uh thank you Kaylee um hi everybody my name is Jonathan Alam and I'm the federal CTO for service now uh I'm really excited for today's conversation on artificial intelligence and how service now is already leading in this area and how our service now Solutions Support Agency missions how we align with om requirements around Ai and the president's executive order on AI I've been at service now for a number years and before service now I worked as a Chief Information officer in the federal government I was a CIO at the Department of Agriculture and worked in CIO positions at other agencies as well and the introduction of new technologies like artificial intelligence and generative AI into agencies is is never um done simply it always takes time it always is something that everyone has to become comfortable with but we know that this is a really important technology and it has the opportunity to change the way that we interact not just inside our agencies um not just how we serve other employees but how we serve our customers how we serve other government agencies and how people really benefit from government so in today's conversation we're going to talk about the way service now is playing a really important role in this critical transformation as we get started um I just want to share our Safe Harbor statement uh long and short of it is please don't make any investment decisions based on anything you hear us say during this Pres presentation we will be potentially making some forward-looking statements now I want to get started by having a conversation about the service now platform and a brief overview of service now so we're all on the same page as we get into this really important AI conversation and and for this part of our conversation I'd like to introduce my colleague Ser Sergio Hernandez to talk us through service now at its core and I think the most important thing is to remember that service now is not a single technology it's a it's a platform it's not a single product it is a platform with lots of great capabilities so Sergio please go ahead and take it away awesome thank you Jonathan hello everyone my name is Sergio Hernandez I am a solution consultant here at service now specifically supporting uh our department of the Air Force uh Team uh organization and so what as Jonathan alluded to we are a platform and the goal of what I want to do here at the beginning is just be able to articulate what it is that we do and ultim the goal is that at the end of this conversation today you guys can articulate what service now specifically can do for you all uh and so uh Jonathan you kind of started by saying you know we we got our start as an IT service management solution where we helped uh organizations within the IT department route work effectively and since then we've really grown drastically our capabilities have expanded as you could see through this visual to address a number of different use cases of across an entire Enterprise so not just it now today we're going to be talking specifically about generative Ai and one thing I want to highlight here is that over the years we've actually been building up those capabilities to get us to this generative AI point in fact Ai and machine learning have been a big part of what we've been doing for for uh a number of years now so when it comes to service now where is it that we usually start the conversations when we engage with organizations a lot of what uh we see when we kick off those conversations is what you see here right you have a number of different teams and organizations you have a lot of data that needs to move across those different teams and there's also a lot of processes involved across those teams as well as within those organizations and what I like to do is kind of summarize what we see in the beginning so if you see the first thing we see is that there are very unstructured and manual processes that exist across an entire Enterprise with that organizations are using very uh Legacy kind of tools right whether it be uh an org boox a shared Email tracking things on spreadsheet or SharePoint things of that nature and ultimately we understand that there are a lot of systems at Play Systems of record that organizations need to use especially when it comes to the government there are government mandated systems that folks need to use and therefore there's a lot of swivel Cher cheering going on between emails looking at a spreadsheet and then tracking everything against a system as well so that's a lot of what we see ultimately it comes down to people processes and data need connected and that's what service now does through what we call our service now platform and it all starts with the bottom layer that you see down there and that is our core platform which has a number of different built-in capabilities one of those being again predictive Ai and generative AI we take those capabilities and we put them together through specific workflows that we've already uh pre-established uh on our platform that address very specific use cases whether you're part of an IT organization whether you're part of a personnel and talent management organization if you're on the operational side or on the uh finance and supply chain side we've pre-built these different workflows using industry best practices uh so that folks can then engage with our platform and that's where that third layer comes in through a number of different ways whether it's through different portals that they're using through thirdparty conversational uh tools such as Microsoft teams and workspaces and you're going to see a lot of these different um experiences uh throughout the conversation today but again what we're doing is we're bringing together different teams different groups of people different processes and data and engaging him into our system of action which is the now platform now now today's conversation is really focused on generative Ai and that's what we we want to talk about specifically um as I alluded to earlier this is something that service now has been working on for a number of years to get us to this point and I'm sure a lot of you have heard of a different uh AI uh excuse me generative AI uh models that are out there such as uh open ai's chat GPT Microsoft's co-pilot and they are all built to do a lot of different things right and service now we also have our own domain specific large language models which attribute to our generative AI capabilities and what I want to start by saying here is you know we've we are an organization of structure and and purpose when it comes to how we help Enterprises and that's the exact same thing that we're doing with our generative AI so if we jump to the next slide we are using our domain specific large language models to address very specific use cases the first one of those is being able to understand uh the intent of what an a customer or an organization is trying to get at right and we have that capability built into our platform where it understands uh common everyday language to get to the intent of what it is that an individual needs and you'll see some of that today the other is being able to take that information and understand what needs to be connected or who needs to collaborate across an entire