Insights View Recording: Accelerating Financial Services with AI

View Recording: Accelerating Financial Services with AI

Join us for an enlightening workshop on “Accelerating Financial Services with AI.” Explore how artificial intelligence is reshaping the financial industry and driving efficiency, innovation, and customer satisfaction. Delve into practical strategies and real-world case studies that showcase the transformative impact of AI in areas such as risk management, fraud detection, customer experience enhancement, and personalized financial services. Discover how AI-driven insights and automation can streamline operations, mitigate risks, and unlock new revenue streams for financial institutions. Whether you’re a seasoned finance professional or new to the world of AI in financial services, this webinar will provide valuable insights and actionable takeaways to propel your organization forward in the age of digital finance. Don’t miss this opportunity to stay ahead of the curve and harness the full potential of AI in financial services.

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0:0:0.0 –> 0:0:1.230 Nathan Lasnoski OK, welcome everybody. 0:0:1.240 –> 0:0:5.270 Nathan Lasnoski We are going to have a great conversation today about AI in financial services. 0:0:5.680 –> 0:0:7.130 Nathan Lasnoski I am nanosky. 0:0:7.140 –> 0:0:11.290 Nathan Lasnoski I’m concurrencies chief technology officer and with me as well is Brian Hayden. 0:0:11.300 –> 0:0:13.90 Nathan Lasnoski Brian, once you introduce yourself as well. 0:0:13.740 –> 0:0:15.330 Brian Haydin Hey there, Brian Hayden. 0:0:15.340 –> 0:0:18.910 Brian Haydin I’m a solution architect that concurrency love working in the space. 0:0:19.320 –> 0:0:25.540 Brian Haydin A lot of lot of experience working with a lot of financial industries, so looking forward to the conversation. 0:0:26.630 –> 0:0:28.660 Nathan Lasnoski Awesome and welcome to everyone who’s joining us. 0:0:28.970 –> 0:0:32.340 Nathan Lasnoski So today we’re gonna talk about AI and financial services. 0:0:32.570 –> 0:0:41.940 Nathan Lasnoski That is a broad group of companies, so we are mainly targeting this conversation around capital markets, banking and little bit of insurance. 0:0:42.0 –> 0:0:57.900 Nathan Lasnoski We broken out some of those use cases between both of those sets, so looking forward to covering both a framing of the conversations today as well as examples of what companies are doing in your sector that are your peers and help you to gain some perspective so you can plan for your organization. 0:0:59.740 –> 0:1:13.750 Nathan Lasnoski So as we think about the impact that AI has in this particular domain, you can see various points in our history where major changes within technology have influenced the way that we’ve engaged in our businesses. 0:1:13.890 –> 0:1:25.710 Nathan Lasnoski So I’m going to happen in manufacturing, so it was transportation and particularly in financial services, it was the advent of PCs, the Internet, smartphones, the ability for us to engage directly with customers and digital ways. 0:1:25.900 –> 0:1:36.550 Nathan Lasnoski But also, as we all live through those transitions, it was the transition of skills that happened in each of those spots that were both positive and negative impacts on every person. 0:1:36.740 –> 0:1:44.950 Nathan Lasnoski And the thing that’s going to be very important for the financial community is most of your people are information workers in some context. 0:1:45.20 –> 0:1:57.540 Nathan Lasnoski So you are very right for leveraging technology to be able to advance the capabilities of every one of your employees to be able to do more within any given day, both within what we’ll call the commodity space and the mission driven space. 0:1:57.550 –> 0:2:9.160 Nathan Lasnoski But it’s going to come with a transformation of skills that they need to become capable of, where maybe relearn that they had before, but they haven’t used very often and we need to help them to be able to engage in that capability. 0:2:9.310 –> 0:2:11.180 Nathan Lasnoski So we’re going to talk a lot about that today. 0:2:11.840 –> 0:2:15.850 Nathan Lasnoski So you think about the capabilities of AI. 0:2:16.160 –> 0:2:21.110 Nathan Lasnoski You’re gonna see that more and more of this is moving toward where we say this future state. 0:2:21.120 –> 0:2:33.350 Nathan Lasnoski We’ve called up what we can do now and what’s in the future state now as you start on the left hand side here, you can see many companies have any people been doing this for some time predictive activities against large datasets. 0:2:33.460 –> 0:2:48.550 Nathan Lasnoski Financial Services was one of the first movers in this particular domain, using customer data to be able to analyze where markets are going or how I can optimize the client portfolio and giving prescriptive guidance back to our customers to help them do better. 0:2:48.880 –> 0:3:6.170 Nathan Lasnoski The best companies started to move into storytelling, helping to not only think about what could happen, what’s going to happen, and maybe what possibilities exist for your portfolio or your customers based upon potential outcomes that could happen within the family or choices that you can make. 0:3:6.180 –> 0:3:11.270 Nathan Lasnoski Or if I went on this particular vacation or bought this house, how would that impact My Portfolio? 0:3:11.840 –> 0:3:13.670 Nathan Lasnoski And then you can see the starts to move. 0:3:13.680 –> 0:3:34.120 Nathan Lasnoski Now this lead a segment where we explore and we got this advent of chat, GPT is this general activities in general activities are activities that are less, they’re more generalizable across a variety of different tasks and that’s where you’re seeing things like copilot and other build ONS that wouldn’t addressability been possible. 0:3:34.130 –> 0:3:55.90 Nathan Lasnoski And the way we built AI models before now being able to be actualized within our organizations and I’ll show you some examples of what that looks like moving into the idea of directed autonomous activities in creative directive activities that are tied to taking action on things that I’m working with that agent to do. 0:3:55.260 –> 0:3:58.650 Nathan Lasnoski These are partnerships between a human and an AI agent. 0:3:58.830 –> 0:4:6.770 Nathan Lasnoski We’re we haven’t truly gotten to yet, is this idea of general autonomous activities or creative autonomous activities, but that is coming. 0:4:6.780 –> 0:4:23.420 Nathan Lasnoski That is where we know that AI sort of functions like an intern right now, but it has to be heavily directed and it has to be heavily prescribed what it’s gonna get to is that intern’s gonna grow up that interns gonna turn into a full fledged employee within your team that enables you to offload activities. 0:4:23.430 –> 0:4:28.390 Nathan Lasnoski That person, particularly in the financial space, so a lot of opportunities for us to talk about that today. 0:4:29.670 –> 0:4:33.840 Nathan Lasnoski So let’s start by framing the AI journey and what what? 0:4:33.910 –> 0:4:35.880 Nathan Lasnoski You know, companies are doing in this space. 0:4:36.330 –> 0:4:39.260 Nathan Lasnoski We usually think about this in six major steps. 0:4:39.530 –> 0:4:49.760 Nathan Lasnoski The first step is this idea of executive alignment, and most organizations that are providing financial services or something around the insurance space, they already have a business mission. 0:4:49.770 –> 0:4:50.920 Nathan Lasnoski You exist for a reason. 0:4:51.150 –> 0:5:1.190 Nathan Lasnoski You’re doing your work within the world because you’re doing something valuable, and the best companies and adopting AI, they’re not changing the mission of their business to be able to do this. 0:5:1.600 –> 0:5:4.410 Nathan Lasnoski And they’re not necessarily changing their business strategy. 0:5:4.480 –> 0:5:7.390 Nathan Lasnoski What they’re doing is saying I have a business strategy over X number of years. 0:5:7.500 –> 0:5:24.120 Nathan Lasnoski How does AI fulfill that strategy and how does it help me gain ground and producing more revenue, retaining customers, increasing customer satisfaction in the relationship with me, building operational savings and how I do the work for my customers and all that has to support the business mission. 0:5:25.150 –> 0:5:36.130 Nathan Lasnoski So the best companies are starting with that mission and then building a strategy that’s based upon that mission, not just based upon like whether if this use case is successful or fails, that’s it for my AI journey. 0:5:36.140 –> 0:5:37.790 Nathan Lasnoski Like, that’s not how this story works. 0:5:37.800 –> 0:5:42.70 Nathan Lasnoski Like you’re focusing on your business mission and all this needs to support that. 0:5:42.400 –> 0:6:16.400 Nathan Lasnoski So once companies think about it in that context, they then start thinking about envisioning and where that moves from there and how do I leverage both kinds of capabilities in mission driven opportunities and commodity opportunities that create value for me in from that you then sort of earn the right to be able to do proofs of concepts and pilots that move into production implementations that are then leads you into providing value back to the business that you’re measuring and you’re showing the outcomes from and sometimes that can move really fast in terms of like copilot deployment. 0:6:16.670 –> 0:6:24.80 Nathan Lasnoski And sometimes it takes a lot of work to be able to make sure that there’s a follow through on the swing to get to a point where that’s being leveraged inside of my organization. 0:6:24.350 –> 0:6:27.360 Nathan Lasnoski And then on the tail end, you see this idea of skilled pattern. 0:6:27.370 –> 0:6:31.560 Nathan Lasnoski What this is really dealing with is everything here is somewhat unconnected. 