Unleashing AI's Potential for Business Transformation

In episode 45 of "Syncing with ServiceNow," host Andy Whiteside delves into the transformative powers of AI in business with a distinguished panel including Mike Sabia and John Dahl. The discussion explores the three tiers of AI—Analytical, Generative, and Agentic—and their roles in enhancing business operations. The episode uncovers how businesses can harness AI to improve customer service, streamline operations, and foster employee productivity. The conversation also challenges listeners to rethink traditional business metrics in favor of a more holistic approach to AI integration. Whether you're looking to refine your AI strategy or explore its practical applications, this episode provides essential insights into making AI work for you in the real world.

Transcript

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Andy Whiteside: Hello, everyone! Welcome to Episode 45 of syncing with Servicenow. I'm your host, Andy Whiteside. I've got a good panelist of guest here. I've got Mike Sabia, who's always with us, Mike. How's it going.

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Mike Sabia: Doing well. Thanks, Andy.

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Andy Whiteside: I think I ask you every time. But I'm gonna do it this time. What's the what's the most interesting customer conversation you've had in the last 2 weeks.

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Mike Sabia: A lot of them is.

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Mike Sabia: you know, our, you know, like, in this case, we have a customer who is moving from another managed service provider, and they have questions about how we can best help them, because they were not satisfied with their old partner.

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Mike Sabia: I'd say that's the most intriguing discussions I had.

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Andy Whiteside: And that's the conversation I have all the time, like all the time, whether it's a managed service provider or whether it's a consulting firm they're working with.

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Andy Whiteside: I made a comment to somebody at the Hims Conference last week that 9 out of 10 service now, customers aren't really happy with their current scenario. It's not like they got the bad code. The code's great. It's just the implementation or the sport of it's not good. Maybe they didn't implement it the way they thought they would, or maybe they haven't achieved the goals they set out to do set out to achieve very, very, very common. And it's sad. It's sad that your answer is what it is because we want it to be something fun exciting. We want to be AI, how using AI to solve problems.

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John Dahl: You're just talking about unhappy customers. That's that's just reality.

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Mike Sabia: And you know, today, we're gonna be talking about AI, which is, hey, where can we grow? But yeah, a lot of times. It's a fundamental hey? Are we doing it right? Are we being responsive? Which their partner was not.

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Andy Whiteside: So, Mike Steve is our solutions architect here at integra. John Dahl is with us replacing Fred, who's off gallivanting somewhere around the world doing something fun, I hope. John, how's it going.

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John Dahl: It's going very well, thanks. It's good to be back. I was on a couple of these

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John Dahl: year or so ago, and so it's good to be back.

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Andy Whiteside: So, John, you don't have the best audio quality. Maybe the background noise and stuff that kind of picks up. So just to just try to avoid any like.

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John Dahl: Extra noises.

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Andy Whiteside: Same question to you. What is

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Andy Whiteside: What is? What's the most interesting thing you've talked to a customer about in the last week or 2.

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John Dahl: Well, right now, there's a lot of talk about knowledge coming up in a couple of months. So trying to figure out what kind of conversations they should be ready to have. What kind of what expectations they should have as they go into an opportunity to see a bunch of different technologies, and they know that it's AI focused service now, has been talking about AI for the last couple of years almost constantly so.

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Andy Whiteside: Yeah, yeah. Come to knowledge prepared to talk about any and everything, including how unhappy you are with the current implementation

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Andy Whiteside: or how you can't can't understand, or don't know what to do with AI or something somewhere in the middle of all that, or I don't know some something along. The come prepared to engage is what I would say.

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Andy Whiteside: So, guys, you brought forward a blog this week called achieve exponential outcomes with 3 vital levels of AI. 1st question to you guys, is it a capital, a capital I, or is it capital? A lowercase? I.

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Mike Sabia: A capla. In my opinion.

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John Dahl: Capital.

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Andy Whiteside: Okay? So when I see it, uppercase a lowercase, I why do I feel like an idiot? Because like is this way, or is it that way? Which one is it.

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Mike Sabia: I don't normally experience that. So I'm surprised that you have.

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John Dahl: I.

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Andy Whiteside: Yes.

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John Dahl: Yeah, I I think when you see that somebody blindly accepted whatever auto correct told them, it should be.

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Andy Whiteside: Okay, maybe so. I think maybe the cool kids like little K. Little I, we'll see what wins out. This this article is from January 23rd of this year by Chris Beattie is Beatty. The way you say that BEDI beat.

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Mike Sabia: I would think so.

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Andy Whiteside: Okay. Awesome.

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Mike Sabia: Or Betty.

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Andy Whiteside: Betty. Well, whatever it is, Chris, thanks for writing it. We're going to talk through it now. I'll read the 1st sentence here because I think it's kind of enlightening to what we're going to talk about. AI. Oh, wait a minute, Mike. When you jumped on you made some reference to John not being the biggest fan of AI, while maybe you are a huge believer in it. Did I get that.