Enterprise to make sure that ultimately if we click on the next uh bit there we generate content or or I like to say we actually enable you to generate action or take action items U specifically on what it is that you are looking to accomplish whether you're an end user needing to get some information or whether you're on the backside helping uh trying to address or help someone with uh an issue that they have so that's just a very quick highlevel overview of what service now does and sort of how our generative I uh structure is built out and what we can help organizations do I won't steal anyone's Thunder but pass this on to thank you Sergio for for that great overview you know one of the things that I I uh I find so intriguing is you know on that prior slide service Nows got a multiple approaches to generative AI first with the domain specific large language models that you talked about that make it make service Now operate uh give the opportunity for Serv now to operate in a much more efficient way because the generative AI can take on so many of the tasks that people do in service now today and generate resolution notes or generate knowledge articles or you know do a do a really great uh virtual assistant chat or any number of things generate um code we'll talk about all that all those capabilities later on in the presentation and the other piece of it is the connection to other indust other large language models available in the wide world and being able to leverage those if an organization is using a commercially available uh large language model like chat gbt or something else we can pull that intelligence into the service now platform to make it actionable so again service now is about being able to take action and doing it as intelligently as we can and I think the approach that we've we've settled on with service now is one that's going to really resonate in the federal government based on my experience and all the things we're hearing from our from our customers already and there customers that have already gone out and acquired these kinds of capabilities um including service generative AI tools so thank you Sergio I want to I want to continue on in our conversation and um ask everyone a polling question so Kaye if you can launch that first polling question you we want to know where are you in your AI journey and I think this will be really Illuminating as we continue our uh conversation today all right so as I shared in the beginning introducing a new technology like generative AI inside the federal government isn't simple and there's guidance that's required and we we have an executive order that um I think most people are aware of in guidance from The Office of Management on budget and we want to talk about this topic a little bit and really do our best to kind of demystify this executive order and how it impacts uh organization so uh to do this I want to bring on my colleague Jeff Browning who is uh our government relations uh leader at service now um Jeff why don't we uh oh we have some results from the polling questions and it looks like uh the vast majority of uh participants are researching AI use cases and you know or beginning some adoption and well we have a few that haven't gotten started and we have some they're using successfully many are in this uh research mode and and and slowly uh getting started so to the rest of our presenters as we have this conversation let's keep that in mind so so Jeff let's uh let's talk about the executive order on artificial intelligence thanks for being here and want to begin with you know really a question about what does the um executive order require agencies to do sure so the executive order itself was pretty sweeping touches many parts of the economy but relevant to this conversation it uh it requires om to issue guidance to all federal agencies that governs how they evaluate procure and deploy AI that om guidance which is m241 was issued on March 28th of this year it's now in force uh it creates a lot of different requirements for agencies which we'll get into in a moment uh but that's really the policy requirement that we're talking about here the the niche of the executive order that's most relevant to today's conversation yeah so as we take as we start talking about these things you know Chief AI officers governance boards what are we what are we seeing as we have uh agency level conversations sure so agencies are required to appoint Chief Artificial Intelligence Officers it can be the same person as the CIO but it may be a different person at bigger agencies we're seeing uh that those roles are split sometimes you even have a deputy chief artificial intelligence officer agencies are also directed to establish AI governance boards which consist of certain cross- functional stakeholders they're required to create compliance plans and file those with uh with om there are some December 1st deadlines which are relevant here which agencies are racing towards I would also take a moment to mention that while we're here to discuss service now's gen ey capabilities as agencies stand up these governance structures the service now irm product is also very ideal to operationalize the decisions that they make uh and you can certainly follow up with the irm business unit about that um agencies also have to inventory their AI use cases they have to determine which ones impact rights and safety according to certain criteria and then for those that do impact rights and safety they have to take additional steps uh to protect rights and safety I would also note that the decision about how to interpret and implement this guidance including the decision about which use cases impact rights and safety that's up to agencies we can't give legal advice we can't that if you buy service now's product you're automatically compliant but what we can say and what we're laying out here is that service now's existing AI governance and responsible AI practices align very closely with a lot of the themes and the guidance and agencies can leverage that they have to conduct impact assessments uh they have to look at the anticipated risks and benefits they have to to conduct human training and oversight which is a great fit service now offers uh continuous learning on demand to all of our customers they have to assess which decisions AI is not allowed to make without a human our platform certainly allows that certain types of cases can bypass or be configure to always fall back to a human they have to provide plain language notification to the public when they're interacting with AI service now also plainly notifies our customers when they're doing the same they have to consider Contracting Provisions which uh incentivize Contin ous Improvement that also is a great fit for service now as many people on the line know we re-release our platform twice a year with new features ofered and response to things customers have asked for so continuous Improvement is a part of our business model you don't just buy it once you buy it in all future versions for as long as you remain a customer agencies have to promote uh competition there's concern about vendor Lock Service now as you know connects with hundreds of Technologies we believe uh some of the best productive outcomes