0:6:31.750 –> 0:6:33.460 Nathan Lasnoski I’m doing work within copilot. 0:6:33.530 –> 0:6:35.380 Nathan Lasnoski I’m building something in copilot studio. 0:6:35.450 –> 0:6:37.30 Nathan Lasnoski I’m creating a custom AI model. 0:6:37.420 –> 0:6:43.450 Nathan Lasnoski All of that fits together in a way that has to work as a seamless capability for my end users. 0:6:43.460 –> 0:6:51.930 Nathan Lasnoski If I’m delivering it to my customers of my business, or if I’m delivering it to my agents or my individuals that are working on plans, all of that has to fit together. 0:6:52.60 –> 0:6:55.810 Nathan Lasnoski So the scale pattern is hey, if I do a lot of this, how does it fit together? 0:6:55.820 –> 0:6:56.970 Nathan Lasnoski How do I think about that picture? 0:6:58.10 –> 0:7:0.250 Nathan Lasnoski So companies will go down that that path as well. 0:7:1.390 –> 0:7:11.0 Nathan Lasnoski So the two domains that we’re going to talk about today are commodity and mission driven and commodity is this idea of every person you organizer. 0:7:11.10 –> 0:7:16.120 Nathan Lasnoski Has the ability to be more as a result of leveraging AI without having to build a custom model. 0:7:16.130 –> 0:7:20.80 Nathan Lasnoski It’s not the build story, it’s the leverage and use story. 0:7:20.210 –> 0:7:23.480 Nathan Lasnoski So a perfect example of this is spell check. 0:7:23.570 –> 0:7:26.80 Nathan Lasnoski So like everybody uses spell check right now, right? 0:7:26.270 –> 0:7:29.580 Nathan Lasnoski If I took that away from you, you’d probably have some problems. 0:7:29.590 –> 0:7:30.760 Nathan Lasnoski You won’t be able to write your emails. 0:7:30.770 –> 0:7:32.100 Nathan Lasnoski Well, you’d be slower to do it. 0:7:32.320 –> 0:7:34.870 Nathan Lasnoski Yeah, nobody has a dictionary sitting next to them anymore. 0:7:34.880 –> 0:7:43.550 Nathan Lasnoski So you just would notice this immediate productivity loss that you just don’t really realize is there because you just use it almost without thinking about it. 0:7:43.740 –> 0:8:0.560 Nathan Lasnoski That’s sort of commodity AI continue will continue to push us forward each commodity AI capability that we adopt will become something like spell check where you get to a point where it’s like, well, it’s just this is just spell check like what do you mean I stay AI like no, this is that is a very rudimentary version of AI. 0:8:1.870 –> 0:8:5.570 Nathan Lasnoski And then on the right hand side, you have ideas like mission driven. 0:8:5.580 –> 0:8:9.260 Nathan Lasnoski These are things that are built on the very nature of your business. 0:8:9.270 –> 0:8:12.320 Nathan Lasnoski The heart of your business, they have to be very accurate. 0:8:12.330 –> 0:8:13.630 Nathan Lasnoski They have to be prescriptive. 0:8:13.640 –> 0:8:15.60 Nathan Lasnoski They have to be precise. 0:8:15.360 –> 0:8:19.640 Nathan Lasnoski They have to answer questions a very specific way, not a generalizable way. 0:8:19.650 –> 0:8:31.270 Nathan Lasnoski A very specific way, and oftentimes that’s where you gain tons of ground in the market because you’re disrupting something or you’re bringing something to the table faster that you alternately did very manually. 0:8:31.620 –> 0:8:34.430 Nathan Lasnoski So both of these fit together in a in a very cohesive way. 0:8:35.840 –> 0:8:44.250 Nathan Lasnoski So when you think about that in the context of concerns, these are the primary concerns that we’re seeing that companies have as they start to adopt AI. 0:8:44.780 –> 0:8:54.470 Nathan Lasnoski The first concern, and this is just based on a lot of fear, uncertainty and doubt, but also some real stuff, is the data privacy concern and where this primarily comes from. 0:8:54.480 –> 0:9:6.750 Nathan Lasnoski Is people using chat GPT realizing that that data put into chat GPT can be used to retrain other models or just the realization that anything I put in Google is used to sell me something and maybe something? 0:9:6.760 –> 0:9:15.920 Nathan Lasnoski I’ve even said out loud like Google knows, I said out loud is selling me and putting into my ad stream like we know there’s a data privacy situation going on here. 0:9:15.990 –> 0:9:22.600 Nathan Lasnoski How do I make sure my data and my customer’s data isn’t used outside of what I am building for my own organization? 0:9:23.690 –> 0:9:24.980 Nathan Lasnoski This is very important. 0:9:25.90 –> 0:9:30.380 Nathan Lasnoski Everything we talk about today is using private instances, so internal implementations. 0:9:30.390 –> 0:9:38.480 Nathan Lasnoski Your data being your data and no one elses is a very critical delineation between some of the public implementations of the AI that we’re seeing. 0:9:39.90 –> 0:9:41.260 Nathan Lasnoski The second is this idea of data readiness. 0:9:41.570 –> 0:9:44.180 Nathan Lasnoski This is really a use case by use case situation. 0:9:44.310 –> 0:9:53.610 Nathan Lasnoski Sometimes the long term story you’re going after requires an enormous amount of data, and you’re planning very proactively to go after that because you know what your end game is. 0:9:53.940 –> 0:9:56.550 Nathan Lasnoski Sometimes it’s a relatively incremental goal. 0:9:56.600 –> 0:10:0.420 Nathan Lasnoski You already have the data, and AI is a great build on something you’re already doing. 0:10:0.620 –> 0:10:4.970 Nathan Lasnoski We’ll talk about examples in both of those streams, so data writing it shouldn’t be like a cop out. 0:10:4.980 –> 0:10:14.420 Nathan Lasnoski It should be like a very intentional understanding of how your data relates to the use cases that are most important to your strategy in the financial services space. 0:10:14.490 –> 0:10:20.310 Nathan Lasnoski Human displacement represents probably one of the more significant situations that we need to be thinking about. 0:10:20.320 –> 0:10:25.400 Nathan Lasnoski Every one of your employees is gonna be changed as a result of AI, and that’s gonna happen over the next one to three years. 0:10:25.530 –> 0:10:31.620 Nathan Lasnoski So as you are engaging in that story, if don’t think about it as a reactive thing, think of as a proactive thing. 0:10:31.630 –> 0:10:34.540 Nathan Lasnoski How do I enable people to take on new skills and new capabilities? 0:10:34.550 –> 0:10:35.680 Nathan Lasnoski We will talk about that. 0:10:35.890 –> 0:10:44.830 Nathan Lasnoski The tail end of this presentation today, the 4th and probably the most second most significant thing we hear is what if I gets it wrong? 0:10:44.890 –> 0:10:46.0 Nathan Lasnoski Like, what’s what? 0:10:46.10 –> 0:10:46.790 Nathan Lasnoski What do I do then? 0:10:47.130 –> 0:10:50.780 Nathan Lasnoski And quality and hallucination is really at the heart of that. 0:10:50.790 –> 0:11:1.180 Nathan Lasnoski Like there’s a lot of conversation about AIS making this thing up, or it’s answering it incorrectly or there it says this lawyer that responded to a brief and excited incorrect case like case law. 0:11:1.190 –> 0:11:2.170 Nathan Lasnoski That’s fictitious, right? 0:11:2.180 –> 0:11:2.780 Nathan Lasnoski These things exist. 0:11:4.90 –> 0:11:17.510 Nathan Lasnoski So what’s really important is that a we’re building in the right measures to know whether or not we’re being successful with our AI model, just to all up, and because many things that we go to implement AI models around, they’re currently not measured well enough. 0:11:17.790 –> 0:11:27.380 Nathan Lasnoski 2nd is you have to build an hallucination and protection to understand whether it’s getting this answer from the accurate source or if it made it up. 0:11:27.450 –> 0:11:33.440 Nathan Lasnoski So there’s ways to be able to mitigate some of those concerns that allow you to build confidence back into the business. 0:11:33.760 –> 0:11:37.600 Nathan Lasnoski And then the final thing is biased, particularly important in financial services. 0:11:37.610 –> 0:11:47.520 Nathan Lasnoski Industry biases this area that, like data, can be used in a way that can draw people into outcomes that aren’t appropriate outcomes for who they are. 0:11:47.730 –> 0:12:2.810 Nathan Lasnoski And we need to be very intelligent about how we use that, especially because the financial services industry is so close to people’s goals in life that if we’re not thinking about the way our data is going to be used, then we can put ourselves in a position where we’re leading to outcomes that we didn’t expect. 0:12:3.120 –> 0:12:7.780 Nathan Lasnoski So all of these are important, but things that we can manage throughout the development process of AI solutions. 0:12:9.330 –> 0:12:21.180 Nathan Lasnoski So as we think about AI and fencer of use cases, I want you to think about these the way the domains between commodity and mission driven start to break down into practical practical scenarios. 0:12:21.450 –> 0:12:43.750 Nathan Lasnoski So on the full 4 right hand side here you can see uses AI aligning to things like M365, copilot configures pre built AI elements starting to align the things like Microsoft Copilot studio and this stone right here associated with building highly functioning AI models so associated with things like AI. 0:12:43.810 –> 0:12:47.940 Nathan Lasnoski Azure ML Studio are these the only products that exist inside of these domains? 0:12:47.990 –> 0:12:48.970 Nathan Lasnoski Absolutely not. 0:12:49.40 –> 0:12:51.960 Nathan Lasnoski They are products that are examples of those domains. 0:12:52.920 –> 0:12:58.510 Nathan Lasnoski You going to find many different platforms that start to light up commodity AI, semi custom and fully customs use cases. 0:12:58.700 –> 0:13:6.250 Nathan Lasnoski But what’s important is that if you need more accuracy and precision, it’s gonna lead toward more custom ML scenarios. 