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Mike Sabia: I would say that, and John will probably agree with me that AI is something you need to be considerate about. What are you trying to accomplish. You can't just put AI. Too many people do say, Hey, we're doing AI, this, we're doing. AI, that. And there are different types of AI, which we'll talk about today, such as you know.

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Mike Sabia: traditional AI to do ticket routing. Then there's generative AI where we're trying to like, ask questions and then have that response generated. And then there's some of that genic stuff which which results from it.

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Mike Sabia: You know

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Mike Sabia: the the 1st one, the predictive intelligence, the routing. That's pretty clear cut and dry. It can be very useful. But for the generative AI. There are, you know, cost implications. There are, you know, actions that even Servicenow charges you, for they give you a whole bunch of them. But each query or act of that against Servicenow is an action, and and those could add up rather than simply, you know.

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Mike Sabia: spending a punch of money. You want to make sure that what you're doing is useful, and some of the stuff is really cool. The question is, is it worth the investment.

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Andy Whiteside: So, John, did Mike understate or overstate your thoughts?

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John Dahl: Well, I I tend to be cautious about it. So AI has been a buzzword for a couple of years, and you know, going into this. I took the position that everybody's gonna try to use it everywhere.

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John Dahl: And it's gonna explode into every every possible

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John Dahl: use case. And then over time

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John Dahl: organizations are gonna start to figure out where it really truly adds value, and where it's just Fluff. And I, I think we're starting to get into an area where companies are seeing

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John Dahl: where it's adding value and where it's just extra cost. But as we continue to push for more and more advanced

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John Dahl: capabilities.

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John Dahl: there still needs to be a good amount of oversight. This article actually highlights that or it mentions it, there has to be oversight. You have to be able to recognize when AI is wrong, and there are times where AI is wrong and it has cost companies lots of money.

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Andy Whiteside: Yeah.

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Andy Whiteside: sometimes I like to think of this way. It's it's it's going to be wrong most of the time, but right enough to add value

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Andy Whiteside: like no, I say that at the same time, the other day my my wife wanted to look at a house, and we're trying to find who the listing agent is.

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Andy Whiteside: She looked for a day like she took all day trying to find it. It took me 30 seconds with AI, and not only did I find a listing agent. It told me that it was. It was the listing agent's house that she was trying to sell unbelievably spot on, and powerful.

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John Dahl: Yes, and and I think that probably tends to be more of that predictive intelligence that we already have in place. Right? It's it's it. It definitely has value. Right? I.

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Andy Whiteside: No.

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John Dahl: Would, I would never say that there's no value involved with it. I just am cautious about

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John Dahl: putting it in areas of sensitivity and making too many business decisions without validating, because it's learning off of our own old mistakes. Right? So it's not like it has true intelligence to make wise decisions out of the ether. It's looking at what mistakes we've already made, and it's learning from that, and in some cases repeating it.

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Mike Sabia: Right, and and a good example of some of the mistakes that can happen is putting it directly in front of customers. There's, you know a case in the news where somebody was making a air flight reservation, and they acted, you know, made certain decisions. And it turned out that that wasn't company policy, and there was a big hoopla whether the airline was gonna honor that that statement. By AI. But if you are utilizing it for yourself, and then you review it.

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Mike Sabia: such as you, with your your real estate thing. You got the answer. Then you're saying, Hey, does this make sense? That's great use. Case.

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John Dahl: Yes.

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Andy Whiteside: Yeah, in that case, I told my wife, call this person and find out. That's true. And it was 100% true, which was awesome.

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Andy Whiteside: Alright. The the article starts off. I'm reading the 1st line, says AI is not a strategy. It's not a new channel product or goal. It's certainly not better Chatbots meeting summaries or email draft creations. AI.

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Mike Sabia: But yes.

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Andy Whiteside: AI is the most powerful enabling technology to emerge in the past century, and then we'll go on to talk about it. But it's something we can't ignore. We have to realize it's real. It is very, very, very real. But the case, the challenge is what to do with it, what to expect out of it. And we're going to break that down here with these 3 levels. There's a graph here

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Andy Whiteside: it goes from left to right and goes up, goes higher as it goes from left to right. Analytical AI, which we'll talk about generative AI, which I've been talking about a lot, but I can't wait to hear what you guys say about it. And then Agentic AI, which I've heard those words.

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Andy Whiteside: I heard that word in the acronym together, but I really don't know what it is. I'm excited to hear what you guys are going to say. So the real 1st of the 3rd that we're going to talk through here, or the 1st section talks about unleashing the power of AI Mike, kind of help us lead into this, and then help between you and John break down the 3 that are listed.

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Mike Sabia: So the analytical AI focusing on that is the AI that's existed for a number of years, even before generative AI. It's like, Hey, based on the analysis of 30,000 records based on these keywords. Which assignment group do I recommend? Or you know, there's another type of analytical AI which can be powerful. But you have to be very cautious. To deliver in front of customers is, you know, on this type of ticket. What's the average response time.