from AI will come from companies working together not Walling themselves off uh they have to maximize the D the value of their data for AI uh what you do on the service now platform Belongs to You retaining the rights to your data is built into the DNA of the platform um so there are a lot of places where we where we align with the guidance here let's uh let's jump over to this idea of um risk management because we know risk management is a really important element of introducing AI into organizations and and of course government agencies have been using AI in different way ways over time as we talk about generative Ai and some of the risks around hallucinations or um lack of transparency it becomes you know even more important but let let's let's talk about risk management and you know om is uh directing folks towards the nist AI risk management framework talk about that for a minute sure so at service now our internal AI risk uh and development policies are inspired by the N race management framework there are a lot of open questions right now policy makers around the world are looking at what the standards should be there are open questions about if and when we will have an auditable hard standard for AI systems models and data sets uh there are some things coming from ISO some things coming from the oecd some things coming from the EU but as far as American leadership nist has done a great job with the risk management framework uh which we think is perhaps more specific and executable than some of the other uh options that are on the table and we are partnering closely with customers as they also adopt the N risk management framework work which they are directed to do by the OM guidance and as they contemplate questions that aren't even envisioned in the N risk management framework we're also working through a lot of those issues uh with our customers as well so other thank you Jeff and you know other countries are doing similar things and that may have an opper may have an impact ultimately on on US Agencies as well um but you know the ongoing monitoring transparency you know these these are questions that you know we're hearing from Chief AI officers as we're out there and you know share a little bit about your your perspective what agencies are asking for and how how you've seen um service now respond sure I think you know given the the tone of the OM guidance and where we all are I mean in government you know in public service with public funds come increased responsibility people want to know if something goes wrong could it have been foreseen people don't want to be stuck there having put something into place that creates a problem uh inputs and outputs to AI are logged in the platform and can be used for analytics that's very important especially if you're at an agency trying to sort of log those things look at those Trends especially considering the agencies are directed to do ongoing monitoring evaluation uh and mitigation um also as far as transparency and adequate performance standards you know when you're looking through this from from a procurement standpoint service now's llm model cards include plain language information for our customers about uh known limitations risks and realistic performance standards uh for our models we really do a lot of work internally to align and make sure that we make that information available to customers uh and we have very clear channels in place to respond to their more detailed questions as they're working through procurement and deployment uh and risk management we um we have very good systems in place and we'd encourage you to engage with your account teams on that well I think that is um you know that's an important uh conversation for us to continue having because you know when you look at the news and you you hear people um Express trepidation about Ai and generative AI so so much of it comes back to well we don't know how these models are operating we don't know how they were trained or they're trained on all the data of the internet um so the risk of a hallucination of bad information of making a bad decision is high and you know really feel that you know with the way the approach that we've taken with service now with our industry specific domain specific large language models that are based on service now data we have the opportunity for um to to Really describe how where the data comes from and how those models work and alleviate some of these some of these needs and these some of these concerns and you know one place I know that you were touching this briefly but where this risk uh perspective comes comes up is in the idea of Rights and safety and talk about rights and safety a little bit more um you know what what are some of the you know examples we're hearing and you know you've already you've already shared that you know we service now may or may not be um part of part of this uh it's an agency determination but just share a little more on your your perspective sure so one of the things that agencies are directed to do this year is to as we touched on earlier conduct an inventory of their AI use cases and determine which ones impact rights and safety well what is the standard for that in the OM guidance there is language that lays out certain categories of use cases which are presumed to impact rights and certain categories of use cases which are presumed to impact safety agencies are required to make a determination of which of those use cases that they have are presumed to impact rights and are presumed to impact safety and then in those cases they're required to take additional steps again it's up to agent genes to decide we can't give legal advice some of them may ultimately decide that certain service now use cases like now assist for itsm are lower risk but we again they have to come to that decision on their own we can't give legal advice even if they do decide that a service now use case is presumed to impact rights and safety again we have clear channels in place to work with them through that process make sure their concerns are met and one of the requirements here that's written in is real world testing which I know there are a lot of questions there what does that mean well what I can say is that we do allow customers to conduct their own testing in a sub production environment before deployment uh which is also a really important thing here if they're going to be running through clones or their own data to see how things uh Jeff let me build on this um you know point we can't give legal advice but there I totally agree are there but there are other legal considerations that we need to keep in mind or customers need to keep in mind as we as we go down this process together sure and and again the the primary one that I would give as a caveat is that that interpreting and implementing the guidance is up to agencies it is their prerogative and again we can't say that if you buy service now you're automatically compliant but the goal of this materials and these collateral that we've created is to educate customers on what our internal governance practices are which are strong and which are cross functional what our responsible AI practices are throughout the model development life cycle because those themes are