done that in many different um grounds whether it is specifically for taxonomy or antology or um you know more on the Enterprise AI or C um areas so we can talk about that separately if you want um you can reach out to Eric and then we'll get in touch um on that so thank you Brad for your question Joshua um ask do you have any suggestions for trying to develop km culture in an organization that doesn't have a formal cko or km cell um we um interact with a lot of uh organizations a lot of our clients who don't necessarily have yet a formal km structure within the organizations and um in order to better kind of like um try to develop that culture is very important to understand what are the business needs around km what is the value for the organization the business value for the organization in exploring km um and that um may take different forms such as identifying um you know some of these initial business you know like use cases to better understand um is it because people are trying to access information faster from a particular area is it because there is a need to increase collaboration is it a need to potentially drive more business business from the clients uh so that they can better understand you know our Solutions and products and then that way um you know increase increase Revenue like what are the business drivers and how do they align with the uh corporate strategic objectives so that's one of the key things that eventually will help from the to organization from the leadership perspective um have understand the importance of KM and and again linking that to the use ises I would say that that's when you involve more of the people who are on the dayto day on finding information creating content tagging content um having the need to connect different pieces of information to make it more findable and so on um so again involving each of those different people and make it more like user Centric um would definitely help in identifying those um those models and then there are quite a few different like best practices and approaches to Foster uh collaboration and knowledge sharing and so on some of them take the form of like processes and more like day-to-day kind of practices some of them um take the form of more technology supported activities that would help um and like um with a um you know reach out to a larger kind of audience and so on um but those are the things that um come to mind initially I don't know if s may you have any other thoughts on that um I think I think what what you mentioned Tatiana is pretty comprehensive the only thing I might add is um uh often we're working with with other types of of business units or or functions so we might be you know doing a project within um within like the the tech sphere of an Enterprise or specific to the data sphere um and you know we can apply those Knowledge Management best practices and kind of Be the Change agents that that you know might be missing if there's not a a formalized cam kind of structure within the organization so you you can bring km you know into insert it into different parts of the organization too very good thank you Sam and then there's a question from Elizabeth would be great in the class to learn how to appr properly select approaches based on the size of the organization um things you know for small or medium business buiness and yeah I think um you know we'll address some of that in um you know as we get more into the various like use cases and some of the categories of Enterprise Ai and so on um we've worked with organizations from you know various different types of sizes and industries as well so we can add that uh kind of component or flavor you know when we start talking about those detail um categories in these cases so we'll be happy to do so and okay we have a couple more questions here from Mario sounds like the scope fun Enterprise is an organization company or subet they all have you consider or experience the extent definition of an Enterprise that may be required to successfully achieve bigger picture goals like supply chain go market yeah I don't know s may do you want to um address that question yeah I think Mario I want to make sure I'm clear are are you talking more about sort of uh domains or applications of of a potential solution when you say supply chain go to market yeah thanks AR um so for example in some Industries um there are Knowledge Management and Concepts that are going to be necessary across an organization not just a company for example if you happen to be in the energy space um emissions are you still sticking to scope One Two Three or are you going to the crossborder uh concept if you're in Europe are you looking at Red uh demarcations and then thanks to covid all of us who experien the supply chain struggles if people are using uh AI or generator of AI or Knowledge Management Etc how do we now make it so that whether you're sensitive to supply chains or you're sensitive to investor streams whatever those dependencies are for the bigger picture context of an enter prise not a company have you guys looked at that so I would say um we we work within we work in sort of Industry wide um domains as you're as you're talking about so we've done a lot of work within the financial services space um in particular so I think that's one example of how a a solution might be applicable across you know an industry like industry types of problems or that kind of scale of things um I'm not sure if Elliot or Fernando do you have any any additional context to add here um to I guess to round that out a little bit you know going back to how we go about our our design of solutions it is very use case based as we mentioned so um it's it's we're not necessarily looking at um every system that could impact um an Enterprise level we're looking at what is the particular challenge that we're trying to solve um and designing designing around that and if that challenge is touched by supply chain or go to market um pressures things like that then we would be accounting for that within our design yeah I think yeah just to add to S uh response I think that uh goes back to having uh kpis and success measures in place uh without having a specific problem specific use case in mind it would be impossible to to measure the impact and uh just uh for for an example I'm not sure I'm understanding uh the question but uh like sorry