okay connie you wanna kick things off here with the recording yep done okay hi good afternoon everyone um thank you for joining today's gopro presentation on predictive intelligence and the predictive intelligence capabilities within servicenow my name is mobi invade i'll be leading today's session alongside a colleague named david williams i'll do a quick introduction of myself walk through the agenda and then you know we'll get right into the session um a little bit about me i'm a solution consultant within servicenow and i cover our federal health care accounts uh i am local to the dc area so um you know i guess having some nice weather these days uh but yeah they've been around federal and federal healthcare for some time really excited about this session and some of the topics that we're going to be able to talk about today i'll kick things over to david really quickly to do an introduction uh for him to do an introduction about himself real quick steven yeah hey welcome everybody um yeah my name is david williams working alongside mobeen today partners in crime i'll be giving the demo portion of today's demo so uh as well i've been uh solution consulting for a little bit of time now uh just over a year so it's very exciting time for us at servicenow and we're really looking forward to what we have to share with you all today so back to you moving cool thanks david i guess we'll go ahead and jump into our session today um a couple of things number one please double check to see if you're on mute or not i i i think a few times i've heard some background noise and so if you can please mute your line um that'd be helpful um for everyone this session is being recorded and so provided as a as a follow-up alongside the presentation and the deck that we're going through for questions please go ahead and ask your questions in the chat what we'll do is we'll save those questions and you know if there's time in the middle we'll try to get to them or we'll just save them until the end there so we'll start off by giving an overview about the topic itself then we'll dive into predictive intelligence highlight the capabilities and sort of what they are what do we mean by by predictive intelligence when we say that uh we'll transition from there into a demo and then we'll talk about next steps which is really going to be a lot of resources and content material that's going to help you to get more familiar with it and really even get hands-on with the application and then you know we'll open it up and address some of the q a for the next slide here so those of you who have been participating in this series on predictive intelligence and itsm professional capabilities uh last week we had a session on virtual agent uh today we're focusing on predictive intelligence and then you'll see these other three topics are going to be hit across the next three weeks as you can see here you're seeing some statistics around benefits that organizations have received by leveraging these capabilities so incident deflection by way of virtual agent predictive intelligence really giving that boost to agents in terms of making them more efficient in doing their job now these numbers and percentages are a reflective of studies that we've done with customers there are some of these numbers are industry studies as well and some of them are also informed by what we've seen as an organization um with our now on now platform and so that's really servicenow's usage of its own application and so you know like to say we drink our own champagne right or or drink our own kool-aid here um and so and so you're seeing some of the benefits that organizations have received and appreciated as a result of leveraging these platform capabilities and so you're seeing across there the five pillars of the itsm pro package that all work together and today the predictive intelligence stuff and so diving into predictive intelligence let's um what we're going to do to start today off is we're going to start off with the poll question here give me one second and uh and launch this poll um one second here um let me just get the polling going and so what i'm going to do is prop up a question for the group just to start start getting people's thinking going uh about uh about predictive intelligence so which capability of predictive intelligence is something that uh you can see your organization leveraging um so feel free to go ahead just respond to that pulling question you know we'll give it a second here for people to uh to respond a couple of responses that we've received so okay about halfway there with responses we've got seven people that still need to respond i'll close up the poll here after a minute and just talk a little bit about what we're seeing um with the response and we'll talk through all these capabilities that are there in the question as we go through the presentation here so 10 more seconds okay and let's close it out right here okay so it gives a final 20 seconds here for participants to submit responses so now we're at a 10 second countdown okay so what we're seeing by way of uh poll results a lot of distributed results actually so uh you know agent assist seems to be a big big point of interest so leveraging the uh similarity framework and so we'll definitely show that as part of the demo today and we'll talk through it uh the automatic categorization and assignment of incidents we saw some good traction there and then knowledge demand insight not as many people's about seven percent responding on that but the top two really taking up a good chunk of those who responded so very cool that said we can we can sort of track forward here and talk a little bit about what predictive intelligence is and what it enables so you think about protective intelligence and artificial intelligence these are capabilities that you end up interacting with in a lot of other systems as well especially consumer facing systems this is really enabling those same capabilities within the enterprise and predictive intelligence allows employees to focus on more meaningful work by powering those workflows with machine learning so now routine tasks get automated you're getting quicker resolution you're enabling deflection so fewer and fewer users actually have to submit incidents right you know that they're able to figure things out on their own and we're enabling all of that through these capabilities that just facilitate the end user experience whether it's an employee or customer or alternatively