very relevant to the requirements in the OM guidance and agencies can leverage that uh to the question in the that yes and we would encourage you to speak with your account teams about that about what those riskmanagement Protocols are um what they mean as far as functionality um and also what we have on our road map as this uh exciting World Keeps to develop yeah okay Jeff we we're uh coming to the close to the end of our part of the conversation so I have uh just you know a final question as as we're talking about the executive order together and uh customers with our um you know internally at at service now where where do you think that this is going to be going over time you know is is this going to lead to legislation is going to lead to some formal regulation what give us a prediction of the future I I think it could I think that some of the biggest use cases in the federal government when we look at really you know look at the high impact service providers for example and the customer experience EO these are people who are making enormously important decisions about anything from Social Security benefits to nutrition benefits that impact millions of people's lives I think everyone knows that AI has the ability to potentially alleviate those burdens but I think everyone also knows that you know there are issues with with the data with how it's trained with biases and again when we're talking about the federal government we have to hold ourselves to a higher standard um and so I think that that is you know an important part here of the public private partnership between agencies industry will be having those conversations in advance about those risks about those use cases and working together to build something that is satisfactory to everyone and that at the end of the day helps employees and citizens alike um I hope that answers your question it does Jeff it and gives us something to look forward to or look towards as this uh this space continues to to evolve so thanks for your time really appreciate you being here um I want to go back and ask another polling question on this one specifically on the uh executive order so Kay if you can put up that polling question uh how impactful will this will this be in your agency really interested to hear what uh what people say okay thank you we're now um at the point where I want to um we we want to get into the nuts and bolts of how service now generative AI capabilities are going to impact the platform and the kinds of uh opportunities they creates for you and your agencies to really streamline the way you work to really transform the way government works so I first want to uh ask my uh my colleague Gus Gus Pastor to to join me on on screen um Gus thank you for uh for being here Gus is one of our leaders in the Creator workflow space and he is going to uh walk us through now assist for Creator workflows as you get started though two things one if you saw the anwers to the poll question the um AI executive order is going to be highly impactful just about everywhere so Jeff once again thanks to that conversation helping to demystify it a little bit this is going to be part of an ongoing conversation and you know Gus as you get into your presentation we you may talk a little bit about Erp and we do have a question about Erp that we can chat about as uh as you conclude your your section so gus please take it away yeah thanks let's um answer the Erp after this because the domains are a little bit separate in concept so I've been here at service now for about four and a half years and I've spent the entire time at this company uh espousing and teaching customers across public sector the benefit and the who what how when where and why of building on the service now platform um before coming to service now I spent a lot of time in the open source development community and one theme that's been consistent and true is that software development struggles to keep up and deliver on the expectations of the mission or of business and uh kind of the exciting thing for me when I came over here is that low and no code um operating software development on top of of a configur configurable platform versus systems configuration uh really gave us the opportunity to decrease the barrier of Entry to being a collaborator in the software development life cycle that in its own though brought with it its own issues um customers have concerns about governance and how you put the right Tools in front of the right people without interrupting software development um and they have questions about the validity the security the performance of the products that developers across a broad spectrum of skill sets actually produce so the exciting thing from my perspective is that generative AI is going to become the great equalizer across software development on platforms like service now it's still very much about a configur system versus systems configuration um and what we're doing is we're going to automate and provide end users of development the ability to speak their outcomes into existence these uh the automation of this process is what's going to allow us to reach a greater pool of quote unquote uh developer resources the important thing to note here is this is based off of service now best practice when you're speaking these things into existence it really depends who you are in an organization and the rights that you have remember that service now is role based it's governed from the ground up um that allows us to put the right elements of development in front of those people and leveraging your investment in now assist for Creator we're going to drastically simplify that barrier of Entry to be a software developer now the question is is who does this impact you have real three three real buckets of developers you have core true service now developers you have um High competency developers that may not know service now but have skill sets in the arena and then finally on the on the other end you have true no coders every single person in every single bucket is going to be able to benefit uh from leveraging generative AI to speak their outcomes into existence whether that's a flow um an API and an integration or a full-fledged application and a later release really the ability to uh take your knowledge of a business process of a government process and to put it into a system that can then inherently create that outcome uh it's going to have a ton of velocity and it's going to allow you to push service now into the Far Corners of an organization and tackle the backlog that sometimes takes a little bit longer to deliver on I want to keep it short and simple there um and just and keep it at that Jonathan yeah for you know this is a this is a tremendous extension of low code capabilities that service now's offered over time and you've been you know highly involved in with a lot of our customers that have adopted low code and one of the challenges with low code is always the governance around it and you know my experience is worth the effort to set up the right guard rails so you can push out this capability across an organization we know the governance pieces can be hard but you know the CIO office and having been a CIO I know this can sometimes be viewed as this bottleneck