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Mike Sabia: That sounds great. It's useful internally. But I would be very cautious about putting that from the customer, because they don't want to hear that on average. Their ticket takes 3 days resolved. So that type of and a little AI traditional AI. Looking at the data, you know, you type a key, you know some search in the in the

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Mike Sabia: search box, and it comes out with some relative articles like that's traditional AI, and curious about.

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Andy Whiteside: The analytical AI has been around for about how long.

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Mike Sabia: Oh, I don't know a number of years.

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Mike Sabia: probably 4 or 5. I'm not exactly sure.

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Andy Whiteside: John, your your thoughts on how long it's been around.

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John Dahl: Would push it closer to 8. It's

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John Dahl: servicenow has actually had predictive intelligence and natural language assessing or analysis for a long time.

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Andy Whiteside: Yeah, is that sorry for my ignorance that this sounds like a silly example. But is that like me typing on my phone and it, predicting the words, I'm gonna say, next.

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John Dahl: It on steroids. Yes, but it's more like saying, when customers have submitted a request for

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John Dahl: a new network card.

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John Dahl: 80% of the time it was assigned to this team and resolved by this team. So we're gonna suggest that it go to that team.

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Andy Whiteside: Okay. Okay.

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John Dahl: So it's it's just a yeah, it's it's just a pretty simple analytical assessment. It says, what is the what is the highest percentage or probability that it should go here

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John Dahl: right or or where should it go? Based on.

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Andy Whiteside: So, John, the game changer for me was.

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Andy Whiteside: I guess, maybe this conversation, this concept, this type of conversation. And then, when somebody started using the throwing the word generative AI in front of it. And that's when I don't know. 2 years ago it started to make sense to me that this was something that was necessary and was going to be valuable. Can you help us understand generative AI in this context.

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John Dahl: Yeah. So it still uses a similar approach to looking at what the prompt is. What is it that they're looking for? What information are they giving me? And it's kind of assessing based on the training data where it should go. But where generative AI steps up is, it has the ability to not just select from

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John Dahl: going back to the previous example a selected group or list of groups to assign a ticket to it actually can compile a friendly response, using normal language. So if you ask about hey, I want to go on a vacation to West Texas next week, it can look through all the information that's available about holidays in West Texas, and it can present you with

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John Dahl: a paragraph, not just a a list of bullets or a list of Urls, but it can.

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Andy Whiteside: Craft, wording.

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John Dahl: To try to inspire you, or guide you to where you want to go.

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Mike Sabia: And, as John said, based on their timeframe, is it this week versus next week? What's happening those weeks.

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Mike Sabia: and what

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Mike Sabia: what's your what's your destination. How many days you should go you have where the key spots. You should stop on your trip.

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Andy Whiteside: And and just to be clear, I'm not an idiot here when it comes to Texas geography. I don't think the analytical AI would recommend West Texas is this 1st one.

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John Dahl: We've had.

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Andy Whiteside: Making sure I wasn't clueless on that one beautiful place.

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John Dahl: That that was actually gonna be something that that I would kind of throw in there, where sometimes it'll come back and say, Are you really sure you want to go there.

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Andy Whiteside: Yeah.

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Andy Whiteside: well, that's that's interesting. So help me help me with the evolution here. So I've got this predictable what's going to happen

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Andy Whiteside: next? Concept of analytical AI. And then, instead of just kind of giving me guidance and predicting, it's actually going to take.

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Andy Whiteside: it's actually going to do something for me is when we start talking about generative AI, almost as if it does it completely for me, or, in the case of, you know, like Microsoft Copilot or something. It just takes my email and and rewrites it better

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Andy Whiteside: that that example of generative AI.

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John Dahl: Yeah, it. Yeah, it has the ability to craft regular human language responses. So it it can.

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John Dahl: Obviously it

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John Dahl: make decisions or suggest directions. But more more importantly, it can use specific style of wording. It can do basic different languages. It can even articulate your statement

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John Dahl: based on certain parameters. You want a higher education like a university level, or you want to give it to me like a 5 year old. It has the ability to adjust how it crafts those responses.

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Andy Whiteside: So Mike, using the most common, so far use of service now as a ticketing system or itsm, what would be the the best example you would give of generative AI in, let's say the I don't know. In in ticketing we'll start there.

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Mike Sabia: Well, the the most common one, which is probably the most useful is case summarization. So if you have, you know, customer going and a agent going back and forth on discussion, and somebody new comes in rather than having to read several pages of of Back and Forth. It can summarize the ticket. It'll speed up, it'll, you know, maybe even create a knowledge article based on what the discussion was and what the eventual solution was.

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Andy Whiteside: Yeah, John, I'll give you one, too, and I'm sorry to put you on the spot with this. Feel free to defer it. Asset management would be a great example where generative AI would be valuable. There.

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John Dahl: If if you wanted to.