making life easier on our agents and fulfillers that are actually having to track these incidents that come in and support them so let's talk about some of the benefits that come out of predictive intelligence uh you know a big one is just that csat right customer satisfaction improving efficiency um but also working faster working smarter right you start looking at statistics around how long it's taking us to close out some of these incidents especially ones that are routine ones that we see coming in a lot how do we get done with those quicker especially when we might have different people in the help desk or different agents that just have a big load that they're shouldering right some of that can be supported by just automating activities right we're getting a lot of people who come in just need to reset their password and do sometimes tasks that are really um rudimentary right things things that should be easier for them to figure out and that we can help them out with um you know big big value out of our predictive intelligence capabilities is that you don't need a data science expert to really come in and configure it it's configurable within the platform you have a guided setup right you have templates out of the box that facilitate with rolling this out and then you can tweak it and configure it to your own business need and you're seeing that fast time to value zero coding right so you're not having a developer go in and have to write something complex you're just having capabilities that are there within the platform that can start being configured and used um and so there are a couple of frameworks that machine learning traverses within the application platform uh the first of these that you're seeing here on the left is the natural language understanding and so what that does is based on what the end users put in like that syntax we're actually starting to interpret that more intelligently so we're saying okay you know people people are putting in a question and you know email and issue and now what we can do is present relevant content and information to help them diagnose their problem and really figure out how they can solve it either on their own or for the agent themselves uh when we talk about classification here we're talking about auto populating fields that are critical to the actual incident or case and so that may include things like how we're categorizing those activities or sub categorizing them it may also help us determine what the right assignment group is or who this should be assigned to um the similarities i think are pretty straightforward right so what we're doing is we're linking available incidents to similar incidents or relevant knowledge articles right so we're saying okay you know we're trying to resolve this here's how we've resolved other incidents that are like this one right and so we're creating those linkages to give agents that additional insight right someone else in the organization's already done this you don't have to start from scratch and figuring out that in figuring out where that information sits and resides something that agents are able to do you know without having to do big email blasts calling people up right we're enabling collaboration in a way that's automated doesn't require a lot of men any manual intervention and then that last part is clustering this is a really really powerful part of the platform when you start talking about plugging knowledge gaps um and having more efficiency so what we can do is you know even with incidents right you can have a lot of incidents that are taking place and you say you know what we're relating all these incidents together and what we might have here is a major incident that needs to be created right and we can correlate all those individual incidents to that major incident record and clustering is actually surfacing up that recommendation for us based on an analysis of the of the platform and the activities that are occurring within it now a little bit about the journey within servicenow and what we've done um you know a lot of organizations today talk about machine learning artificial intelligence natural language understanding but then the question is you know how's this really been enabled within the applications what's the work that's been done how simple is it to leverage and use and for servicenow this is a journey that we've been on for some time and so you're seeing there's some of the some of the tools that we've acquired to help enable those capabilities within the platform starting in january 2017 right enabling intelligent automation clue also in 2017 conversational virtual agent um parlo you're seeing there in may of 2018 and so that was you know natural language understanding um we also have additional uh acquisitions that we've made so loom within ai ops so that helps us analyze metric data so we can now proactively address issues right so we're seeing that built out within ipsm and our it operations management tool passage ai as well conversational ai company so now what we're doing is leveraging that natural language understanding and conversational capabilities across multiple languages right so not just doing one language but also additional languages so if it's an inquiry that comes in in german or japanese or another language idea is that all of our core capabilities and the virtual agent everything else can still address that so what you're seeing is is us as an organization really making serious investments in machine learning and and streamlining all of that within the platform in a way that's simple to adopt simple to leverage and simple to use now what if those investments produce for us right what it does is it enables our customers to really take advantage of these capabilities in the way that's secure and so we recognize the uniqueness of our customers and different organizations and obviously this is especially so in the federal space where a lot of agencies are are a snowflake right they have their own peak times when it comes to production activities they have their own vernacular and language and syntax they have their own mandates and requirements right and being able to have a predictive intelligence capability set that's responsive to that right is important because what you want is accuracy what you want is are predictions that actually enable the business and don't impede the business right things that aren't just set in stone and that reflect you know uh organizations and businesses