to getting things done and you set up the right guard rails and suddenly you have created some self-sufficiency um does that change you think with the introduction to these tools it change is there a uh important I'll speak super honestly about it um there's this moniker out there around citizen development um I kind of push away from that I think the right way to think about it is delegated development because it brings with it the inherent connotation that the group brokering the service is ultimately in control governance is still hugely important um it helps tackle the cultural side of scaling development um so absolutely like you're going to need to understand what your organization expects from developers but all this does is make development more accessible it is the next evolution of building on the platform um it doesn't change what's being built or how it's being built it just changes the speed to which it's being built and what is the input before it was gather the requirements and then wait to be provisioned access to a scoped application and then kind of Cobble together the right types of flows and experiences and then go through the code review process the code review process is still there developers are still huge us important um but what this does is it allows you to select who the right person is and put the right tool in front of them and now instead of dragging and dropping they're going to be speaking and the system is going to be automating so governance is still a huge part of it it's inherent to the DNA but um the real the real acceleration is going to come into the uh productivity and the acceleration on the dev side and I can get things done faster and if my less complicated applications are the ones that I can speak or type into existence I can take available resources to deploy them perhaps to more complicated things so it gives new flexibilities inside organizations that you know may already be a little bit resource constrained and um you know I I think about you know that as potentially being a huge impact a huge game Cher for how we staff our organizations and how we get our work done in service now so uh you want to talk about Erp for a second that is an important topic for for you personally yeah so um I just moved over to spearhead this new initiative at service now around modernizing Erp systems and I saw the question in the chat um I thought it was a really good question because we get it all the time uh is service now becoming an Erp and the answer is unequivocally without a shadow of a doubt no uh we don't seek to become an Erp the fact of the matter is is whether you're Oracle or your sap workday or otherwise everyone is going through this journey of modernizing um but what we talked about and what Sergio covered earlier is we've created a ton of silos within our organizations and in the Enterprise you made the right decisions with the Erp systems you elected uh 10 15 20 years ago uh but your end users they need an elegant way of navigating the things that they need to do uh service now seeks to be the workflow layer and to have a comprehensive integration and data strategy to allow you to leverage those Erp systems as a system of record and to elevate the customization in the business process up into an abstraction like service now which is inherently more flexible and agile um we're not saying everything belongs in service now highly performant business process or highly performant systems uh like sap as an example are going to have certain things that belong close to the power users close to inmemory um but you can get away with and Achieve significant gains in terms of productivity and the ability to tackle the backlog using a generative AI or low and no code mod to provide this immersive experience that spans across multiple different systems and that experience should also tie into where someone gets their benefits it should also tie into requesting uh network access or uh around a lap or like laptop service like the goal is to consolidate the experiences and not force you to have to make an architectural or data propagation decision about where that data lives and through some intellectual property and some advancements in terms of service now we're providing a much higher rate of performance and throughput in the connection of those systems allowing you to interact with data in service now and then to mirror that data down into the Erp layer just kind of bringing all this stuff together and not having to ret and retrain users every time you have to introduce a new system or configure a system a different way let's consolidate that work and that's what service now seeks to do and when it becomes that that destination for application migration application modernization there's so many additional ancillary benefits including what you said better security one place to go less complexity the organization U so there's so many reasons that agencies across government and organizations across the world are identifying service now as a core platform for application migration and modernization so guys I want to thank you um I want to jump back into the uh presentation we're going to ask another polling question everyone um Kay if you could put that up there this one is on application development so we'd love to hear uh from you all your perspectives on on this topic and and uh what Gus just shared so next we're going to move on to uh now assist for Creator work uh excuse me for technology Workforce or it service management and joining joining us is uh my good friend Chris russik uh one of our one of our leaders in the technology workflow space Chris thank you for being here and pleasure thank you John oh excellent so you know service now is um known as it service management tool right that's that's not we're a platform as we talked about in the beginning with Sergio um but we made our we made our mark and um here so let's let's dig in to technology workflows and uh you can see the aners to the last polling question highly impactful moderate and uh moderately impactful everyone seees impact from having uh generative AI application development tools so we're on the right track you know as as a company I think in the right kind of conversation so let's uh let's go back to our you know our founding it service Management on the service now platform how's it evolving with with now assist Chris yeah absolutely thank thanks Jonathan um so how is it evolving um it's a great question and I'm going to introduce to you service operations as part of this use case today and for those that don't know service operations is the marriage of IT service management and it operations management and you know getting to the to the basis of itsm um we all know that agents are using service now for itsm and more and more often are required to read and interpret information uh related to incidents or problem tickets and that requires a lot of time it takes the ability to synthesize that information and make sure it's accurate so that people down uh on the receiving end can make make uh excuse me can make decisions and take action based on uh that synthesized information and when an incident arises those agents need to quickly understand and resolve the situation rather than