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John Dahl: I don't know. Let's say you wanted to plan for asset replacement next month or next quarter. Right? Say, Hey, I need to know all the assets that are coming due, or they're coming to end of life need to be replaced in the next quarter, and I want to arrange some conversations with the asset managers for each of those geography areas. Or, however, we want to break that up, and it can say, Okay, well, you've got 100

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John Dahl: assets coming due. 50 of them are managed by this person, 40 by this person, and it can help you. Just

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John Dahl: basically tell you, hey? So schedule a meeting with with Joe for next week to talk about these 50 assets.

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Andy Whiteside: You know, I love this concept because, you know, I 1st started learning some of this AI stuff on the in the Microsoft co-pilot world, which is where I get to experience it the most. I'm sorry my examples keep going back to that. But you know somebody did a prompt or somebody mentioned something that I saw valuable, and then I turn around, did it, and then they did it, and I did it. We just learned from each other. Hey? I didn't. It's like, you know, trial and error. Oh, and it just kept getting it better and better every time we would do it. It became more enlightening. What was, you know, potential the possibility?

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Andy Whiteside: Mike, I'm going to come back to you on this 3rd one here, agentic. AI.

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Andy Whiteside: I have no idea what that is. Can you?

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Andy Whiteside: Can you put that 3rd grade language.

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Mike Sabia: So I would say, it's going beyond just General AI. Hey, here's you know, some.

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Mike Sabia: the text or summary it's hey? What are you gonna do with that? Are we gonna orchestrate, hey? If we see that there is this behavior, we're gonna go and orchestrate, we're gonna

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Mike Sabia: make you know, make another call to an external system to take other changes.

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Mike Sabia: So it can act, you know, some autonomously. Now, there are some, you know, topics here that are, you know, kind of cross that shady line between generative and and agent guys. AI. But if you were talking about like itom, if you're doing alert management, and you see that you know, the server is at

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Mike Sabia: 78% this capacity and usually have a flag at 80%. You might think, well, that that's something we need to be concerned with. But if it's been 78% for the last 6 months, but another one is going from 10 to 20 to 40 to 50 to 60, that other one's gonna have more more input. And with some of that generative AI, you can actually identify some of the up and coming alerts that were gonna happen before they do. And

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Mike Sabia: you know, to pull that into gentic. If you have some of that stuff, then you could actually maybe, you know, have it automatically, create a ticket or spin up more disk, drive, or, you know, bring in more vms like.

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Andy Whiteside: Okay, that's kind of where I was thinking generative was going. So what you're saying is a gentic is the idea that now it's got this data, it can do something. And it's going to do something specific to to, you know. Maybe take the next step and react.

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Mike Sabia: Right and service now, and and the community are still evolving that. And agentic AI means different things to different people. But I I would say, that's the next step into hey, Genera! Fantastic! Now, what.

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Andy Whiteside: Yeah, hey, John, forget that, Mike just answered that you answer the same question.

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Andy Whiteside: Agentic AI! What does it mean to you?

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John Dahl: Yeah. So I I hadn't looked into it much prior today. So I'm I'm kind of learning as I go here as well. But one of the things that struck me is

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John Dahl: today we can choose to get Gen. AI for

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John Dahl: development, or we can get it for the virtual agent for Hr. Or we can get it for itsm and ticket summarization, like Mike suggested. Where Agentic AI comes in is it gets those different agents to talk to one another, so that when you have that Hr onboarding activity happen, it can spur off

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John Dahl: other things automatically. It can already talk to the it agents and trigger the creation of an order for a laptop, because we know this person is going to be working remotely, whatever it is. So it's the idea that these agents are all coordinating together to optimize for the for the organization.

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Andy Whiteside: Yeah. And I'm going back to read the 1st line of this section agentic AI is capable of delivering exponential value. I think that's where this all really becomes very interesting is this, where my robot shows up.

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John Dahl: The one that cleans the house.

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Andy Whiteside: The one that does whatever it needs to be done without me even having to tell it needs to be done.

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Andy Whiteside: This this is it, and and this where the robot lives.

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John Dahl: Sees that spill on the floor, decides to clean it, and decides that, hey? It's been too many spots on the floor. I need to schedule that carpet cleaning instead.

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Andy Whiteside: Yeah.

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John Dahl: I need a. It just tells me I need a mid server in my house so that it can work with all of my robot vacuums.

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Andy Whiteside: All right. Next section talks about exponential AI outcomes require coordination makes total sense. But let's see what this what this means, John. We'll go to you first.st On this one. Take a platform, approach.

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John Dahl: So this one got into what they're calling AI control tower. And I didn't get a chance to look at it in detail. But it's it's really just

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John Dahl: looking at it across the entire platform. All of service now. Servicenow's capabilities have been growing since the day it was released, and they're taking this by storm. They're they're saying we have access to all these different capabilities in your enterprise. Let's make sure AI can help you add value everywhere.

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Andy Whiteside: Okay, Mike, you want to take a shot at this one, too.

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Mike Sabia: No, I I think that that what John said is pretty accurate. You're looking at more than just this particular generator you use.