0:13:6.480 –> 0:13:12.570 Nathan Lasnoski And as you need less of that, the commodity AI scenarios start to take hold and are easier to take advantage of. 0:13:12.580 –> 0:13:25.440 Nathan Lasnoski So you can see in commodity AI things like writing a faster email or creating a marketing asset in the semi custom you see things like connecting service now to teams or documents that are being surfaced for people to be able to find them or answer questions from them. 0:13:25.830 –> 0:13:31.30 Nathan Lasnoski But in this middle zone, you see things like high visibility chat bots or require custom engineering. 0:13:31.40 –> 0:13:37.340 Nathan Lasnoski I’ll show you a really interesting one of those that’s disrupting the loan market or client portfolio optimization. 0:13:37.450 –> 0:13:38.980 Nathan Lasnoski This is people’s money we’re talking about. 0:13:38.990 –> 0:13:43.460 Nathan Lasnoski We need to make really intelligent decisions about that and we know why it came up with that decision. 0:13:43.630 –> 0:13:51.140 Nathan Lasnoski So as we move into certain zones, we need to build models that are very trustworthy and are based on data that we understand. 0:13:51.150 –> 0:13:55.190 Nathan Lasnoski And we have an explainability associated with that sort of model. 0:13:56.890 –> 0:14:0.580 Nathan Lasnoski So, umm, talking through a little bit of the commodity examples. 0:14:3.200 –> 0:14:10.610 Nathan Lasnoski Here’s a few that I think everyone could take advantage of finding information and answers, summarizing meetings and action items. 0:14:10.760 –> 0:14:11.450 Nathan Lasnoski Creative work. 0:14:11.460 –> 0:14:15.50 Nathan Lasnoski Things where we were like the the world is full of the Johnny come lately? 0:14:15.60 –> 0:14:15.390 Nathan Lasnoski Yeah. 0:14:15.400 –> 0:14:15.930 Nathan Lasnoski Expert right. 0:14:15.940 –> 0:14:23.790 Nathan Lasnoski But like the creative work of these prompts will lead to me being able to do more of a colleague of mine says never create a draft again, right? 0:14:23.800 –> 0:14:26.890 Nathan Lasnoski This idea of like basically the hardest word to write is always the first one. 0:14:26.980 –> 0:14:31.350 Nathan Lasnoski So the creative work is just an idea of like getting me started, analytical work. 0:14:31.420 –> 0:14:57.480 Nathan Lasnoski This idea of doing things that require a lot of deep processing time to get to an answer that Excel copilot is got some really cool on fire capabilities to take a look at planning their day one of my colleagues just got back from vacation, went to copilot and said what happened was call it and I think just even things like that like joining and joining a meeting late and getting the summary some really interesting capabilities. 0:14:57.960 –> 0:14:59.380 Nathan Lasnoski And then admin tasks. 0:14:59.390 –> 0:15:6.510 Nathan Lasnoski So I want to take a break here and ask Brian like anything that for you, you’re also copilot user uh. 0:15:7.130 –> 0:15:18.570 Nathan Lasnoski And you have had some experience working with it or anything that’s stuck out to you when you think about things that have really forced multiplied your general tasks, not even like the mission German kind of thing. 0:15:18.580 –> 0:15:19.860 Nathan Lasnoski Just stuff you’re doing on a daily basis. 0:15:20.730 –> 0:15:24.410 Brian Haydin Well, I do a lot of presentations and so probably one of the most. 0:15:38.330 –> 0:15:38.610 Nathan Lasnoski Umm. 0:15:25.200 –> 0:15:41.870 Brian Haydin Uh time saving things that I can do is uh for example, add a slide to my PowerPoint presentation that talks or compares two different, you know ideas and it usually comes with a kind of a creative, beautiful thing. 0:15:41.880 –> 0:15:49.910 Brian Haydin It basically goes out and does a Google search for me or Bing search and pulls some results in it and it’s a great starting point. 0:15:50.130 –> 0:16:1.590 Brian Haydin Another thing that I like to talk about is something that happened like the first day that I was using copilot, and I was in a meeting and I missed something. 0:16:1.640 –> 0:16:5.170 Brian Haydin Like somebody said something and I I kind of missed it and I typed in there. 0:16:5.340 –> 0:16:7.810 Brian Haydin Hey, tell me what John just said. 0:16:7.860 –> 0:16:16.750 Brian Haydin You know 2 minutes ago about what he did on Monday and pulled right back, though the summary I didn’t have to, like, embarrass myself and say, what did you say? 0:16:16.760 –> 0:16:19.130 Brian Haydin I didn’t catch that or I wasn’t paying attention. 0:16:19.820 –> 0:16:28.700 Brian Haydin I was able to get right up really quick, so a lot of really amazing things that can do and every single day I’m trying something new that just surprises me. 0:16:28.710 –> 0:16:29.860 Brian Haydin Like I can’t believe this does that. 0:16:30.380 –> 0:16:30.970 Nathan Lasnoski Yeah, totally. 0:16:31.580 –> 0:16:33.220 Brian Haydin I would also add one last thing too. 0:16:33.790 –> 0:16:34.330 Brian Haydin Uh. 0:16:34.960 –> 0:16:39.440 Brian Haydin Fortunately, you know, concurrencies great about, you know, giving me access to it. 0:16:39.910 –> 0:16:42.320 Brian Haydin But if they didn’t, I I would do this myself. 0:16:42.400 –> 0:16:45.220 Brian Haydin It it is just I I would buy the license. 0:16:45.230 –> 0:16:46.210 Brian Haydin I would make concurrency. 0:16:46.220 –> 0:16:46.920 Brian Haydin I just pay them. 0:16:47.650 –> 0:16:48.380 Brian Haydin I’m gonna do it. 0:16:48.570 –> 0:16:49.50 Brian Haydin It’s great. 0:16:51.650 –> 0:16:53.340 Nathan Lasnoski So let’s show him a couple of examples. 0:16:53.890 –> 0:17:3.480 Nathan Lasnoski So one of the starting points, a lot of people see is the idea of never create a draft again and starting through the process of developing a document. 0:17:3.490 –> 0:17:13.480 Nathan Lasnoski Now I finally my perspective of this in the PowerPoint drafting is it’s much better when you give it excellent source material. 0:17:13.750 –> 0:17:18.180 Nathan Lasnoski So if you give it the you give it nodes, you give it a template, you want it to use. 0:17:18.750 –> 0:17:35.590 Nathan Lasnoski That tends to accelerate its capability over like starting from total scratch, but I found this to be a great starting point, especially the things I don’t like doing, like running a statement of work or, umm, developing a template for something just getting me down the road and not having to spend the time on the initial words. 0:17:35.600 –> 0:17:40.90 Nathan Lasnoski Especially something where like it’s it’s a functional document. 0:17:40.100 –> 0:17:43.890 Nathan Lasnoski It’s not like what I blog on something or I write online on LinkedIn. 0:17:44.20 –> 0:17:47.370 Nathan Lasnoski I generally issue using AI for that. 0:17:47.380 –> 0:17:54.50 Nathan Lasnoski I really prefer to just make that my own authentic self, but when I write like a statement of work or something, I really prefer just like man. 0:17:54.60 –> 0:18:8.100 Nathan Lasnoski How can I get this done faster because I hate the process of creating those things, so if I can use a tool like this to get it done faster, 100% same situation with PowerPoints so like I do a lot of PowerPoint creation just like Brian. 0:18:8.210 –> 0:18:22.160 Nathan Lasnoski And can I use a presentation on presentation asset to have it create just starting point of ideas that I can use to be able to think about the comments I’m bringing back to a company and that can really help me take that first step. 0:18:22.710 –> 0:18:24.400 Nathan Lasnoski I’ll say my favorite. 0:18:26.650 –> 0:18:26.920 Nathan Lasnoski Right. 0:18:27.940 –> 0:18:31.190 Nathan Lasnoski And then we’re we’re starting to make some of these capabilities very specific. 0:18:31.200 –> 0:18:45.590 Nathan Lasnoski So there’s companies even coming out that produce presentation assets that plug into copilot that start to do very financial industry specific activities like presentation prep associated with industry or company research to help you prepare for the presentation you’re doing. 0:18:45.640 –> 0:18:56.310 Nathan Lasnoski So facts, that’s an example, that there’s a whole variety of platforms that are doing a lot of similar things that help inform the platform that are already plugged into copilot that help you gain ground. 0:18:58.50 –> 0:19:6.0 Nathan Lasnoski Probably my favorite copilot feature out of every feature is meeting summaries just like Brian. 0:19:6.10 –> 0:19:10.960 Nathan Lasnoski What you said like this requires 0 prep like this. 0:19:11.670 –> 0:19:18.640 Nathan Lasnoski There’s a lot of things in copilot you need to know how to use various specifically to gain ground for it to be something that’s really useful for you. 0:19:18.650 –> 0:19:20.540 Nathan Lasnoski And there’s some things that need to be turned on. 0:19:20.870 –> 0:19:24.0 Nathan Lasnoski Meeting summaries tend to be something that are like very intuitive. 0:19:24.90 –> 0:19:34.180 Nathan Lasnoski So for example, in this meeting summary, this is using copilot in teams premium, it’s giving me an index of when people talked what the meeting notes were, follow up tasks and questions. 0:19:34.190 –> 0:19:36.330 Nathan Lasnoski I’d like to ask are things like what was the tone? 0:19:36.560 –> 0:19:40.590 Nathan Lasnoski And here you can see it’s actually describing the tone very accurately. 0:19:40.600 –> 0:19:43.380 Nathan Lasnoski The tone between liberty and currency was professional and collaborative. 0:19:43.390 –> 0:19:45.110 Nathan Lasnoski They discussed various topics. 0:19:46.60 –> 0:19:48.850 Nathan Lasnoski There weren’t any disagreements, et cetera, et cetera, right? 0:19:48.860 –> 0:19:50.540 Nathan Lasnoski So, like, do you make me a good description? 0:19:50.550 –> 0:19:53.600 Nathan Lasnoski Another thing I really love about it is joining and meeting late. 0:19:53.610 –> 0:19:56.140 Nathan Lasnoski Just happens to me because I’ve always got this back to back stuff. 0:19:57.10 –> 0:20:0.0 Nathan Lasnoski Join in meeting what happened in meeting so far, just like you said, Brian. 0:20:0.10 –> 0:20:2.260 Nathan Lasnoski Like that alone is useful. 