spend time searching for the root cause so at the end of the day this is about uh you know faster root cause analysis and and get faster braak fixs so welcome to the service now's now assist for itsm and you know here's a you know maybe a big surprise for some people but our customers been using service Nows now assist via the AI assist agents for several years now and as Sergio alluded to earlier that capability allows customers to use any internal and external data as desired uh and what meets your policy of course to help draw better and more accurate conclusions we also uh provide recommendations for these agents um to assist those resolutions so uh analysis combines the ability to draw from both types of data while giving the admins the ability to choose and synthesize what data set uh to use and by how much um what that does in the end is it gives the flexibility to tailor the capability to their use case while minimizing the organization to risk um agent assist also lays the foundation for it organizations to pull from popular AI data sources so when you're ready to integrate the underlying capability is ready to go um and this as a result saves time and speeds resolutions by rapidly gaining contextual information uh information that's not able to be uh derived any other way and uh you know related to incidents and problems so that agents can better tackle the same uh incident over and over without manually searching for that information so how is this done May oh yes go ahead real real real quick um that idea that agents can you know move faster because there are resolution notes or there is information leading them to the right answer to hands somebody can you talk a a little bit about how um you know a service now customer would integrate that kind of uh resolution note generation or um case closure um or or knowledge article generation how how that how that works and how they would have access to those kinds of um benefits yeah so um you know this this might be related to incident problem and change knowing what changes have been coming down uh maybe it's a patch update that happened last night that didn't go as well as we might have expected and as a result of that information what is the roll back procedure or what's the fall forward procedure so those are those are two quick uh examples um you know I also think about from a Knowledge Management perspective uh you know the federal government many times there's policy that dictates that we follow a certain order and operations for performing an action and when that's in the knowledge base uh just having quick access to that uh in the summary so that you have a predictable repeatable approach to uh solving these challenges um over and over or hopefully less often as a result of using now assist for itsm so uh we have a question thank you for that in in the in the chat uh CL asking for clarification um person asked what is called an itsm and itom married solution did you mention something related to that can you pleas absolutely so uh service operations Mar's itm and itom for a Better Together solution and it presents itself in uh in the platform as the service operations um uh um excuse me the service operations uh road map and uh platform uh uh excuse me the uh the Frameworks in there so you have the ability to understand what events are happening and cross correlate those to how uh those events are causing outages and how those events are causing uh other changes and what changes then need to precipitate and take action to Res to resolve those events as well so it's all all built into one so we we can we can dive more deeply into service Ops in a in a followon conversation if anyone's interested please you know reach out uh to us directly through koft and we can talk in detail the last thing I'd like you to to share a little perspective on is the um the virtual agents that can uh it you know that are enhanced through generative AI the the chat capability enhanced and you know that's one thing that as as uh more and more customers are using gen Solutions in service now they're seeing as a way to really deflect incidents from a human they're seeing opportunities to really restructure the way questions get answered so talk a little bit about the difference what people we've had virtual agents for a long time what's new how's it different sure yeah that's a great question and so um you know not just incident resolution but also self-help and avoiding to have many people uh work on different requests so from the request management process and some of that information can come from both of those data sets that I alluded to earlier or it can come from those knowledge articles and pull that information from those knowledge articles so that if I do need to go in and uh you know hypothetically have a uh an integration with OCTA or some other uh type of solution to reset my password I've got the ability to go in there and it will help me go through those steps to reset the password as well as it'll look in see maybe what I have not completed from a security training posture and recommend that I then take the security training uh so I stay compliant and things like that so it's the ability to cross correlate that information so that we have the ability to offer more to our to our customers and I want to pull I want to remind everybody about the slide Sergio shared about what service now is doing it's uh you know our our generative AI capability is understanding the intent he's understanding the question Chris is as Chris is describing it is synthesizing information from a variety of places like chis was saying it might go to a knowledge article it might go to a training database it might go to other places and using that intent and and and synthesis synthesization or synthesizing of all that information and creating something for you to react to creating a recommendation creating guidance so we're really fulfilling you know that model that that Sergio shared and this is an important way and virtual agents in the past you'd have to write out essentially a uh um a conversation you know what's a likely question and you know what's an answer and you could we were those are very successful we know that many agent many uh customers use Virtual agents highly successfully this is an incredible um evolution of that where the information in the organization becomes the basis of that chat and now I can rely on those virtual agents to answer lots of questions and help people where I used to have uh to need an agent to do that and I now have a new a human agent now I now I have these virtual agents that can be a lot more Adept at answering those kinds of questions so it was a very exciting uh Evolution I a big change for us so Chris thank you so much for your time uh I want to question awesome that you're you're here I want to I want to jump back in and um uh move move forward to our next uh polling question so uh Kaylee if you can put up that next polling question this is going to be on um the value of AI and uh and itsm okay thank you all right next um we have uh Matt Barrett who is going to uh join us to talk about now assist for customer service management Matt thank you for being here thanks so much Jonathan uh I'm thrilled to be here speaking