that have no relatability to what you do versus having something that's really learning which is really learning based on your own use of the application that allows you to go in and tweak and configure that to be updated right so updating automatically and also updating through you know things that you can do to see hey here's an area in which we can have it capture something that's really specific and unique to our business so with that said we'll be kicking things off here and i'll be transitioning over to david for the demo um these the personas that you're going to be seeing with joe employee and linda jackson you're going to see a lot of capabilities right the service portal and agent experience but with that said let me let me hand things over to him yeah thanks for being if you could just stick on this slide real quick um yeah so everybody for for our demo today we are going to go through two different personas really to help emphasize where predictive intelligence is providing value to different types of individuals within the enterprise so the first persona we're going to see is joe employee we're going to check out that service portal so if you had the chance to join the other itsm pro session last week then you would have had some exposure to virtual agents so we'll touch very lightly on it but today's emphasis is going to be around how an end user can take advantage of predictive intelligence frameworks within the virtual agent to help them either troubleshoot or get incidents assigned to the proper area in the support desk and then once we take care of that we're gonna switch things over to the back end check out agent workspace from linda jackson's persona she's our tier one agent and so what we're gonna do is identify those few key areas that are really help saving lots of time for those agents in terms of automatic um you know assignment and a few other areas that we're going to touch deeper on but really identify those areas where predictive intelligence is optimizing the way our agents are working and then lastly we are still going to stay in linda jackson's persona but we'll take a look at the platform ui that way we can kind of touch on how we can configure these machine learning solutions and the reason we want to do that is because it can seem like a daunting task right machine learning configuring these it could take a lot of effort but what i want to show you all today is that with a few key simple steps you're going to be able to get this implemented into your system um fairly quickly so uh without further ado mobine i hope you don't mind but i'm going to go ahead and take control here sure presenter share screen all right so this will be the first and only time i ask i promise but uh but being helped me out can you see my screen yes i can see your show all right perfect we're off to the right start so all right as i mentioned everybody today we are going to walk through a couple different personas so i like to tell a story to help really identify the value here so for our story today joe employee is a regular employee at servicenow comes into work in this morning and his email is not working what does he do first and foremost go to the service portal and now what we're going to check out is how he can walk himself through submitting an incident using virtual agent so as you can see here and let me move our faces so as you can see here we're opening up virtual agent so virtual agent again there was a session on it last week if you didn't get the chance to see it please check out the recording there's lots of good information there but what we're going to do is start to walk through the self-remediation process so for joe today email's not working that first part of predictive intelligence is natural language understanding so you know for joe he's going to type email is not working it's very standard we're not using boolean operators or anything standard language as that comes in that first step is that we're the virtual agent is going to try to identify the intent here so basically you know what is joe employee trying to get done or what is what is he is he actually looking for assistance with so right here it gives a couple of different options but today it's email issues right email's not working and basically at this point virtual agent has started to narrow down his his um what he's looking to get done today and so we're going to see that first part of predictive intelligence very soon so now that we've gotten down to what type of issue we understand okay we're having email issues well get us down even further what kind of email issue can't send email so now we have identified that the entity that so the intent is getting assistance and the entity is email we want assistance with email natural language understanding is going through this process to help understand okay what is joe asking for and how can i help and that first line of defense right here is um this this kind of mess this kind of messaging we have here and so you know the reason this is here is it kind of acts as uh you know as incident deflection right if we could go through this first step in troubleshooting and it works out great we don't need to submit an incident but that wouldn't be as much fun so for today we're going to say that hey these steps did not work so now that it hasn't worked what you're seeing is that similarity is that similarity framework at play here so what has happened is virtual agent has identified the uh intent which is uh can't send or need assistance with email and so based on that on that description of email issues can't send email the virtual agent has given through a couple different resources here available and it looks like the knowledge base and what it's done is it's surfaced similar knowledge articles right so that's where predictive intelligence is coming in for that first line of deflection here is because it's bringing in knowledge articles that may be relevant towards helping joe self-remediate again if we can self-remediate for our employees then the agents can work on more complicated tasks so right there we see these similar knowledge articles again we're going to go ahead and assume that joe read through them and they did not work systems not perfect almost but the system's not perfect so for that being said we're going to go ahead and move forward and say no this did not resolve our issue so after selecting no basically it's that last step here virtual agent says okay i've understood