0:20:3.10 –> 0:20:4.560 Nathan Lasnoski Just being able to tell me what happened. 0:20:4.570 –> 0:20:5.570 Nathan Lasnoski What’s the action items? 0:20:5.580 –> 0:20:20.680 Nathan Lasnoski What’s the summary of what occurred within this meeting, giving us a pretty good understanding of, like, what’s what’s happening and you can back that out on a more general basis of what’s happening in the context of my relationship with this person or account or company or project. 0:20:20.800 –> 0:20:22.810 Nathan Lasnoski And it can give you those summaries and assets. 0:20:23.0 –> 0:20:24.730 Nathan Lasnoski So why this is so useful? 0:20:24.840 –> 0:20:36.330 Nathan Lasnoski Nobody wants to be the meeting note taker and meeting no taking is often really arduous and annoying, and that person tends not to be a very engaged in the meeting, so being able to focus people’s time and enable them to be engaged. 0:20:37.630 –> 0:20:40.360 Nathan Lasnoski OK, So what have we learned from copilot adoption? 0:20:40.440 –> 0:20:42.510 Nathan Lasnoski What we’ve learned is that people don’t want to give it up. 0:20:42.520 –> 0:20:51.240 Nathan Lasnoski That’s really at the end the net net of it is that people find there’s enough benefit that they don’t want to give it up and that it leads to aggressive productivity. 0:20:51.290 –> 0:20:52.470 Nathan Lasnoski And this is just the start of it. 0:20:52.480 –> 0:20:54.930 Nathan Lasnoski I mean really, copilot is like six months old. 0:20:54.940 –> 0:20:59.730 Nathan Lasnoski Like we’re getting to a point where, like, wow, like, I’ve already getting benefit imagine was gonna be in a year. 0:21:0.120 –> 0:21:1.590 Nathan Lasnoski So amazing stuff. 0:21:1.600 –> 0:21:8.630 Nathan Lasnoski So I think this is really something that is sort of an obvious go do for most companies, especially information workers. 0:21:9.640 –> 0:21:11.610 Nathan Lasnoski But you gonna have to be intentional about your adoption. 0:21:11.620 –> 0:21:12.850 Nathan Lasnoski It’s not like you just go turn it on. 0:21:12.860 –> 0:21:18.120 Nathan Lasnoski You do have some activities you need to go through in terms of doing an effective adoption and that’s something we can talk more about. 0:21:19.300 –> 0:21:22.410 Nathan Lasnoski OK, so let’s get into mission driven scenarios. 0:21:22.420 –> 0:21:28.350 Nathan Lasnoski These are going to be ones that are very specific to financial services industry and we’ll group them into two sets. 0:21:28.360 –> 0:21:37.450 Nathan Lasnoski So the first set and I’ve kind of boxed these all together to go through them, but I just want to show two different examples of spaces that we’ve been talking with. 0:21:37.530 –> 0:21:40.410 Nathan Lasnoski So the first space is things like banking and capital markets. 0:21:40.520 –> 0:21:58.10 Nathan Lasnoski You can see a lot of very interesting use cases on here, loan origination automation, customer support, it being a knowledge base, helping build teams and next best action activities together even like pitch book generations, stuff that I sort of started to show earlier risk mitigation claim automation. 0:21:58.20 –> 0:22:15.500 Nathan Lasnoski There’s a lot that goes into the banking and capital market space and many of these have been examples that we’re seeing companies wanting to talk about similar kinds of conversations and insurance, except it’s a slight pivot, right, a lot of customer support automation happening here trying to drive a better relationship. 0:22:16.520 –> 0:22:17.850 Nathan Lasnoski Iron uh. 0:22:17.890 –> 0:22:24.870 Nathan Lasnoski The scenario where I was adding my daughter onto my insurance for my car and I was curious what I was gonna cost. 0:22:24.980 –> 0:22:34.120 Nathan Lasnoski So I called my insurance agent and they didn’t know how to like answer that question quickly for me and eventually they said they get it back to me. 0:22:34.130 –> 0:22:35.750 Nathan Lasnoski Never did I had to call them back. 0:22:35.760 –> 0:22:40.930 Nathan Lasnoski It’s just like a totally arduous, annoying experience that where I was like looking for a great customer service and I didn’t get it. 0:22:41.400 –> 0:22:46.350 Nathan Lasnoski And then it would have been one of the scenarios like, can’t I just go to the app and ask this question? 0:22:46.360 –> 0:22:48.90 Nathan Lasnoski Like what would it cost to add my daughter? 0:22:48.100 –> 0:22:48.790 Nathan Lasnoski She’s 16. 0:22:48.800 –> 0:22:49.870 Nathan Lasnoski Never been in an accident. 0:22:49.880 –> 0:22:51.290 Nathan Lasnoski Good student, et cetera, et cetera. 0:22:51.440 –> 0:22:51.660 Nathan Lasnoski Right. 0:22:51.670 –> 0:23:9.130 Nathan Lasnoski Like it should just be able to tell me these are scenarios where like insurance and capital markets are lighting up new asynchronous abilities for people to get their questions answered while they keep doing the regular tasks rather than having to go and have to actually talk to a person. 0:23:10.380 –> 0:23:13.280 Nathan Lasnoski And there’s many times you want them to, but there’s a lot of times you don’t. 0:23:13.290 –> 0:23:15.410 Nathan Lasnoski You just want them to be able to get their question answered. 0:23:15.420 –> 0:23:21.360 Nathan Lasnoski You want to have the best relationship possible, so we’re going to talk through a variety of examples and cover those. 0:23:22.640 –> 0:23:33.810 Nathan Lasnoski And Brian, any within these two zones that are really sticking out to you that you think have been meaningful both inside of banking and capital markets as well as insurance before we start to show some of the examples? 0:23:34.860 –> 0:23:35.670 Brian Haydin Yeah. 0:23:35.820 –> 0:23:51.900 Brian Haydin So in the banking, you know, world, the regulate the regulatory mock, right, getting you know, getting answers about regulations is something that I I see you know a really strong use case for in some of our customers have experimented with it. 0:23:52.110 –> 0:23:57.290 Brian Haydin And similarly with insurance companies with the insurance world. 0:23:57.520 –> 0:24:29.550 Brian Haydin You know these verbos contracts, you know, being able to answer questions about some of your customers contracts can be a daunting, you know, question or like an insurance policy or you know finding understanding some of the financial, you know details about what a contract you know is going to turn into like an A specific example that I worked with the customer on was trying to find variances between margins across their different contracts and identify all liars. 0:24:29.820 –> 0:24:40.740 Brian Haydin So a lot of good use cases and the documents that usually Support these are usually verbose and let an LLM take care of that work for you. 0:24:42.520 –> 0:24:42.840 Nathan Lasnoski Awesome. 0:24:42.850 –> 0:24:43.390 Nathan Lasnoski Thanks, Brian. 0:24:44.990 –> 0:24:45.520 Nathan Lasnoski OK. 0:24:45.530 –> 0:24:50.100 Nathan Lasnoski So as we talk into some examples, you’re going to notice they’re going to fall into two lanes. 0:24:50.160 –> 0:24:55.20 Nathan Lasnoski One is incremental examples, things that you’re already doing. 0:24:55.30 –> 0:25:2.860 Nathan Lasnoski You just wanna do faster and then this disruptive idea, which is the market doesn’t truly engage this way now. 0:25:2.910 –> 0:25:18.230 Nathan Lasnoski And we’re gonna come to the market in a way that changes the way that your customers engage with in the transaction or the customer service relationship and take a whole set of functions within the market and shift it on shift it. 0:25:18.300 –> 0:25:22.310 Nathan Lasnoski So this just show you some examples of where companies are doing that in the financial services space. 0:25:23.980 –> 0:25:24.510 Nathan Lasnoski OK. 0:25:24.840 –> 0:25:34.340 Nathan Lasnoski So on that note, uh, this is one that we are working with the company right now on which is self service loan application with 0 harm to credit. 0:25:34.550 –> 0:26:9.940 Nathan Lasnoski So this is a company that has launched a ability to not use a loan officer as part of the loan application process for a certain lane of self service loans that’s now live in California and their idea and the way they built this is to use a self service experience kind of like if you’ve done your taxes on TurboTax kind of like TurboTax except for the lone industry and doing an amazing job of thinking about like how do I engage this space differently and how do I help them to think about the problem differently? 0:26:10.90 –> 0:26:17.440 Nathan Lasnoski How do I engage at like an average consumer who just has a standard loan and maybe ask them the best questions possible? 0:26:17.630 –> 0:26:29.200 Nathan Lasnoski So I can mitigate common problems before they get to the hard credit pull and do this in a self service way where they’re saving on what they would traditionally be paying for those those costs. 0:26:29.380 –> 0:26:45.950 Nathan Lasnoski So this is really interesting example of where like a whole lot of people spend their time doing that today and they’re going to have to think about how do I do that differently or how do I build value on top of the ability to do this in a self service way rather than necessarily having to do the entire process. 0:26:46.160 –> 0:26:51.440 Nathan Lasnoski So fascinating how this is changing so quickly and how this is driving. 0:26:51.450 –> 0:26:58.390 Nathan Lasnoski They have the opportunity to drive tremendous value for consumers, but also impact into the sort of loan origination space. 0:26:59.920 –> 0:27:2.870 Nathan Lasnoski Another example, and I think this is I think this is fascinating. 0:27:2.880 –> 0:27:5.810 Nathan Lasnoski And when you do your taxes, your take a quick look for this. 0:27:6.60 –> 0:27:13.720 Nathan Lasnoski So into it launched their self service chat bot that exists in the TurboTax experience. 