with you all today about how now assist for customer experience really makes it easy simple and fast for government agencies to Drive Mission successes by leveraging the Investments that have already been made uh in the platform but before I dive into you know talk about now assist for customer experience let me just kind of zoom out the macro just for a quick moment here you know if you look at analyst reports um or reports from some of our largest Partners they're all talking about areas of opportunity for generative Ai and a lot of these are falling typically into two categories right number one is how do we improve that citizen engagement experience and the second is you know how do we improve our operational efficiency and Excellence Gartner for instance you know surveyed 25ish Executives uh about their Genai initiatives and over 40% are focused on that CX customer experience right so the energy is there and we're seeing that government agencies are really following in those footsteps you know we all want to improve the quality of service for the stakeholders that we serve also simultaneously driving that back office middle office operational efficiency um and a lot of agencies I think are still trying to figure out where do I focus do I focus on AI enabling my customer selfservice channels first you know or do I give my employees better tools to do their jobs and really the Brilliance of now assist for customer experience is that it lets you do both it can give agencies that easy button to drive key service delivery metrics uh things like customer satisfaction networ motor Score first call resolution um while also continuing to improve the service delivery within the agency and so you know how do we how do we do this right how does it work um well it starts by infusing AI into every customer and agent touch point so beginning with a customer who's searching for information uh in a self-service Channel like a web portal or a mobile app and having generative AI tailor a very specific response to answer their question right but if that customer needs to escalate from a selfservice to an assisted service Channel like a live chat with an agent now using generative AI to provide insight and summary to that agent about what that customer's question is um or what they've already tried or what led them to that point now when a case needs to be created right giving all the agents who touch that case immediate insight into the history of what's been done and what needs to be done saving them time and resources and then once that case gets resolved now assist is going to help automatically generate those resolution notes and even draft a net new knowledge article for that issue that you can then push back out to your self-service channels to deflect and contain similar issues like that from happening in the future now a lot of customers are going to then send a survey to to or a lot of agency are going to send the survey to your customers saying hey how did we do right rate US on a scale this rate US on a scale that maybe provide some free form comments now know geni can even summarize all those responses all those comments back and bring new insights into how your customers feel it's truly voice of the customer right it's like when I'm looking at a product on Amazon you know and I can see that there's 10,000 reviews but Amazon's actually just going to summarize for me a lot of customers are saying this they like like this they don't like that about the other thing right so imagine having that voice of your customer and all that Insight at your fingertips now in the CX side of things we also have a number of customers who use us in the contact center right so we apply things like generative AI to be able to analyze those real-time voice transcripts from phone calls and that's going to help you learn where your areas of service improvement lie and coupling that with the automation of generating call wrap notes right so every time your agent hangs up the phone they spend a couple minutes typing up what was done gen can automate that process as well and that's going to help contact centers improve a whole number of kpis like reducing their average handle time lowering their average weight time you know reducing call abandon rates abandonment rates and all of these are driving up you know cat MPS fcr um which is going to provide tremendous value for our agencies so to kind of arize right if you just want to think about three things that you know gen for customer experience provides with now assist is that now assist is easy simple and fast it's got a very low barrier of entry with these pre-trained llm models it's going to help you drive your mission success by improving citizen Employee Engagement and as well as improving your operation operational efficiency and it's going to enable faster adoption of new capabilities as we continue to innovate with those twice yearly releases that Sergio had talked about earlier Matt thank you that was a great presentation and I just want to the audience to know and perhaps you want to comment comment on this Matt know how um the the approach and the model you're describing with virtual agents um with you know using multiple sources of data to generate answers whether it was the conversation in a call center or you know other cases you they're very similar to what um Chris was talking about for itm you know and at our core we're doing this generation of uh content that you would normally previously have to create on your own in service now you know because we have access to the same sources that a human would have had Source access to and they would have to take their time to type it we can do that in seconds now absolutely and you know you're seeing CRI wrong but you're seeing this in in customers at customers you're seeing we're seeing this as a company um in the commercial sector private SE in public sector people are taking advantage of these these kinds of capabilities absolutely and what we're finding is that it's actually allowing our customers to be able to scale their capabilities um without necessarily having to scale up their staff right it's all about being able to do more with less while improving experiences for everybody and again we're doing this in the context of the service now uh application of the instance that they've had for a long period of time where they've already been doing these kinds of actions but using people to do them and we're describing a way where we can use the generative AI technology to do these things for them so those same people can be more focused on serving customers more focused on uh ensuring that their processes are the right processes and so on and so forth so elevates them to more higher value tasks right 100% so thank you Matt really appreciate uh you being here we're going to jump back into uh our presentation with with with another polling question on this you know important topic of customer service management uh Kaylee if you could throw that polling question up there I'm going to go ahead and at this time introduce our last presenter uh my colleague Joe fieldler who is a expert in HR Service delivery and how now assist is going to be a game changer here as well Joe please uh turn on your camera and join us