May said we like right now uh we're both in a in a financial services project with a large uh multinational and for example uh we are starting looking at their non-financial risk ecosystem and we are implementing different uh different Enterprise AI initiatives going from Auto tagers to recommenders to uh to knowledge extraction uh initiatives and uh they do touch with uh regulations that go across borders like is this uh you know is this piece of data uh regulated in Europe reg in North America so our the solutions do expand across borders across domains because it also serves their wealth management uh their wealth management arm it serves their uh their trading accounts uh as well as other uh others I'm not sure I'm completely understanding what's that issue in the question would you mind uh refr cing it yeah Elliot so an Enterprise like I said could be a company or it could be it's extended value chains right so um depending on where you're operating and I like that that you all use the financial or the fintech example there's a lot of different things from a knowledge B standpoint or conceptual standpoint like nfts and the bubble that happened because people got super excited about it cryptocurrency um the climate related finan cial disclosures spearheaded by Michael Bloomberg there's a lot of different things that extend beyond an individual Enterprise whether it's regulation or behaviors in specific Pockets Europe versus North America versus Asia Pacific Etc so I was just curious do your definition of enterprise extend beyond that within the knowledge that's actually owned and and created within the Enterprise itself hasht company organization or the extended macro view Perfect all right I think understand the question now I think that one of the things that we're seeing in the space of um like using symmetric layer applications is bringing in ontologies that are um kind of just industry standard ontologies um so that's one of the things for like the financial sector right um and we're seeing these ontologies be built up in lots of different sectors some work has been done though not by us specifically on attempting to capture facts inside so creating knowledge graphs not just ontologies for certain sectors um obviously when you're using any generative model you'll have some set of information maybe knowledge that will be kind of pre-loaded into it um I think actually given your question one of the problems that we Face going forward into the future is figuring out how to um deal with the fact that these models have all this information this external information that might be out of date and making sure that we're able to correct for that so um like this is this is a very complicated question great question and something that we have to think about a lot but I think the context within which at least I'm thinking about it is figuring out how to edit information out of these models that is about the wider ecosystem when I'm trying to answer a question within a domain or Enterprise but it's a very good question something we have to think about a lot going forward does that answer your question Mary yeah it does thank you very good excellent I think we have um few just to address a couple more questions that we have in the queue and there was a comment from Joshua about like you know it often feels like those of us who are arcm minded need to do it more from the bottom up and yeah I look for this business case supporting metrics I think that alludes to some of the uh comments that s and Fernando were also mentioning U the importance of having um very clear business you know cases and use cases so that you can actually track that you can actually measure it um and uh yeah that definitely important here in km and Enterprise AI so I don't think there was a question per se there but it was more of a comment um there is um a question is the training also relevant for people that don't have an immediate use case that are interested in Enterprise AI in general absolutely these class I mentioned earlier at the very beginning of this webinar it's um really for anyone who is interested in learning more about how km an Enterprise I intersect what they mean in the context of your own organizations and then even if you don't have a use case yet it's going to start opening up some opportunities for you starting to think about different ways of seeing um improvements in your organization oh I just realized that these you know top um you know Enterprise AI category could potentially apply to this particular business area within my company right and then start exploring um that route so absolutely you know we're we're happy to have you in the class even if you don't have a particular use case um identified regarding training in general is there a contact and I believe that Eric already provided um a response as well as um Elizabeth so there is already a um URL as well as the the name of the person who will be addressing your questions is the recording available um Eric yes um we're gonna have the recording available probably in the next couple days I want to say early next week we' just like to do a quick edit um so we'll make sure that everyone who registered with us today will have access to that you'll be the first to get it uh before we post it uh in our social channels so short answer is yes excellent ER well with that um actually forgot to share the last uh page of our presentation but we welcome you to um you know register for this class um it's going to take place in November um we have the specific dates uh here is November 5th and 6th and um the web page with the details about the class is already available in the in I'm actually going to put it here in the chat as well for those who don't have it yet um and we'll be happy to have you there so thank you everyone for attending the webinar um thank you for your questions thank you for your interest in this particular topic and again our team will be very very happy to share with you all our knowledge and expertise over the past few years in this two main topics thank you everyone thank you totiana thank you EK instructor team and thank you everyone for joining us today we hope to stay connected with each of you and let us know with any questions about this exciting new course debuting in November thanks again for joining all right take care thanks again thanks everyone