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Mike Sabia: if we have seen this behavior, maybe we then trigger this other thing that could have generated that. And and they all work together. Know, hey, what's the health of our system? These things are increasing, those things decreasing. How's this? Gonna maybe increase the number of help desk agents? We need things like that.

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Andy Whiteside: So is so as a

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Andy Whiteside: I'm going to call myself a newbie to the service now Platform. But I've seen this coming for a while, and and watching this act as a platform. This is where being a platform really shows a lot of value beyond disparate applications and things that aren't going to be able to bring it all together.

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John Dahl: Absolutely.

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Andy Whiteside: And you think Servicenow knew this day was coming

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Andy Whiteside: 20 years ago, when this all started, or do you think this, or maybe whenever it started, however, many years back.

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Mike Sabia: I don't know about 20 years ago, but they were certainly ahead of the the curve with generative AI before it really became the bus. They were thinking about their own Llm. And so forth. Before that.

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Andy Whiteside: Well, I think what I'm really asking is it? Was it really the platform concept that was leading to this? Or is this just the benefit of having a platform.

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John Dahl: Nope, this, this was a unique take that service now had Fred Luddy, David Lou, the whole group. They started day one saying, this is not an itsm solution. This is a platform for the enterprise. Itsm is just going to be the 1st application we're going to deliver with it.

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Andy Whiteside: Yeah.

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John Dahl: So it it was really defined around that core functionality, your Cmdb, your reporting all your intakes and outtakes outputs the security model. It was all designed for the beginning to say.

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John Dahl: this is the starting point. This is going to be able to help you with your entire enterprise.

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Andy Whiteside: Yeah.

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Andy Whiteside: what? What I what I love about that conversation is the opportunity that it creates for us is, you know, we're we're an it reseller managed service provider, and and it is where we start with service now, just like service now started with itself. That's where we have an opportunity to come in and help if you can't get the it part right. Chances of getting the rest of this done correctly is almost none which creates a massive opportunity for us to come in and be a better partner for the the clients that are going down the It route.

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Andy Whiteside: Mike, I'll start with you on number 2. Here, choose one or 2 enterprise wide metrics to start.

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Mike Sabia: So

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Mike Sabia: you can't start everywhere. And you need to identify. Hey, what's what's what are we trying to accomplish? What are we trying to measure are we trying to measure

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Mike Sabia: customer agent response time? How many tickets they're able to serve in a certain day? Are you looking at revenue? Are you looking at this or that, and and you need to speed that up. Are you trying to have more deflection? So you don't have tickets to go to the agent. Do you want to speed up the amount of time a person works on the agent? Do you want to speed up ordering of new product into your warehouse, so that you have the correct amount.

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Mike Sabia: You need to focus on a couple things first.st

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Andy Whiteside: That's just good advice

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Andy Whiteside: all the time. But with a platform as extensive as this is, and with as many opportunities there are to to solve problems without creating additional problems for yourself. It just makes common sense.

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John Dahl: John, your take on this the concept of limiting yourself to a couple key metrics to start.

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John Dahl: Well, I think part of it is when you start talking about enterprise, wide capabilities, each of your your departments or your divisions, or however, you want to break up your organization into silos, they're all gonna have different measures of success.

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John Dahl: And so when we talk about an enterprise wide tool like this, we need to make sure we're focusing on measuring value and success in a way that applies across all of them.

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John Dahl: If we focus too much on each department's own measurements.

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John Dahl: they're not going to play nicely together. They're not going to combine together to give us one holistic view of the success of our work.

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Andy Whiteside: We won't get the benefit of the platform. If if we if we try to do the whole thing at once, we're gonna fail and never get the plat the benefit of

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Andy Whiteside: walking through the pieces of the platform that apply here.

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Andy Whiteside: Okay. Number 3. Prioritize a unique blend of business acumen.

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John Dahl: And bullets.

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Andy Whiteside: Guy thinking, don you want to tackle that one.

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John Dahl: Well, obviously, if it

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John Dahl: you kind of touched on this earlier with the value of it. Most organizations have traditionally looked at it as a cost center. We are only here to spend the money that everybody else makes in the company

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John Dahl: it. It's always been a challenge for it to make the case and help business understand that we're here to make sure we can protect and manage the information you require to do your business.

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John Dahl: This kind of adds to that.

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John Dahl: or at least it supports that approach. When you talk about your it, people being able to understand what the business is doing.

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John Dahl: the idea that it is just the people sitting in the basement with propeller hats on. That doesn't play well. We need to be a part of the business organization. We need to be business partners. We need to be in those conversations to help understand where the business is trying to go so that we can help them make sure the data tells them what they need to tell them.

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John Dahl: It really is about being a business partner, not just an expense center.

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Andy Whiteside: Yeah, Mike, your comments.

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Mike Sabia: I mean everything, John said, but I would say that if you want to think forward

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Mike Sabia: about hey, how can I improve the business. You can ask generative AI, hey! What can I do to improve the business and give you good ideas? The step to say I want to improve it is important.