0:27:13.980 –> 0:27:24.270 Nathan Lasnoski So in the process of going through and filling out your taxes, they have these sort of like guided experiences and non guided experiences and then totally self driven, right? 0:27:24.460 –> 0:27:29.550 Nathan Lasnoski What they launched is this chat bot that pops up in a couple different spots. 0:27:29.560 –> 0:27:31.600 Nathan Lasnoski It pops up when you’re missing items. 0:27:31.610 –> 0:27:33.680 Nathan Lasnoski It pops up when you have questions. 0:27:33.690 –> 0:27:37.350 Nathan Lasnoski It provides interactive help, direct and then it also leads. 0:27:38.500 –> 0:27:40.210 Nathan Lasnoski Do you into a directed conversation? 0:27:40.220 –> 0:27:55.430 Nathan Lasnoski If you need it escalates to person enabled versus AI agent enabled and I think this is a nice example of what many financial services, insurance and even this kind of tax prep service scenario can do, which is I need to figure out a way to meet my customer where their app. 0:27:55.640 –> 0:28:1.730 Nathan Lasnoski How can I help my customer to get their question answered faster or help them through the process of answering their question? 0:28:1.900 –> 0:28:7.340 Nathan Lasnoski So in this context, I’m, uh, finding that there’s a question about the refinance. 0:28:7.350 –> 0:28:8.980 Nathan Lasnoski It wasn’t answered prior. 0:28:9.70 –> 0:28:10.260 Nathan Lasnoski We’re bringing that up. 0:28:10.270 –> 0:28:18.960 Nathan Lasnoski We’re asking them to answer it right in line and then that’s going right back into the form that we’re filling out to be able to correct it before it becomes a problem in the actual return. 0:28:19.230 –> 0:28:28.330 Nathan Lasnoski Same kind of idea on any kind of question I might have in the context of filling out my form and what’s the state of my taxes and my. 0:28:30.70 –> 0:28:31.40 Nathan Lasnoski Exposed. 0:28:31.50 –> 0:28:33.810 Nathan Lasnoski Is this a high risk situation, et cetera, et cetera. 0:28:34.100 –> 0:28:36.90 Nathan Lasnoski Some of those questions, I think it does really well. 0:28:36.180 –> 0:28:42.310 Nathan Lasnoski Some of those questions, I think they’re holding back and they’re still working on it, but it’s a great start for where they’re going with the platform. 0:28:42.560 –> 0:29:4.280 Nathan Lasnoski So customer support just all up is a great use case for using AI and enabling your customers to be able to get a better experience from you, enabling them to get information faster around their own policy or their changes to their policy move adds changes on in the context that it’s supported by by your people that can make sure that certain certain things are done right. 0:29:4.710 –> 0:29:8.950 Nathan Lasnoski So a great starting point and something to think about pretty strongly. 0:29:10.180 –> 0:29:21.690 Nathan Lasnoski Another example in the same space in this is it’s a healthcare example, but I think it’s very meaningful because this is actually something we all deal with is questions about your insurance plan. 0:29:21.880 –> 0:29:27.40 Nathan Lasnoski So, and there’s many companies we’re working with that do things like processing of transactions and so on. 0:29:27.50 –> 0:29:42.220 Nathan Lasnoski They have questions about the goal of this is to say if someone has questions about their insurance plan internal to a company or if I’m a serving uh, if I’m I’m served by one of these larger insurance firms, what does that exactly look like and how do I ask that question? 0:29:42.230 –> 0:29:46.160 Nathan Lasnoski Well, I can give that in a self service experience where they never have to call their HR professional. 0:29:46.250 –> 0:29:47.810 Nathan Lasnoski They never have to call the insurance company. 0:29:47.820 –> 0:29:49.740 Nathan Lasnoski They don’t have to call it an insurance professional. 0:29:49.830 –> 0:29:53.840 Nathan Lasnoski They can get this questions answered very directly for them like is this covered? 0:29:53.850 –> 0:29:55.120 Nathan Lasnoski Is it something that’s in my plan? 0:29:55.130 –> 0:29:57.180 Nathan Lasnoski Is it something that it does or doesn’t do? 0:29:57.190 –> 0:30:0.570 Nathan Lasnoski Like maybe I had a question about similar JSON idea. 0:30:2.480 –> 0:30:4.810 Nathan Lasnoski Like if a tree falls on my house is a covered. 0:30:4.940 –> 0:30:21.270 Nathan Lasnoski If a tree that I failed to like, if I knew, was if the branch was falling off and I didn’t do something about it, is it still covered like these are some of these questions that you might put into a chat and see what it says and we can optimize that. 0:30:21.280 –> 0:30:28.790 Nathan Lasnoski We can send only the most important questions to your Rep and answer the questions correctly that we already know about back to individual individual consumers. 0:30:30.240 –> 0:30:30.600 Nathan Lasnoski Umm. 0:30:31.160 –> 0:30:37.670 Nathan Lasnoski So another space that we’re seeing this and I’m, I love Brian, to talk more about this in a second is things like customer call analysis. 0:30:37.680 –> 0:30:45.770 Nathan Lasnoski So we are seeing that oftentimes companies have these big phone banks of individuals they’re working on answering customer calls. 0:30:45.780 –> 0:31:7.230 Nathan Lasnoski They’re optimizing different parts of the process and things that you saw earlier in the commodity space or things like did I that this can you could simply ask the question of the call is was there a customer intro, was there information provided that was accepted by the customer, what was the tone like, what was the tone in the call started, what was the tone when the call ended? 0:31:7.290 –> 0:31:21.740 Nathan Lasnoski And my customer wrapped on average bring my my people that are calling in their frustrated forward to a space where they’re less frustrated by the time they end the call or are they basically the same spot and I’m not moving in a better direction. 0:31:21.830 –> 0:31:27.610 Nathan Lasnoski Is there a follow-up action taken this can be taken directly from call logs or call recordings. 0:31:27.750 –> 0:31:33.560 Nathan Lasnoski So for example, what was the tone of the call Elise tone during the meeting was polite and respectful. 0:31:33.570 –> 0:31:43.370 Nathan Lasnoski They introduced themselves etcetera, etcetera and then be able to take the action items that are coming out of that leading to a direct relationship from call data. 0:31:43.440 –> 0:31:55.350 Nathan Lasnoski How I’m tracking them to be able to then take something that in the past, even a year ago would have been impossible like the amount of AI training to be able to just do call analysis is ridiculous. 0:31:55.620 –> 0:32:3.90 Nathan Lasnoski Now the ability to use large language models to infer that information and then provide data back is fantastic. 0:32:3.160 –> 0:32:9.160 Nathan Lasnoski So this is a space that many organizations can use at scale to be able to optimize their customer relationships. 0:32:9.170 –> 0:32:12.270 Nathan Lasnoski So Brian, you’ve been working in this space for a little while. 0:32:12.420 –> 0:32:17.580 Nathan Lasnoski What has been your experience with customer call centers, call analysis optimization, things like that? 0:32:18.570 –> 0:32:29.360 Brian Haydin Yeah, my experience is that the the the AI tooling that’s available in a lot of these platforms is, is is OK. 0:32:29.370 –> 0:32:31.540 Brian Haydin I mean, it does a relatively decent job. 0:32:31.550 –> 0:32:55.730 Brian Haydin I mean, you can get sent them at the analysis out of it, but what it doesn’t understand necessarily is is your business and so being able to ground, you know, some of its interpretation of what that call outcome was with information that’s relevant around your business is actually, uh, actually increases the value that you get out of some of these these tools. 0:32:56.20 –> 0:33:8.470 Brian Haydin So, so yeah, there’s a gap yet where a lot of the out of the box tooling might get you like a simple dashboard, but it’s not really gonna get you valuable and impactful information out of it. 0:33:8.930 –> 0:33:9.460 Nathan Lasnoski Mm-hmm. 0:33:10.10 –> 0:33:10.600 Nathan Lasnoski Exactly. 0:33:10.610 –> 0:33:10.980 Nathan Lasnoski Thank you. 0:33:13.530 –> 0:33:13.940 Nathan Lasnoski OK. 0:33:13.950 –> 0:33:18.970 Nathan Lasnoski Another example that we’ve been seeing value in is wealth management teams. 0:33:18.980 –> 0:33:23.60 Nathan Lasnoski In this kind of leads us into a combined set wealth management. 0:33:23.150 –> 0:33:26.940 Nathan Lasnoski Next best action, parent child relationships. 0:33:26.950 –> 0:33:54.980 Nathan Lasnoski All of this fits in the same zone of optimizing my customer relationships, leading to increased retention leading to increased monetization of my customer, bringing them to a better place, helping them to make good decisions, keeping them in the family of my financial relationship, whether that’s a insurance relationship you have or it’s a they’re invent your investment relationship or maybe in combination of both that relationship and understanding how to manage it. 0:33:54.990 –> 0:34:0.340 Nathan Lasnoski So the things that we’re seeing people doing is a thinking about AI functions as another team member. 0:34:0.350 –> 0:34:16.320 Nathan Lasnoski How do I use AI as a delegated team member for research meeting prep portfolio analysis to get me ready for the conversations that I’m going to have and maybe take that and send that to my customer as well, like research this possibility for the customer return this data about My Portfolio. 0:34:16.570 –> 0:34:19.580 Nathan Lasnoski These are examples where it’s not just a copilot, right? 0:34:19.590 –> 0:34:23.740 Nathan Lasnoski This is I’m talking to a business repository that has this information. 0:34:23.890 –> 0:34:25.360 Nathan Lasnoski Writing it back into. 0:34:26.180 –> 0:34:26.580 Nathan Lasnoski Uh. 0:34:26.620 –> 0:34:30.310 Nathan Lasnoski Into prep data that I then used to be able to prepare for my customer. 