all right so we are going to get started welcome everyone good morning good afternoon good evening wherever you are joining us from today is the now assist for itm we're going to learn more about what's coming um and what has come okay so Safe Harbor notice as you may have seen this before um everything we talk about is based on our current beliefs um things are fast and fly moving at service now so keep that in mind as you make purchasing decisions in the future uh with that being said this event is a part of a curated event series to connect you with service now experts and peers um so that you can help deploy your products and Achieve value faster we hope you can join us again for another webinar or Meetup uh you can scan the QR code here to see additional um sessions that we'll have that'll be for itsm and other products within service now a few housekeeping items we've saved time at the end for Q&A so in the meantime please use the Q&A button at the bottom of your screen along the way this presentation will be recorded and shared on community I will be posting the link for that um shortly after this event you will be prompted to fill out a short survey and we really appreciate your feedback this is the first of its kind hopefully there will be many more to come uh Shashank will be monitoring the Q&A in in the um meantime so feel free to drop your questions in there at the end we can come off mute and chat more with that being said my name is Isabella I'm a product manager here with itsm primarily focused on everything AI ml automate within our products um I am joined today with shashan I'll let him introduce himself thank you Isabella well good morning good afternoon good evening everybody depending on which part of the world you're in my name is shishang Sager I'm a senior director product management for itsm and I couldn't be more excited to be sharing with you what we're up to of generative AI especially for itsm and what use cases we're bringing to Life as a part of our Vancouver release awesome thank you so much okay so how if you've been on a session with me before how I always like to start off sessions or webinars meetups whatever it might be is with a poll so I'm going to launch a poll here um our question to you is regarding your implementation timeline if you had a magic wand and could do things the way you wanted to how when or if you already have plans when would you be implementing generative AI is or specifically analysis for itm that be next quarter oh sorry that should say this quarter versus next quarter so that first one should say this quarter I apologize [Music] um yeah so it should be next quarter next year oh no I didn't say it right okay next quarter next year no plans to implement or other W my brain is still waking up this morning I saw next in both of them and I was like what did I do uh no worries so we're going to leave this open for about 15 more seconds and then we will read out the results and this helps us really kind of address you all as we have this conversation how um how to have this conversation especially if you're implementing next quarter next year or no plans yet we can really take the conversation really broad or make it more specific so I'm going to end the poll now um we got about 70% responses so it's 177% said um next quarter 31% said next year and then 49% said um no plans yet and 3% said other I'm going to open the chat to see if anyone said anything regarding other um not seeing anything in the chat but that's okay so this will really help so 70% of you responded and then um majority of you don't have planned yet so hopefully by going through this exercise really explaining to you our thought process behind analysis for itsm we can help um you create your plans and you know get more refined with your implementation timeline okay so I am going to move forward so um after talking to multiple customers one of the common things that popped up is organizations are pressed to provide better experiences and more efficiency almost all of them consider this their mission critical objective so really what we're focusing on as we move forward is efficiency and improving experiences and that's what we really wanted to bring to life with now assis for itm it's no wonder when you dig into the data that Harvest Harvard Business Review reports that 9% of time is spent reorienting the task after finding that information finding the information to complete the task or workflow switching from one incident to another is taxing um also 20% of time is l searching for information to complete a task according to McKenzie and then finally Gartner reports that 43% of employees miss important information because they have too many applications open requesters move on with their work quick and if it takes too long for a resolution it might be too late to be helpful so these are the things that we kept in mind as we were um you know discussing what to bring to life in now now assis for itm so employ employees and agents shouldn't have to struggle this way and they no longer have to with nysis for itm uh nysis for itm helps you drive the business so that you can increase operational efficiency employee experience and organizational throughput ultimately we want employees to get the information they need so that they can get back to the things that matter most which is their work so efficiency experience and throughput now assis for itsm helps lead helps leaders like you meet their goals by helping service owners agents and employees uh synthesize information faster for service owners specifically they can resolve outages Faster by getting up to speed on the problem faster therefore they're assisted with po incident reviews and Communications as well agents can get up to speed faster many times that means picking up where the last agent left off furthermore they are assisted with knowledge creation making adherence to KCs simple and then for employees they don't have to search for Snippets of knowledge in an article they really get the answers that they're looking for not just search results so when generative AI took the World by storm we immediately started thinking about use cases and we what we landed on is that it is not a fundamental shift in work but it's a fundamental shift and how work is done and so we set out to weave this capability seamlessly into the fabric of our platform the the application and workflows within now platform our customers data like yourself and the power of large language models is the trifecta that puts us in the very unique position to take advantage of these these kinds of generative AI capabilities such as text classification generation summarization supporting analytics Etc in ways that create you uh value for you while providing exceptional experience for your employees so why nysis for itm well it's our expertise our applications our workflow that stand to benefit the most from our Ai and the fact that we're not we're the fact that we are obsessed with you our customers that makes us in the best spot to bring now assis for itm alive and to really create this um expertise with our application and workflows and our Obsession of customers to that is how we approach um this generative AI future um so I'm going to go into some features but before I do that I'm going to pop in into the Q&A and there's a question around what is KCs um so that's knowledge essentially Knowledge Management I'll go back to that slide um where support yeah exactly so I think we were talking about it here with agents for KCs um knowledge creation support so thank you any other questions just add one more thing to it and knowledge centered support just to elaborate