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Mike Sabia: The leveraging the tools to identify them is important. But yes, you need to, you know, not as carte blanche. The answers as that's what I'm going to do. But but you know, take it, ask it, evaluate it. See what's possible. There's been a lot of stuff out there saying, Hey, is AI going to take away our jobs? Well, maybe not. But you need to know how to use AI, or you probably will lose that job.

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Andy Whiteside: Yeah. Is this where my thought on you know AI in all aspects should not do the job for you. But it should be a great coach to help you do the job better.

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Mike Sabia: Yeah, mostly, yeah.

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Andy Whiteside: But but then there will be menial things, and and maybe things that go way beyond menial. I assume way beyond medial at some point that it can just do for you. It can predict it. It can see it coming. It can take care of it when it happens. If something goes awry. It can make the adjustments needed on the fly.

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Mike Sabia: Right? I mean, you know, in most cases you want to have AI, and whatever form focus on the the key issues or the most common ones, but as we grow it'll be able to handle those exceptions a lot better able to say, Hey, I don't. I haven't seen this exactly this, but maybe I can do my own query of General AI to find out what an answer was, and then that directed, or or initiated from there.

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John Dahl: Hello! Anybody!

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Andy Whiteside: So there's another picture here. It says a quote to realize the full power of AI. Think of it as a spectrum from incremental outcomes through exponential results, although it's a spectrum

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Andy Whiteside: along the spectrum.

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John Dahl: She's off in the house.

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Andy Whiteside: John muted you real quick, all right, making autonomous AI real encounter, potential transformation, using multi agent, autonomous AI every day speaking with clients. How can my customers resolve their issues more quickly? How can my sales team serve their customers better? How can my employees be more productive? It starts with, how can my customers resolve their issues quickly, Mike, you want to jump on that one.

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Mike Sabia: Well, it kind of comes back to that that some of the Kpi question we had before. It's like, Hey, when do you want to accomplish, and one of the ones I I said, is, Hey, I want to be able to have our customers serve themselves in order to have deflection.

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Mike Sabia: and if we want to help our customers solve their issues more quickly, deflection is a great way to do that. To be able to say, Hey, these are the common situations. Here's a knowledge article. Here's a Kb, here's maybe something else that I search for. That might be able to find your answer. We integra is not just a service now, shop or an Msp. If we can, you know, provide them answers in order to answer those questions that would be helpful, though, of course, we don't want to get rid of our our businesses either.

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Mike Sabia: So these are just more examples of what your use case is. What's that Kpi, you want to measure.

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Andy Whiteside: And, John, I'm gonna ask you to unmute so we can get your answer to this one. But, you know this, this next one, you know. How can my sales team serve their customers? Better, be more productive, sell more? I haven't really thought about AI as a

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Andy Whiteside: sell enablement, but it's really popped up on my radar several times.

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John Dahl: Yeah.

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John Dahl: Yeah. So there was a meme that went around a little bit ago

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John Dahl: was talking about how AI has become so good at doing graphics and how creative it is, and

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John Dahl: capture or kept the line at the bottom, was saying, well, I don't want

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John Dahl: AI to do all of the creative stuff, so that I'm free to do my laundry and clean my house. I want AI to clean my house, so I'm free to do all my enjoyable creative stuff.

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Andy Whiteside: Right.

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John Dahl: And what this is talking about is.

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John Dahl: let's make sure the AI can handle all of the nuanced stuff behind the scenes. How can we help the customers, you know, deflect the tickets, and that so that I can focus my time on managing my relationships with my customers and with my employees. How can I have that relationship? Be more about the positives and not worry too much about the negatives, because those are already taken care of.

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Andy Whiteside: Mike, this last one. How can I help my employee? This is really the one that's probably most applicable to everybody who listens, and everybody who's thinking through this, how can I help my employees be more productive and satisfied with their jobs, which I think goes back to what John was just talking about a minute ago. They want to do the fun part of the job, but not the worst part of the the day to day, and or their job.

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Mike Sabia: A slight spin on this. So when my very 1st job I wound up leaving because they outsourced me to another company, but in seat and the tools they had were a pain to work with. And I was like, I'm not enjoying myself because I'm having to struggle with these tools, and if we can make our employees more effective to say, Hey, I'm actually able to accomplish this rather than doing routine menial things. That doesn't seem to have much utility or helping me grow.

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Mike Sabia: Then I'm going to be a happier person, and if we can give these people the the, You know ticket summarization so they can quickly look at it and understand it. They can act quickly and and focus on the real stuff rather than having to like. Spend 20 min, you know, delving through back and forth between 2 people that will make them happier and therefore more attractive and more likely to stick around.

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Andy Whiteside: Yeah, so, Mike, that that seems to address the incremental. What's what's the exponential future of that.

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Mike Sabia: Well, I'd say that you know, rather than having the employee know, that they have to do work on this, just as that 1st sentence, says, you know, provide the employee with a series of tasks to initiate. Hey? I've identified these issues. Why don't you then take a look at them in order to improve them.