0:34:30.640 –> 0:34:32.770 Nathan Lasnoski Tell me about the recent accidents for my customer. 0:34:32.780 –> 0:34:34.40 Nathan Lasnoski I’m preparing for a. 0:34:35.570 –> 0:34:40.80 Nathan Lasnoski Like every six months, my my insurance agent calls me right, like they need to retain me. 0:34:40.90 –> 0:34:44.300 Nathan Lasnoski They wanna keep me engaged, but it seems like, you know, they don’t always know exactly what’s happening. 0:34:44.350 –> 0:34:49.970 Nathan Lasnoski What if he did a better job prepping and had understood whether or not I was gonna be doing XY or Z? 0:34:50.430 –> 0:34:59.640 Nathan Lasnoski What changes should I make to my customers portfolio per scribing changes that I should be making as an advisor inform my team members about the schedule for the week preparing for my week? 0:34:59.650 –> 0:35:3.320 Nathan Lasnoski The best advisors are always preparing their week to be effective. 0:35:3.370 –> 0:35:9.530 Nathan Lasnoski So how do I force multiply all my team members to be able to be more effective with my customers to increase retention? 0:35:9.540 –> 0:35:15.60 Nathan Lasnoski That’s really much of the game associated with wealth management teams, and we’re working as a team. 0:35:15.70 –> 0:35:18.950 Nathan Lasnoski We need to work as a team together and then also use AI as an agent within that team. 0:35:20.70 –> 0:35:27.360 Nathan Lasnoski I’m also interpretation information, so a question that I asked was what are non obvious conclusions from this image? 0:35:27.370 –> 0:35:29.140 Nathan Lasnoski You can see that this is from monarch. 0:35:29.300 –> 0:35:33.950 Nathan Lasnoski It’s providing a little bit of a breakdown of the cache flow of a person. 0:35:33.960 –> 0:35:50.130 Nathan Lasnoski This is not mine and then a breakdown of how people are spending their money asking the question of what are non, what’s a non obvious conclusion did a pretty effective job of indicating what are things that are not obvious or at least maybe they’re statements. 0:35:50.140 –> 0:36:4.970 Nathan Lasnoski Maybe you might think they’re obvious, but one of the statements coming out of this particular particular person, what’s interesting about this is once an AI agent is able to interpret something like this, it can then be used as data across the entire portfolio. 0:36:5.100 –> 0:36:9.650 Nathan Lasnoski And it can also because I can ask questions of the data from the visual sense. 0:36:9.660 –> 0:36:11.250 Nathan Lasnoski Just the nature of this, right? 0:36:11.260 –> 0:36:14.850 Nathan Lasnoski Like large language models, they used to just be able to ask the questions in the text. 0:36:15.160 –> 0:36:23.110 Nathan Lasnoski Notice what I’m doing is I’m asking questions of an image that’s a whole different game, so I’m taking the image. 0:36:23.160 –> 0:36:30.710 Nathan Lasnoski I’m asking copilot to surface using the image interpretation what’s happening in the image. 0:36:30.720 –> 0:36:35.410 Nathan Lasnoski You can see it found, for example, the savings rate in comparison to the average. 0:36:35.420 –> 0:36:38.340 Nathan Lasnoski That’s giving me a perspective to provide back to my customer. 0:36:38.580 –> 0:36:44.520 Nathan Lasnoski I didn’t have to as a person go and analyze this myself to get this prep for my meeting with my customer. 0:36:44.760 –> 0:37:6.450 Nathan Lasnoski I have this already ready for me to be able to provide positive and negative comments back to them if I don’t have time to prep such a significant efficiency in terms of being able to provide information back or could be something that you build right into a robo and experience for the customer to say you’re using my app, here are some feedback that I can give to you directly from us understanding what’s happening, even just from an image. 0:37:8.290 –> 0:37:21.720 Nathan Lasnoski So this also relates into something called next best action, which is essentially the idea of guiding my customer or guiding my reps to be able to make effective console to my customers. 0:37:21.850 –> 0:37:26.860 Nathan Lasnoski So my customers in a certain position I have activities that have been happening with them. 0:37:26.950 –> 0:37:27.900 Nathan Lasnoski I have chats. 0:37:27.910 –> 0:37:29.540 Nathan Lasnoski I have recordings of calls. 0:37:29.690 –> 0:37:31.340 Nathan Lasnoski I’ve translated transcripts. 0:37:31.590 –> 0:37:42.200 Nathan Lasnoski Think about it as like every call with them could be recorded and put into a digital asset that I could use to do next best action like so incredible things are possible digital events that happen to them. 0:37:42.210 –> 0:37:43.360 Nathan Lasnoski What’s their demographics? 0:37:43.370 –> 0:37:53.680 Nathan Lasnoski Some that the demographic thing we have to be very careful about in terms of, umm, the bias activity that could fall into that and then we need some things we have to exclude from that to even use it. 0:37:54.220 –> 0:38:19.970 Nathan Lasnoski Moving that into behaviors that then drive a specific action and this is what we’re finding many of our customers are asking us for is how do I not just provide information like I did in the previous step, but especially provide what you should do, what is the task that the person should take based upon what we know about them that then leads us into we let them go or we that keep them on or we do a phone call or we do a promotion about something. 0:38:19.980 –> 0:38:27.660 Nathan Lasnoski We know that they’re interested in based upon what I know, delta like think about in a sense, think about the digital trail you leave right? 0:38:27.790 –> 0:38:28.520 Nathan Lasnoski Facebook. 0:38:28.580 –> 0:38:30.540 Nathan Lasnoski LinkedIn calls you have with them. 0:38:30.650 –> 0:38:32.820 Nathan Lasnoski I can theoretically know everything about you. 0:38:32.890 –> 0:38:56.190 Nathan Lasnoski How do I use that information to be able to optimize how I bring back excellent suggestions of how you improve your Financial position and use that as part of a marketing communication back understanding that we have to have very careful lines and guardrails around what we can do and can’t do, but we also have an ability for us to be able to leverage some of this information to be able to drive the best relationship possible. 0:38:57.200 –> 0:38:58.810 Nathan Lasnoski So if we do what? 0:38:58.820 –> 0:39:16.830 Nathan Lasnoski The data shows is that if we do this well we have better lifetime value from our customers, better engagement, we have better Net Promoter score, meaning they stick with us and we have better conversion of not only just taking one service from us but also our JSON services that they’re gonna receive great services from. 0:39:17.810 –> 0:39:19.900 Nathan Lasnoski If we don’t do it, we have more complaints. 0:39:19.910 –> 0:39:31.240 Nathan Lasnoski We know less about them, so they just leave because we’re not getting value and then we have higher cost because we’re always having to get new customers and bring on new reps because of existing reps aren’t successful. 0:39:31.370 –> 0:39:33.780 Nathan Lasnoski So we need to get a new Rep that has a new book of business. 0:39:33.790 –> 0:39:34.820 Nathan Lasnoski They have them go after it. 0:39:34.830 –> 0:39:49.800 Nathan Lasnoski Like how can we build stronger relationships where they want to stick with us and it’s not built on the old school model of like, I just worked with this guy and he just talks to me every couple of months and just kind of throws out an opinion like the newest generation wants data driven. 0:39:49.810 –> 0:39:50.780 Nathan Lasnoski They want ease of use. 0:39:50.790 –> 0:39:56.490 Nathan Lasnoski They want something at the tips of their fingers bringing this in, but next best action drives more benefits. 0:39:56.500 –> 0:39:59.180 Nathan Lasnoski So this is where we’re seeing a lot of these projects going down. 0:39:59.190 –> 0:40:4.840 Nathan Lasnoski The directional, which then also relates into this idea of multi generational relationships. 0:40:5.10 –> 0:40:16.180 Nathan Lasnoski One of the most important things that companies are wrestling with right now is I may have a relationship with the parent based upon a buyer that I have and they’ve been with us for a long time. 0:40:16.190 –> 0:40:34.310 Nathan Lasnoski They have this relationship with an old school old school Rep, but then I have all these children that are going to inherit or I have new high performing jobs or in their 40s they they’re doing great stuff and maybe they’ll have they have children of their own and they’re using a robo platform. 0:40:34.320 –> 0:40:41.30 Nathan Lasnoski They’re doing all their stuff on on Robin Hood and on monarch, and they’re planning their own stuff. 0:40:41.40 –> 0:40:48.370 Nathan Lasnoski And you’re like, dude, I’m gonna lose this whole relationship over time because they’re not interested in working with me, even if they are high value. 0:40:48.600 –> 0:40:54.480 Nathan Lasnoski How do I create a relationship with them that drives that connection and shows that? 0:40:54.490 –> 0:40:55.960 Nathan Lasnoski I’m like with it right? 0:40:55.970 –> 0:41:1.880 Nathan Lasnoski Like I’m creating the relationship that creates value for them above and beyond what they can do on their own. 0:41:1.890 –> 0:41:3.500 Nathan Lasnoski Or maybe in conjunction with that. 0:41:3.610 –> 0:41:15.160 Nathan Lasnoski So understanding the whole family, AI powered drip campaigns back to the entire family relationship, engaging them as a family, not just as an individual auto generation of documents that they need and should see. 0:41:15.210 –> 0:41:25.50 Nathan Lasnoski Combine that with the next best action and companies are starting to see like a I can power this whole ecosystem of relationships that I have to create more value. 