um we're excited to uh to hear from you about how agencies are serving their most important customer their employees so Joe take it away thank you Jonathan so at you phrased it absolutely correct who are as our most important customer about our employees and so with now for HR HR Service delivery how can I use that to provide a better experience how can I save time so one of the biggest things we often deal with you know as public sector servants and you know internally looking at our documentation I have policies I have processes like job AIDS these are often you know multi-page memos that we need to review to find information that we need so let's turn that into a conversation you know I'm going to search I'm looking for something about my specific benefits or contract information that might be out there I can go to my service portal I can I can ask what are my benefits for x and it returns to me instead of just this search result for 10-page document it answers my question that single paragraph linking to that source so I got my answer I've saved Time by doing that search and immediately executing into you know that information I need another key piece of this with HR Service delivery very often we can get into the specific kinds of requests that we have with our organization you know I maybe you know I Joe feedler you know working with my agency I went to you know the Wharton School of Business and I took a JavaScript course for $600 and I asked you know the virtual agent about the tuition reimbursement policy well first it's going to confirm all right we're reimbursing tuition let me grab the information you gave me your school you went to the course how much it costs that's already out there on your catalog but let's grab the in missing information when did you go did you get your approval form signed you can you attach it where's your transcript but it's basically guiding that user through a conversational experience of immediately connecting to HR and begin that line of service and to that other side you know we want to think with HR they're often working through these cases that can run not only you know days and weeks but even months long and they're Gathering a lot of esoteric information together in one place and me coming back and looking at this on a weekly basis it grows it can be you know up to 20 minutes to maybe even review this case depending on what I'm looking for and what I'm trying to put together and just like we saw at the other Solutions you can summarize all this information the chats the different activity that's going on and I can see what the initial issue was what actions have we done what's happened in this case as well as providing a resolution for that and just like my colleague before me mentioned you know I can take those those resolutions I can put that in the resolution notes So Gone is the day and that practice of oh case solved close we have meaningful informationless resolution notes and that can be used to generate those knowledge articles that we need forward we're not working from a b blank slate anymore and the kind of third key piece of this is also understanding with the kind of data interactions we're using with generative Ai and our large language model HR especially needs sensitivity detection if I come forward with something such as a labor grievance an eeo complaint you know that language needs to be picked up and saying okay this is something that is sensitive and highly impactful I want to make sure that that employee is not suffering in silence and through a process and get them immediately connected with the individual human person they need to go to so this is where we were talking back earlier about having those fallbacks that are required to go to a human person this is the type of issue where we need to drive directly there and and again Joe you're following uh in HR Service delivery now assist is following a similar model as we saw for IT service management as we saw for customer service management customer experience we're we're we're enhancing people who are serving customers because we're creating the resolution notes we're creating um you know we're documenting uh or summarizing a case so they can get up to speed faster we're making it easier for people to use service now the generative AI Technologies are a a tremendous accelerant for using service now effectively serving customers effectively our internal customers uh our employees our external customers might be another government agency might be a citizen you know we can just do it faster we can do it better and just like you you you were sharing we can take those same uh HR uh Specialists who spend a lot of time researching and understanding answering questions and train them on more important things maybe they become focused on uh culture or maybe they become focused on uh you know uh other processes or integrating systems or other kinds of things that haven't been done yet um so we're changing the game for how uh HR Service delivery happens with with now assist so uh Kaye I want you to put up our last polling question on on HR and I have just a couple of concluding thoughts to to share um so you know we're spending a lot of time on these things I think so let's see what the what the audience says so Joe thank you so much um appreciate you being here I just want to um you know thank everyone who participated in today's uh conversation uh both the service now team and everyone who um you know who who listened in it it was really uh quite an engaging conversation I felt very informative My Hope Is that this is only the beginning of a conversation it's a dialogue between service now and everyone who joined us today in your colleagues and your uh the rest of your agencies there's there there's so much about this topic from the executive order that we talked about in the beginning to how service now platform is is designed to support generative AI capabilities inside the context of our workflows inside the context of a platform and application you're already using and getting value from as opposed to geni that is um wholly new and separate and a new thing that you need to acquire we made a a very strategic and conscious Choice inside this company to apply it this technology to make service now platform better to really be the intelligent platform for the way we transform the way our government agencies operate so we're very proud of the work we've done here we are not done we have a lot more to do we have a lot more to share and look forward to continuing this conversation our next service now release zadoo coming in a in a in a few months is going to have more generative AI capabilities than we think any other release that's ever ever hit the market so we know that there was a there were a number of questions in the chat we answers so we're at time so just a one final thank you and um we look forward to uh talking more very soon thanks and have a great day thank you team I'd like to thank all of our participants as well as our speakers for being with us today we hope this information that you received has been helpful if you have any further questions like he said or would like to request more information please feel free to reach out thank you again and have a great day