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Mike Sabia: How can we we not just improve my day to day moment? But how can I

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Mike Sabia: improve what I need to work on. So I can be more effective.

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Andy Whiteside: Yeah, so it kind of eliminates.

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Andy Whiteside: eliminate the mundane help with the mundane, and maybe even get to the point where the mundane is solved. And now we all move forward with.

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Andy Whiteside: you know, more advanced things to work on.

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Andy Whiteside: John. Any thoughts on this, the employee, productivity, overall thing.

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John Dahl: The the one piece that keeps coming back to me is, you know, this is what Mike talked about. If I am jumping in to help somebody out on a ticket that's already been out for a while. I don't want to have to read through everything. Just give me the summary of what it is, and and help me understand where I need to be, for right now.

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Andy Whiteside: Right.

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Andy Whiteside: Yep.

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Andy Whiteside: all right. The next section talks about how to get started. This is the last section. Don't get derailed by measurement. Focus on value. John, you want to cover that.

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John Dahl: You know, this seems to go against everything that they talk about in business for for decades. Right? It's don't worry so much about your return on investment. Don't worry about measuring every penny of cost and value. It really is talking about taking a step back and just seeing

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John Dahl: from a

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John Dahl: a softer value if you will. That is, is this really adding value to organization? Is this really helping our customers to be happier with our service? Our employees to be happier with their jobs. It seems to to just kind of flip that old Roi conversation on its head.

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Andy Whiteside: Mike, your thoughts on value being the thing to focus on.

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Mike Sabia: You know, metrics are important.

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Mike Sabia: Know what your you know, number of tickets you're solving, and so forth. But you need to see.

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Mike Sabia: you know ultimately, does this keep people around like? Yes, there is now generative AI where it can write some of the code if it speeds up that code. Yeah, that sounds good.

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Mike Sabia: But is it worth the time which kind of goes against the Roi? So again, it seems a little odd to be saying that to ignore Roi completely, but to at least consider where it can help

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Mike Sabia: prior to talking about just, or I think about what it can do rather than starting with Roi, just to to only be narrow, focused on on

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Mike Sabia: some specifics.

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Mike Sabia: you know. See what it could do, how it could help rather than just be so narrowly focused on on one item.

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John Dahl: One of the old.

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John Dahl: So one of the old adages of of metrics and kpis is whatever you define as your kpi. That's the only thing that's gonna matter right? If we only care about how quickly can we get these tickets closed?

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John Dahl: That's all your agents are gonna focus on is how quickly can I get these cases closed? That's not gonna play into how how happy our customers are with our service!

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Andy Whiteside: Right

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Andy Whiteside: next session talks about. Know your know your AI. I think for me this is something I talk about a lot. I mean not being the expert in this area. But knowing there's a lot of AI coming from all the directions. John, what are you seeing out there? As far as people

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Andy Whiteside: being bombarded by AI conversations that they're not prepared for.

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John Dahl: Actually, at this point, I'm still mostly seeing people talking about Chat Gpt, and I see a lot of comments where people are

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John Dahl: kind of mocking this idea of a prompt engineer because it is still very difficult. Or, like you said earlier, it takes multiple attempts to get a reasonable response. And for some people jumping in and asking it to summarize something or write a paper form, or whatever it is that they're asking for.

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John Dahl: If they don't take the time to review the results, and they don't recognize the mistakes they're gonna go with the 1st

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John Dahl: 1st candidate. The 1st result they get. And it it can be important to recognize. No, that's not right. I need to go back and refine this.

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Andy Whiteside: Hey? You probably see me bring it back to copilot multiple times. That's what I experience every day.

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Andy Whiteside: I haven't experienced it that I know of outside of that, but I probably I probably am. I just don't know it. Mike, your thoughts on the AI. That again know your AI.

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John Dahl: That's it.

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Andy Whiteside: What does that mean to the average person.

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John Dahl: No.

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Mike Sabia: I think it kind of comes back to the value you know a lot of people use. AI say, AI, generate this A to AI generate. That. Is it really true? Is it? Are they just throwing the buzzword around? Are they trying to make a business out of something that really

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Mike Sabia: is somebody trying to get a buck?

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Mike Sabia: You know. Ultimately you need to have the practical experience with it to say, Hey, is it useful?

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Mike Sabia: And to John's point of view. Not just accept the 1st answer, you know, if you're gonna try to, you know, make an announcement to your customers, hey? Let's use generative data to draft out a message. Then take a look at, say, yeah, this is fantastic, or this is almost fantastic. Let me just tweak that that stuff.

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Mike Sabia: Once you start using that, it's a great tool for you. I have a friend who's an accountant. They have obviously some restrictions about sending their customers data out there.

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John Dahl: But.

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Mike Sabia: They have the ability to say, Hey, go find me, you know, articles or policies or statues on on these 3 things that saves them a lot of time.

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Mike Sabia: you know.

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Mike Sabia: Know what is out there. Know how it can help you build from there.