0:41:25.360 –> 0:41:27.350 Nathan Lasnoski So this is I think this is like the heart of it. 0:41:27.360 –> 0:41:35.390 Nathan Lasnoski If you take these couple like wealth management teams, next best action and multi generation relationships, you kind of have it in a nutshell, right? 0:41:35.400 –> 0:41:42.290 Nathan Lasnoski Like, how do I retain, maximize their capabilities and value that they get from me and drive more capabilities from that? 0:41:42.300 –> 0:41:49.800 Nathan Lasnoski Like, how do I ensure that I have the best relationship possible so that kind of leads us into this idea of risk management? 0:41:50.90 –> 0:41:58.290 Nathan Lasnoski This idea of one of the things I always have to think about is I have to step back from My Portfolio and think about risk in a context of a lot of different steps. 0:41:58.300 –> 0:42:0.320 Nathan Lasnoski And again, this is a space where bias can enter in. 0:42:0.390 –> 0:42:4.40 Nathan Lasnoski We have to be very aware of data we’re using and not using risk management. 0:42:4.50 –> 0:42:17.80 Nathan Lasnoski Lets us think about it as a bigger picture and not just sitting at ad hoc and reactive, but one of the things that might have seen earlier was like you’re predicting what’s gonna happen, you’re prescribing what you should do about it. 0:42:17.580 –> 0:42:21.50 Nathan Lasnoski And then if you’re really getting into a good space, you’re storytelling. 0:42:21.60 –> 0:42:28.430 Nathan Lasnoski What could happen if you make certain choices, and that’s what enables you to make really intelligent forward moves? 0:42:28.540 –> 0:42:35.90 Nathan Lasnoski Is your storytelling and you’re making intelligent choices based on the possibilities of those stories occurring within a person’s life. 0:42:35.140 –> 0:42:54.390 Nathan Lasnoski Like we know that this person is going to do this and this and this based upon the step the same way that I know that like if I leave some cookies out my 2 year olds gonna climb up on a stool and you know, like I know that this person is gonna move from the starter house to this to this to this based upon these life choices that are making like I can optimize My Portfolio with them based upon that data. 0:42:54.800 –> 0:43:0.850 Nathan Lasnoski There’s ways for me to drive better relationships based upon the managing the risk of their situations and knowing more about them. 0:43:0.920 –> 0:43:6.700 Nathan Lasnoski And then knowing what the risk of my entire portfolio, my organization’s act and AI can provide a ton of value in that space. 0:43:8.920 –> 0:43:15.20 Nathan Lasnoski Umm, another use case here that I think everybody should be aware of is just like anything that happens a lot in your business. 0:43:15.30 –> 0:43:17.30 Nathan Lasnoski There’s a great opportunity for for AI. 0:43:17.40 –> 0:43:20.230 Nathan Lasnoski For claim adjudication is one of those examples. 0:43:20.320 –> 0:43:21.590 Nathan Lasnoski In the insurance zone. 0:43:21.880 –> 0:43:37.950 Nathan Lasnoski So in this case you can see request for payment on Procedure request for payment on Procedure request for payment on procedure wait a minute, all those procedures are actually something called a root canal and maybe that’s actually just a single code and some claim adjudication is really good at that. 0:43:38.160 –> 0:43:55.840 Nathan Lasnoski Like, hey, you’re trying to scam the system because you submitted this stuff, but AI takes that up a step by saying it’s not just the if the and see it, then we actually can tell if that all fits into the same zone, allowing us to then easily go through the process of matching certain codes not matching others, approval rejection or human review. 0:43:55.850 –> 0:43:56.200 Nathan Lasnoski Right. 0:43:56.320 –> 0:43:57.400 Nathan Lasnoski Automatic approval. 0:43:57.410 –> 0:43:57.640 Nathan Lasnoski Cool. 0:43:57.650 –> 0:43:59.730 Nathan Lasnoski You fit into the zone rejection. 0:43:59.770 –> 0:44:0.600 Nathan Lasnoski No way, Jose. 0:44:0.610 –> 0:44:2.230 Nathan Lasnoski Like, that’s not gonna fit that. 0:44:2.240 –> 0:44:4.520 Nathan Lasnoski You’re obviously fitting into this other zone. 0:44:4.590 –> 0:44:5.300 Nathan Lasnoski It’s just straight up. 0:44:5.310 –> 0:44:5.820 Nathan Lasnoski Doesn’t match. 0:44:5.830 –> 0:44:6.700 Nathan Lasnoski It’s the wrong thing. 0:44:6.770 –> 0:44:13.820 Nathan Lasnoski Or maybe in this case we send it down the human review space, we say, alright like this doesn’t look like it’s fitting in the right spot. 0:44:13.830 –> 0:44:23.30 Nathan Lasnoski We’re gonna give it a human review before it gets submitted, and that’s gonna kind of your validation step if 80% of these could flow through the top zone without us having to do anything. 0:44:23.340 –> 0:44:32.460 Nathan Lasnoski And then the rest of them actually have to go into human review, puts us in a totally different position than we were before, for potentially millions of requests that are coming across our process. 0:44:34.640 –> 0:44:39.30 Nathan Lasnoski Alright, so this is a really cool example that I think is killer. 0:44:39.40 –> 0:44:43.90 Nathan Lasnoski In the video, the insurance space this video isn’t live. 0:44:43.100 –> 0:44:47.290 Nathan Lasnoski It’s something I wish was live, but essentially what happens is skywalks on his car, right? 0:44:47.300 –> 0:44:50.90 Nathan Lasnoski And in this video, the guys car is busted up, right? 0:44:50.100 –> 0:44:54.930 Nathan Lasnoski He’s got scuffs and got damage and stuff, and oftentimes you’d have an insurance adjuster to look at. 0:44:54.940 –> 0:44:56.150 Nathan Lasnoski That or they’d validate. 0:44:56.200 –> 0:45:8.310 Nathan Lasnoski It’s correct or whatever and what this does is it takes that camera footage and it summarizes all that into a description of the car damage. 0:45:8.460 –> 0:45:18.200 Nathan Lasnoski So you can see in that Section 2 the rear side of the blue Toyota Camry has sustained significant damage characterized by deep scratches and scuff marks, etc. 0:45:18.320 –> 0:45:43.140 Nathan Lasnoski Eric Cetera, et cetera, this is an accurate depiction, just trust me, it’s accurate depiction of the video that the person walks around the car with that allows us to optimize the entire insurance process associated with getting the claims, submitting it, what it should cost to repair, sending that off to potential providers, not even having to have a person physically go to the provider, actually just delivering the video in the description. 0:45:43.370 –> 0:45:45.120 Nathan Lasnoski There’s a lot of optimization that can happen. 0:45:45.130 –> 0:45:55.250 Nathan Lasnoski Here is another example of like a disruptive space that’s changing the way that we can speed up the process of getting paid and fixing my the thing that went wrong in my life. 0:45:55.260 –> 0:46:4.950 Nathan Lasnoski Like something bad happens, customer service are in the case of insurance, how can I make that process is seamless and easy for my customer as possible? 0:46:5.160 –> 0:46:7.640 Nathan Lasnoski This is another example. Umm. 0:46:9.790 –> 0:46:15.360 Nathan Lasnoski OK, so last thing here we got on, uh, an example of pulling data. 0:46:15.630 –> 0:46:18.480 Nathan Lasnoski One of the things I showed earlier was like running a process right? 0:46:18.490 –> 0:46:26.800 Nathan Lasnoski Like extracting text, running through layout, moving that into something you can see what we’re doing here is we’re pulling data, we’re turning that into something else. 0:46:26.810 –> 0:46:32.400 Nathan Lasnoski And then in this case, we’re actually turning that into JSON that then can be used for another purpose. 0:46:32.510 –> 0:46:40.490 Nathan Lasnoski So sometimes even the things you’re using for AI are just like middleware in a process that you’re building, but things you couldn’t do before with RPA. 0:46:40.870 –> 0:46:43.120 Nathan Lasnoski RPA has a lot of plays inside the financial space. 0:46:43.980 –> 0:46:45.890 Nathan Lasnoski Yeah, many people went down that road. 0:46:45.900 –> 0:46:53.280 Nathan Lasnoski They just found that like they couldn’t get the impact they wanted because it wasn’t capable enough of doing the kind of job you wanted us to do. 0:46:53.410 –> 0:46:57.610 Nathan Lasnoski Now AI kind of brings that interpretability generalizability up and notch. 0:46:57.720 –> 0:47:1.10 Nathan Lasnoski That allows you to use it in this space as well. 0:47:1.60 –> 0:47:3.580 Nathan Lasnoski So anything that you just need to do on a recurring basis? 0:47:5.660 –> 0:47:6.810 Nathan Lasnoski I’ve included this one. 0:47:6.860 –> 0:47:7.680 Nathan Lasnoski I think it’s important. 0:47:9.600 –> 0:47:15.390 Nathan Lasnoski Not because, like financial industry necessary necessarily is directly tied to development efficiency. 0:47:15.480 –> 0:47:18.220 Nathan Lasnoski But I think this will be helpful for Brian to talk a little bit about. 0:47:18.230 –> 0:47:22.600 Nathan Lasnoski I’ll just kind of prep it and then Brian, you can give your statement her thoughts about it. 0:47:22.710 –> 0:47:37.700 Nathan Lasnoski Umm, so one of the more early like generalizable capabilities that was released before anything else in copilot was GitHub copilot and one of the reasons why this was so important is because it’s kind of like uh, like backed. 0:47:37.710 –> 0:47:38.380 Nathan Lasnoski It’s a prep. 0:47:38.390 –> 0:47:47.400 Nathan Lasnoski Before we ran into it and like the other spaces, like what was the result of using GitHub copilot for a developer that needed to produce something valuable? 0:47:47.630 –> 0:47:54.320 Nathan Lasnoski And what we found was that developers with Intellisense are faster and better developers using chat. 0:47:54.330 –> 0:47:58.440 Nathan Lasnoski GPT were faster than that, but there’s a problem because that could be exposed. 