slightly and as if I I see your on as well so chime in too uh is really all about making the work of the agents easier making them more productive and giving them a much better overall experience so some of the use cases that you're going to see today hopefully are a start into uh that Journey where we continue to strengthen the agent productivity and the overall experience with employees also um so let me see if there are any more question oh no thank you all right so we are going to move in to a few slides but rather than overwhelming you all with slides continue to overwhelming with slides we are just going to do two slides and then we're going to um go into a live demo so you can see firsthand what it looks like um so before I do that um really the first step is is that we're enhancing is AI search this is not a new capability but the and the idea is pretty straightforward so given a search cery query um returning relevant KB articles um like I mentioned this works because it's giving results but our employees are really looking for answers so with this capability it's helpful for employees service owners and agents because they get answers faster rather than just search results so um it reduces mttr and improves experience so that everyone is left better delighted um so now you're getting answers not just results so that's the first thing that we wanted to touch on so it's now assist search and then now assist side panel um think of agents that need help to um summarize a task or an interaction generate knowledge articles generate resolution notes well the side panel now allows you to be on an incident on a record and take action um in the side panel uh whether it be summarizing what you're seeing in front of you creating resolution notes um Etc it's easy to access it's contexts aware it's guided and simple interaction methods really helping you reduce time spent on a task and driving deflection so that's what you get with now is this side panel um again you can kind of see what's going on here asking how can we help today can you summarize this incident and then you get a quick summary rather than having to go through and reading what are sometimes extensive activity streams and back and forths between an agent and um an employee so next we're going to talk about incident summarization chat summarization and and um resolution knows but again rather than just showing you more slides I'm going to hand it over to Shashank to do a quick demo all right so I'm going to quickly share my screen Isabella let me know if you're able to my screen okay uh yes now I can all right so what I'm showing here is essentially the experience of an employee as well as and the name of the employee is David so that's what you see on the left side so when employee David logs into their uh employee Center they're able to fire up their virtual agent chat so that's what you see on the on the left side and on the right side I have my agent her name is Beth so Beth comes in to the office in the morning she makes herself available so that ways U she knows that she's now able to take incidents if they come through but David comes in and he realizes that his email isn't working so he fires up the company portal and says I'm having email issues so this is a very typical virtual agent conversation where the virtual agent wants to start to diagnose these issues uh it by itself um and then asks some questions so it's very conversational and then the virtual agent wants to to be able to know if the issues are with a computer or iPhone go through pre-built prompts that actually ask questions around have you installed InTune which is actually one of the the typical software that is needed uh this is the time where the virtual agent is out of its keys so it says would you like me to transfer the issue to a live agent so virtual agent says yes and on the right side you can see the incident from David Miller has now been passed over to Beth who is our agent live now typically what you what would happen is when uh the agent accepts the case they would have to go through typical prompts with the employee to be able to ask what really happened now not a great experience if you think about it because uh the employee now has to be able to give the same information potentially twice but vaila if you actually look at generative AI it not only detects what conversations happen in Virtual agent but with a full context it's able to summarize the uh what steps have been taken what potential issues they have so as we again think about knowledge Center service this provides an elevated experience not only for the agent but also for the employee because now they're not having to repeat the same conversation over again now typically this is where U the the typical conversations would would go on so um you know uh but I'm not going to go through building the entire incidents but in order for me to be able to show you some of the other powerful features that are available in generative AI I'm actually going to switch to an incident that already all of these conversations have happened so fast forward we're now in a scenario where we do have um lot of back and forth that has happened between the employee and let's say David in this scenario and now as the diagnosis is happening the agent Beth wants to be able to see what steps she's actually taken in a very quick way so she can come here to summarize and now with the power of generative AI VOA again feels like magic but I I believe us that this is all powered by llm from service now so it's absolutely secure uh we are very careful about the data sharing and and how do we make sure that we provide full pii and confidentiality but at the same time we do provide functionalities like this where the summarizations can be provided to the agent in a very short amount of time so what you're seeing here is again the continuation of the elevated experience for the agent and what we're finding is that with using some of these features the overall product activ ity of some of our agents are increasing anywhere between 10 to 20 minutes almost entire day so before we actually push any of these features out to our customers we always make sure we test them our own environments with our own agents who are actually be able to provide support to our employees so some of the data and the information that I'm sharing actually has come from the studies that we do there now last feature I wanted to be able to show today in today's demo is we move forward Beth moved forward she's able to help resolve all of the issues with David and this is typically the last step where uh if let's say Beth was to resolve the issue now typically she's going to have to be able to go and type in the resolution nodes um but in this scenario because of the power of generative AI the resolution notes are not only autop populated but they're also provided and written in a very consistent format so thereby reducing uh for the next agent if somebody wants to be able to come back and refer to these incidents not only does it make it makes it easier for them to be able to get up to speed but also for our Beth who's our agent makes her experience seamless increases productivity and the overall experience from the employee to the agent overall is seamless here um but thank you everyone for joining today we appreciate your time hopefully you found this informative thank you Shashank and OIP as well for helping answer questions and driving this session so if there's no other questions feel free to um drop off and we'd love to hear your feedback if not we'll stay on for another moment or so um to answer any additional questions thanks folks thank you everybody