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Andy Whiteside: So this last one here number 3 says.

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John Dahl: But.

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Andy Whiteside: Thinking, what's my AI strategy? That's interesting for me, because that's the number one way I start every conversation when I'm trying to get you guys or Brett on our other, on our AI team involved challenging people, whether they even have a strategy or not. I think it's a very pointed way to get conversations started. But according to this, John, why is it saying, Don't start? Don't! Don't focus on what's my AI strategy. Why do they say that.

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John Dahl: I think part of it is to avoid falling into analysis. Paralysis don't spend so much time trying to figure out what it can and should do for you that you never get started. Just go get into it, see what it does for your business, and adjust from there. Get into it now and and adjust. And more specifically, it's it's

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John Dahl: about looking for how you can adjust your organization

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John Dahl: to get more value from the AI.

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Andy Whiteside: Mike, your thoughts on the stop, you know. Stop thinking about just what's the strategy comment.

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Mike Sabia: I think that's a great point. I mean.

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Mike Sabia: I agree with John a lot. But like, if I was a some sea level, saying, hey? What's my AI strategy?

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Mike Sabia: I don't know what that would mean. It's like, hey? Yes, we want to transform the business around something. Or do we want to say, hey, how can we improve the business leveraging what's out there

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Mike Sabia: it might be too big a thing to chew on. Let's let's take it. Let's take advantage of it

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Mike Sabia: before we necessarily define the whole business route. I mean to find the whole business around. It can be good. But but let's not start and stop there.

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Andy Whiteside: You know.

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Andy Whiteside: So, guys, thanks for covering that with me, I'm go back to the title here.

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Andy Whiteside: How does this concept that we've covered here today result back into the title of exponential outcomes.

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Andy Whiteside: Can you kind of translate what we've covered back into the you know, the the title of exponential outcomes.

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Mike Sabia: I I think, just as we said that this is a transformative technology. I mean, I don't know that it's more transformative than the Internet at all. But perhaps so. That you know, you need to. Just as with with the Internet came out, people were, you know, doing things very brick and mortar, and and there was a lot of potential to do things online. And you know.

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Mike Sabia: put your business online. These are the times to talk about, hey? How are we going to take advantage of our business at knowledge? 24 last year they had one of their presentations was kind of interesting. They had, like a donut shop where they were, you know, using AI to identify. You know how many customers are coming in at different times, based on school releases, and

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Mike Sabia: you know whether it's a full moon or the like, and then order the right amount and let the the people focus on making those donuts. It's pretty fantastic you need to identify and say, Hey, how could this help help our business? How could we transform? How can we go from, you know, some of the predictive stuff to, you know, using Chat Gpt to going into some of that

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Mike Sabia: agent specific capabilities. It's it's fantastic. It's gonna

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Mike Sabia: exponentially improve the business world. It's just a matter of, you know, being on top of it. So you don't fall behind.

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Andy Whiteside: Yeah. So, John, same question you, what?

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Andy Whiteside: What's what's the exponential piece of following the the outline we've gone through in this blog.

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John Dahl: I I think it has a lot to do with not limiting

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John Dahl: your expectations on what it could deliver. Right? Don't don't think of it as just summarizing incidents or just doing one or 2 things that have been a part of the marketing to date. It's reached the point now where we're ready to start having those different pieces talk to one another, and to allow the synergies of the different capabilities of the platform to just add more and more value to your organization.

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Andy Whiteside: Yeah, is it fair to say that if you approach this wrong or you approach it closed? Minded. Ish that the you're gonna have limited outcomes versus, you know, going basically understanding what's in this blog and and really thinking beyond what's positioned for you and and thinking more wide open about what can be.

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John Dahl: I think so. I think it's even more than that where it's don't even worry about what you think it can do.

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John Dahl: Get into it. Start using it, and let it show you what it can do.

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Andy Whiteside: Yeah.

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Andy Whiteside: And and is it fair to think of whatever your challenges or or concerns are that be open-minded to

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Andy Whiteside: what AI, with help from someone like us, potentially on a platform like Servicenow could solve.

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Andy Whiteside: and assuming that there's nothing it couldn't solve with the right approach.

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John Dahl: It's still learning from our own mistakes, our own past right? So there's still gonna be a certain amount where we have to be involved in that. We have to provide that oversight, Mike said

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John Dahl: earlier that we need to make sure that we're using it for our internal

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John Dahl: work. It's it's I don't believe that it is rated, or that it's appropriate to to make it customer facing at this point.

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John Dahl: But it's it's definitely something that

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John Dahl: yeah, I mean, it's here, there. If we can't get on board with what it's capable of doing, our competition will.

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Andy Whiteside: Yeah, there's only one thing certain about the evolution is, it will evolve.

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John Dahl: Yep.

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Andy Whiteside: Yeah. Alright guys, Mike John, thanks for joining today. I appreciate you having this conversation with me.

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Mike Sabia: Thank you.

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John Dahl: Thank you.

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John Dahl: Talk to you later.