what you're asking i've provided you with a few different similar knowledge articles last step do you want do you want to open an i.t ticket yes we absolutely do so this is where we're going to see another concept from predictive intelligence come into play here so as we can see the virtual agent has in fact opened an incident on behalf of joe now those areas where predictive intelligence is coming into play is the classification right so of this incident what category does it go to well based on the understanding from virtual agent of this being an email issue it knows to categorize this incident in the software category that's that first step of classifying where does this need to go and then it takes it a level deeper what's particular group within the within that support group that can help with software issues what particular group are we assigning it to so now we've classified that it goes to the software support group right and again this is saving a ton of time because rather than having incidents come into the queue stacking hundreds of incidents and trying to analyze read through them and assign them we're letting the system take the work out of it for us and by applying that machine learning solution not only are we able to classify where this where this incident is going but it's going to continuously learn so that if it's not 100 accurate it will continue to learn based off of the previous incidents coming in to to further um to further give confidence towards where or what type of classification is going to be provided so you can already start to see a few different areas where the uh the predictive intelligence is starting to save time not only just for the user because it was very simple for joe to identify how to submit this incident but also for the agent because now it is directly going to be routed to somebody within the software group and they can get moving they didn't have to assign so again today we just wanted to focus in terms of virtual agent on the predictive intelligence aspect but these are huge advantages that you can absolutely take care of and it's very simple to configure and again we'll touch on that later but just food for thought so with that being said um now that the incident has been submitted anything else we can help with nope you've done your job so at this point we're going to go ahead and switch over to linda jackson who once again is our tier one agent on the back end so at this point what you're viewing uh by the way i'm logged in as linda jackson now we are in a different persona what we're viewing is her agent workspace and so this agent workspace is that one-stop shop that we have our agents go to in the morning to really help identify what work do they need to take care of first and in that same vein of helping them identify the prioritization of their work what we have is this inbox area so real quick not to confuse it with a general inbox queue this inbox is specific to linda and so this is where that predictive intelligence is going to come in again so how this is working right now this particular incident that was just submitted by joe it has not been put into a queue it has been assigned directly to linda now the way it's been directly assigned to linda is through what we call advanced work assignment which helps the system analyze kind of the information and the incidents and assign it to an agent but on top of that uh predictive intelligence is really coming in here because it has also helped to identify based on previous incidents that have been assigned we are going to continue to analyze those and now we've also identified linda based on however many incidents are in the system linda is the best fitted agent for this particular case or for this particular incident right so now we can see that um aside from the automatic categorization and that classification framework now we're also taking it one step further and using that same framework to identify linda as the best fitted agent for the job so again it's saving a lot of a lot of time because not not only are we not manually assigning these incidents but they are the optimal agent and so as you'll see again you know it is a machine learning solution it's constantly learning it's in the name right so if it's not correct we have the ability to let the agents train the system right so it can tell them to reject it give them the reason why put them back in the system it's going to take that into account the next time it's going through this process but for today the system did work correctly linda is the best fitted agent for this particular incident so we could dive in here now again today is all about predictive intelligence so let's take a look at some of those actions already taken so in this details tab here we can see a variety of fields have already been pre-populated once again due to that predictive intelligence solutions or those classification solutions so what we can see is once again the category has automatically been determined to be the software category we can identify how do they contact support and on top of that we can see that again the assignment group has automatically been identified and the assigned to has automatically been identified again lots of automation going here it's very quick and that is the point we want it to be as easy as possible for agents to pick up an incident and get moving on remediation so kind of talking about remediation that segues into that next area where predictive intelligence is benefiting the agents which is agent assist so in this particular area an agent assist predictive intelligence is working in a few different ways so if you think about it the classification of the incident was done through a um was done through a classification solution right it has identified how do we classify these incidents so where where agent assist comes in is starting with that similarity framework so what it's going to do is recommend several relevant pieces of information from various different resources and surface those directly in front of the agent so now instead of swivel chairing to a different um to a different system to try to do some research on remediation tactics the research can not only be done directly from within the workspace but the automation has already taken place because we can see that that short description has been used as context for that similarity and now we're seeing knowledge articles that are potentially relevant to help with resolving this issue so once again i repeat this is a huge time save because now we do not need to navigate to other systems everything within the system is up for grabs is able to be used in terms of remediating these types of incidents right so if we have uh knowledge articles in the system already we want to bring that forth to the agent so he doesn't have to go search but let's say that linda doesn't want to read their knowledge articles let's say she wants to look through various other resources no problem at all the similarity framework can be configured to to search through any type of uh resource we have within the system right so uh for example if we wanted to see similar let's say similar incidents right maybe there's an end of the system i believe there should be outages perfect so let's say we wanted to reconfigure that search to find similar um incidents that are pertaining to an outage right so again these incidents these records are in the system so if we could surface that and bring that forth to the attention of the agent now they can identify hey it looks like there is an outage so maybe this this email issue is related to that we can start to tie our incidents together really start to streamline that process of identifying you know kind of deeper issues here and then working from the ground up so a lot of different capabilities here but the point being using a similarity framework that's going to take a lot of the research and a lot of the time out of finding relevant information so you know with that being said like for example if we go back to let's say similar knowledge articles right so if we were to do this again uh there we go let's go there it is so if we go back to knowledge articles you know again since this has been brought forth not only do we want to be able to let the agent know hey there is similar knowledge articles but we give them a direct link to read it so they can go through read this knowledge article and then as that kind of final step right the predictive intelligence has taken us this far now the next step is that well what if we want to identify hey this is the knowledge article that um you know that that joe employee would be able to use to self-remediate well instead of typing it out he could attach it and it'll go directly it'll be attached directly to this incident so once again we are providing a very end-to-end experience here that we're providing a very holistic experience here all from within the agent workspace and so we've identified first how we're saving enormous amounts of time by using that that predictive intelligence or using the machine learning solutions to help identify the right agent and then it's taking it a step further to help the agent remediate the issue itself so again now now we can start to put the pieces together in terms of all the various ways that predictive intelligence is not only helping our end users but helping our agents to do their job more effectively so with that being said um you know now that we've seen the end user how they can take advantage the agent how they can take advantage i've mentioned a few times this is very simple to configure it's not a daunting task so let me give you a quick sneak peek into what i'm talking about so here we are in the platform ui right this is where the admin can start to configure some of these solutions so as mobina's touched on in the slides there are a couple of different frameworks that could be taken uh taken advantage of from predictive intelligence natural language understanding classification similarity clustering but the point in case being we can configure whatever solution we want and we can configure it in whatever way that we want so the first kind of way we'll touch on it is by touching on these classification solution definitions so to give you a brief walkthrough a very simple example that should shed quite a bit of light into what we're talking about for this incident uh let's say this incident assignment classification right this solution here so what we're doing is we're actually we're looking at historical data you know looking at the data from within the incident in the system and then what we're doing is telling it to run a solution and drive an output based on that historical data so once again looking at what's in the system analyzing the information driving an output so with that that concept in in mind for incident assignment this is how we got that incident assigned to linda first we what did we want to do well we wanted the machine learning solution to reference the incidents in the incident table what did we wanted to analyze we wanted it to analyze that short description field so for example the can't send email now what did we wanted to do after it's analyzed that short description provide an output and that output is the assignment group so once again this was very simple to configure mobile touched on the fact that we can do this with no coding ladies and gentlemen i do not know how to code a single line of code and yet i'm able to configure this solution right here from within servicenow's platform so again there's a lot of different capabilities and huge time saving and not to mention money saving as well because an admin can configure this and you don't need to pay a developer or an fce developer right so again a lot of different advantages to take to take care of here so you know again we're talking about the various solutions within predictive intelligence so very same format here in the similarity definition so for example when we were in the agent assist from the agent perspective we took a look at similar knowledge articles well that didn't just magically appear i wish it did but it didn't that the way that we did it is we identified okay we want um predictive or machine learning to identify similarities what do we want it to do take a look at the knowledge articles in the knowledge table analyze the text or description description fields and what we wanted to do is based on that bring forth relevant knowledge articles to to the attention of the agent and the reason we have this update frequency here is is because maybe there are new knowledge articles being input into the system every day yeah for an enterprise level organization it's very difficult to keep tabs on all of these things and as you as you shouldn't right so what we can do is keep the machine learning um running right so every we can configure every one day run a scheduled job update add those new those new knowledge articles into the system or vice versa if we're if we're end of lifeing certain knowledge articles we'll take them out because maybe they're not so relevant anymore and so where this really starts to come into play in terms of value is we can update the knowledge article every one day but let's say you know let's say it's something more um more imperative like like major incidents right we want to know we want to bring forth major incidents so maybe every one hour we'd want that to update so that way we can have as much of the relevant information possible available to those agents for usage in terms of remediation so you can start to put the pieces together and start to get some ideas in your head these are just a couple of examples of the way that we've configured the machine learning solutions to help both the end user and the agent but the possibilities are truly endless when it comes to setting up some solutions for your own organization so um last thing we'll kind of touch on is so let's say you know we've gone through how we can configure those solutions well once we've configured a solution so once again we're going back to my reference of the incident assignment how do we test it how do we make sure it worked right we don't want to just kind of set a solution and then and then let it go right so here is where we can start to identify okay it has analyzed all of we've ran the solution it's analyzed all of the incidents of the queue it starts to give you some numbers around how accurate is it what is the what again yeah what is that that accuracy level in terms of um finding the correct incident assignment area so what i mean by that is let's say you know we know that it's 98 96 uh accurate well let's start to test it so there's a few different ways that you can actually test your solution and again the reason we want to do this is because now we can start to identify hey maybe we're not as accurate as we'd like to be so let's kind of add some more things in change a few different metrics to really help starting to optimize the solution but for example let's say that we want to test this so if somebody puts email the word email into um maybe virtual agent right or when they're submitting an incident they the word email if the solution identifies the word email how accurate will it be in terms of helping to assign it to the right support group well if we type email we can run the test and just like that it looks like the machine learning solution with 99 confidence will assign this particular incident with the keyword email to the right support desk so again we can see a lot of value here not only in being able to easily configure the solution but test our solutions to make sure that they are optimal because at the end of the day we want this to provide benefit right so just because we ran a solution doesn't mean we're going to put it into play if it's 50 accurate we're going to go back to the drawing port but this area really allows you to start to dive down and really optimize those solutions there so below you'll see you know this is where it'll start to break down okay how confident will we assign incidents to these particular different support groups so it really gives you a breakdown of all the information so really you can get as granular or as high level as possible with your solutions and as well we can test it for accuracy so with that all said and done um you know today we walked through the uh the end user which was joe employee talked about virtual agent talked about how predictive intelligence comes into play with you know that that classification framework we're saving a ton of time by automatically classifying and assigning it to the right group and then we switched over to the back end we saw linda jackson's persona we saw how not only did the incident get directly assigned to her incorrectly but now she was able to take advantage of similar resources within the system due to that similarity framework and start to pick through and find whatever resources she found most beneficial to you know help her really start the remediation process and then again once again the point being this shouldn't be a scary or daunting task the the main takeaways that we really want you to keep in mind here is that you know the solutions are constantly learning they're they're constantly updating we can get as much of an accurate desired output as we want and they're pretty quick to test for validity so again there are a lot of takeaways here but at the end of the day now that we've seen it all how can we you know the next question could be how can we easily track you know how well is it performing we've configured these solutions predictive intelligence is up and running what is the value that we're deriving so that's where we can take advantage of something like a predict a performance analytics dashboard around predictive intelligence so this is just a quick snapshot i took earlier but you kind of get the point here where you can configure reports to help us identify whether or not our predictive intelligence is performing correctly right so you know uh we can identify a few different reports here that we can put on the dashboard but once again it's just as simple as possible to get it started get it running and make sure that we maintain the solutions so with that um that does conclude the demo portion of today's demo i know we covered quite a bit of content so i'm going to go ahead and let mobine uh take things back over and then uh we will continue on with the with the dump or with the presentation thanks david um we'll go ahead and take presenter rights back again and then go ahead and share my screen okay can everyone see my screen okay okay so again just sort of recapping a little bit of what you saw in the demo you saw those two personas with linda jackson and joe employee you were able to see the virtual agent the way that an end user can navigate across it submit an incident directly from there have that automated classification take place and then you saw linda jackson going in and some of the benefits that she got as an agent when she was looking it up reviewing knowledge articles that were related to it um outages right that might be tied to the incident to give additional insight into why it might be occurring right and then we went into the platform and saw some of the capabilities and where all that's configured um pushing forward here real quick i was going to now launch our next poll question so give me a second here and launch our polling um [Music] and so yes uh nope no okay and let me go ahead and open up this poll so this question will be around natural language understanding give it about a minute here for people to respond and so you're going ahead and seeing that poll question just uh just feel free feel free to respond there and this is really more of a knowledge check here well about coming up on 30 seconds here about seven people that have responded so about half the group uh we're gonna be on a 20-second countdown in a second here so um let's go ahead uh let's see here okay so we just went ahead and closed it out so um responses thankfully everyone got it right right so whoever responded got the question right so natural language uh understanding right it's really understanding that end user intent right and presenting relative relevant feedback so uh so good on everyone for for getting that question right um so let's talk a little bit about next steps um and so our next steps here really just going to be uh going over some content i think is really useful for for anyone here who's interested in really building on this webinar and learning more about the capabilities set the first is an e-book first is an e-book and so this is a predictive intelligence e-book that you can access a lot of good information around predictive intelligence within the platform and just different things that we've done for organizations so you'll see use cases in there and then in addition to that a lot of a lot of good material that goes over the capabilities themselves in addition to that this data sheet that's available so you're talking about something that's a bit tighter right so a couple of pages that goes through the actual capability set what the business challenges are and then the service now um the servicenow solution that we're providing and and the benefits that can be gleaned from that um also a white paper so data sheet white paper ebook right whatever type of collateral you're sort of interested in reviewing right we we have it here right and so a lot of material depending on the different type of user that you want to surface this type of information up to or circulate it around with within your own organization we have forms of collateral that can be pulled down and leveraged for those users and so you'll you'll have all these links provided to you within the presentation that we disseminate as sort of a follow-up to all of this you also see on a customer success site we have a success playbook and so this is really getting into more details around you know let's let's demystify the concept let's let's look at what we need to do to really start a pilot with productive intelligence um let's get some insights about you know what's what's a good governance process for really making use of predictive intelligence within our within our enterprise and then the now learning and so this is a really really nice course that you can take predictive intelligence fundamentals and implementation also we have this one out here for orlando that can be taken free course right you can enroll take it about three hours gives you a lot of information on the ml and predictive intelligence solutions explaining some of the concepts that we went through today in a little more detail and gives you examples of how that servicenow instance is trained and so a lot of good information as part of that course that can be signed up for within the now learning site uh yeah and then you know if you want to get hands-on you can test it out yourself so getting one of those pdis right a personal development developer instance or just trying this out in a sub production instance right a subprime non-fraud instance that you might have within your organization that you're authorized to just go in and tweak and play around with you can go ahead and do this right the professional uh the personal developer instance is something you can sign up for and queue up for free um and so that's directly on the servicenow developer site once you have that you can go ahead and just leverage our documentation and some of that other information that's there both within the learning course and on our doc site to help just start taking your hand at it and building things out so some of the use cases and again this just gives you uh some of the benefits that we're seeing here um reduction in resolution times with sort of some of the capabilities here with agent workspace the cost savings right that's a big one right how can we really become more efficient how do we reduce our overall operational and resource costs right this is the name of the game right we're trying to become more efficient trying to improve customer satisfaction and we want to save money as part of that process right get some of those wins when win wins right and then 77 you're seeing incidents resolved on first assignment with predictive intelligence so we're not having to cue these incidents up through multiple groups multiple organizations multiple departments we're not having to reach out to to different optives and personnel we're able to figure out those incidents right away really cut that time in half so with all that said i guess we can open things up last i checked in the chat we didn't have any questions but uh but we can go ahead and look people have questions i guess they can ask it in the chat or um they can alternatively come off mute as well and ask any questions that they have about this topic so i i don't see any questions coming down okay perhaps there are no questions um but you can you can always follow up with us um and you will be getting all this follow-up email collateral feel free to reach out as a response to that email um and you know you can contact me contact david you know we'll just we'll just get it queued through to us um and you know any activities any information that you need just just let us know here yeah if i could add one more thing sure so everyone on the call if if you do have any questions as well you do have mobian and eyes names but as well i'm going to put in the chat in email that is an email where if you send a technical question in there the demo center team will try to answer the questions as quickly and thoroughly as possible so aside from me and mobine as a resource please feel free to fire away any questions into that email i put in the chat and you should have answers as thorough as possible so hopefully that that additional resource will help as well awesome well i