0:47:58.450 –> 0:48:13.120 Nathan Lasnoski And so on, developer with copilot and copilot chat naturally force multiplied the kinds of activities they were able to get done, and even more so in the future where a developer, an AI developer, essentially is like working with an offshore like you’re. 0:48:13.130 –> 0:48:14.590 Nathan Lasnoski Here’s do this for me. 0:48:14.600 –> 0:48:16.130 Nathan Lasnoski Bring it back to me when it’s done. 0:48:16.140 –> 0:48:17.360 Nathan Lasnoski It’s not there yet. 0:48:17.400 –> 0:48:33.800 Nathan Lasnoski It’s definitely not there yet, but it will get to that point and that’s where things like the results from actually the study that across these 95 people split into halves 50 didn’t use 45 use the 45 that used it completed the task in an hour and 11 minutes. 0:48:34.390 –> 0:48:37.360 Nathan Lasnoski The ones that did not, it was two hours and 41 minutes. 0:48:37.370 –> 0:48:47.580 Nathan Lasnoski Now there’s a lot of examples that kind of backed this up, but I think this is a really great sort of AB that helps you see that once people start using this in a really concrete way, it leads to benefits. 0:48:47.690 –> 0:48:51.300 Nathan Lasnoski Brian, I mean, you’ve certainly been a copilot user for some time. 0:48:51.310 –> 0:48:55.120 Nathan Lasnoski You want to kind of talk a little bit about your experiences in the development space using these tools. 0:48:56.260 –> 0:48:57.80 Brian Haydin Yeah. 0:48:57.220 –> 0:49:6.640 Brian Haydin So six or seven years ago I was using jet brains, you know, pretty, you know, pretty regularly to just make my life easier as a developer. 0:49:7.40 –> 0:49:20.230 Brian Haydin And I was just this tool that I I had in my in my tool belt that I couldn’t live without, and if I went to a new assignment or a new engagement, I would bring it along with me just because I just needed it. 0:49:20.440 –> 0:49:22.590 Brian Haydin And it started to get commoditized. 0:49:22.880 –> 0:49:42.930 Brian Haydin Visual Studio started to like build in some of those features and I kind of maybe gotten away from it a little bit, but I always still like look back at, you know, this was a tool that I absolutely needed and now moving forward with, like copilot, uh, you know it it first it was, you know, being able to, you know, go to chat, GPT and ask some questions and get some sample codes. 0:49:43.240 –> 0:49:52.490 Brian Haydin Now I get this integrated into my environment and it’s really elevated that ability to leverage these tools in a meaningful way. 0:49:52.540 –> 0:50:6.420 Brian Haydin So to me, it’s the jet brains of 2010 or I guess if you want to look back that long, but it’s invaluable for development teams, great use cases. 0:50:6.990 –> 0:50:8.260 Brian Haydin Uh would be. 0:50:8.270 –> 0:50:13.860 Brian Haydin I’ve got a new new developer that’s going to be jumping into help out with something that they’re unfamiliar with. 0:50:14.310 –> 0:50:19.660 Brian Haydin Maybe it’s a toolkit that they’re unfamiliar with, or maybe it’s you can just jumping into some code they haven’t. 0:50:19.680 –> 0:50:23.960 Brian Haydin They haven’t worked in before being able to use the copilot chat features. 0:50:23.970 –> 0:50:25.410 Brian Haydin Just what does this file do? 0:50:25.420 –> 0:50:26.380 Brian Haydin What does this class do? 0:50:26.390 –> 0:50:30.800 Brian Haydin What does this thing you know is is invaluable and gets them up to speed. 0:50:30.810 –> 0:50:45.490 Brian Haydin You know very quickly another colleague of mine talked about like, you know, doing some unity development and that’s something that’s got a very, very high, you know learning curve takes you know typically a couple months to get yourself ramped up. 0:50:45.600 –> 0:50:49.560 Brian Haydin But with, you know, things like copilot like you can be effective using these. 0:50:49.570 –> 0:50:52.970 Brian Haydin You know, really advanced toolkits very quickly. 0:50:53.740 –> 0:51:8.300 Brian Haydin And then I guess the last thing I would say is we’re just starting now to see the benefits of GitHub copilot enterprise, which is, you know, bringing in features that help ground to your organizations, repos and code bases. 0:51:8.870 –> 0:51:12.180 Brian Haydin So, uh, you know, it’s exciting to see that feature come along. 0:51:12.550 –> 0:51:15.950 Brian Haydin I can’t wait for, you know, some of our customers and ourselves to start using it. 0:51:17.130 –> 0:51:17.450 Nathan Lasnoski Awesome. 0:51:17.460 –> 0:51:18.60 Nathan Lasnoski Thanks, Brian. 0:51:18.590 –> 0:51:18.890 Nathan Lasnoski OK. 0:51:18.900 –> 0:51:20.600 Nathan Lasnoski And our last uh five minutes. 0:51:20.610 –> 0:51:22.240 Nathan Lasnoski And then we’ll have a little bit of time for questions. 0:51:22.250 –> 0:51:30.290 Nathan Lasnoski I’m going to talk a little bit about what this means to us as people, and I think that’s just an important closing area for us to think about and how we prepare. 0:51:31.260 –> 0:51:35.510 Nathan Lasnoski So when we started this off, we talked about two lanes mission driven in commodity. 0:51:35.520 –> 0:51:43.470 Nathan Lasnoski Both of these start with executive alignment and drive forward into achieving outcomes, but they have to be partnered with Human First AI it has. 0:51:43.740 –> 0:51:52.790 Nathan Lasnoski We have to think about an outcome that is inclusive of human flourishing as a goal and not as a something we just kind of do as an extra thing. 0:51:52.800 –> 0:51:53.490 Nathan Lasnoski If we have time. 0:51:53.990 –> 0:52:0.50 Nathan Lasnoski So when you think about your people and your organization, think about their current job as having a high degree of repetitive work. 0:52:0.180 –> 0:52:1.550 Nathan Lasnoski They come to the work every day. 0:52:1.560 –> 0:52:4.580 Nathan Lasnoski They do this thing and this thing is how they think about themselves. 0:52:4.850 –> 0:52:15.180 Nathan Lasnoski And if I also sudden took away that thing, what I be able to function in the top quartile of human achievement by being always on creative? 0:52:15.290 –> 0:52:25.0 Nathan Lasnoski Or would I like not really know what to do with myself and sometimes the way you can test that is like if people have gaps in their schedule, are they listless or did they actually use that effectively? 0:52:25.90 –> 0:52:27.180 Nathan Lasnoski A lot of people just don’t know how to use it effectively. 0:52:27.190 –> 0:52:31.540 Nathan Lasnoski They’re going and watching YouTube videos because they don’t know what to do with their time. 0:52:31.810 –> 0:52:35.680 Nathan Lasnoski This is a transition we need to help people through, and it’s not going to necessarily be intuitive. 0:52:36.160 –> 0:52:43.690 Nathan Lasnoski It’s not necessarily intuitive to be able to delegate for a lot of people, so this is a very important shift that many organizations are going to go through. 0:52:44.580 –> 0:52:51.30 Nathan Lasnoski So we’ll see examples like financial advisors to ones that are prescriptive, AI driven, financial services. 0:52:51.160 –> 0:53:2.170 Nathan Lasnoski People need to be able to partner with that self quoting ask and answer business systems describe and draft for presentation prep, coding to autonomous development assistants, automating processes. 0:53:2.180 –> 0:53:8.850 Nathan Lasnoski These are all fit into these transitions of tasks that were performing that we’re not necessarily intuitive right now. 0:53:9.220 –> 0:53:12.210 Nathan Lasnoski So one of the things that you might ask is like, how do I get started? 0:53:12.400 –> 0:53:18.730 Nathan Lasnoski And the question everybody set will say it was everybody going to become a data scientist and learning data science is curriculum for every one of my employees. 0:53:18.740 –> 0:53:20.100 Nathan Lasnoski Like, no, of course not. 0:53:20.110 –> 0:53:26.580 Nathan Lasnoski Like that will impact less than .1% of your staff like or maybe not at all like you will. 0:53:26.850 –> 0:53:30.380 Nathan Lasnoski Your goal is gonna be about upskilling everyone around. 0:53:30.390 –> 0:53:35.140 Nathan Lasnoski Data science is sorry, upskilling is not about data science. 0:53:35.150 –> 0:53:35.780 Nathan Lasnoski It’s about. 0:53:35.850 –> 0:53:40.160 Nathan Lasnoski Everyone deserving an AI system, and it’s reawakening creativity. 0:53:40.170 –> 0:53:48.170 Nathan Lasnoski I think this is like one of the most important components of that and it being necessary to have a growth mindset in order to be able to make that happen. 0:53:48.390 –> 0:53:51.760 Nathan Lasnoski And is not about essentially adding new roles. 0:53:51.770 –> 0:54:2.470 Nathan Lasnoski The organization it’s about adding skills to existing functions that allow them to be able to be more so things that you will see in your organization as you start to gain ground. 0:54:2.480 –> 0:54:15.400 Nathan Lasnoski Here are all these technical functions like data engineering and data scientists and AI engineers that create that prepare the data, build trusted AI models, or use a foundational model to do something. 0:54:16.210 –> 0:54:20.850 Nathan Lasnoski But you may not even have any of those people, or you may be partnering with someone like us to do that. 0:54:20.980 –> 0:54:24.310 Nathan Lasnoski What should really need to build is this idea of an AI practitioner. 0:54:24.520 –> 0:54:29.910 Nathan Lasnoski People that are creating and enjoying value from AI, they’re experts on the business. 0:54:29.920 –> 0:54:35.250 Nathan Lasnoski They’re able to force multiply themselves by using these tools, and that’s not necessarily an intuitive thing. 0:54:35.660 –> 0:54:39.270 Nathan Lasnoski So when you think about AI skills, truly these are human skills. 0:54:39.280 –> 0:54:44.890 Nathan Lasnoski They’re growth mindset, experimentation, creative energy, delegation to AI agents thinking with the end in mind. 0:54:45.0 –> 0:54:47.700 Nathan Lasnoski Just the idea of thinking with the end in mind and saying this is what I need. 0:54:47.870 –> 0:54:49.250 Nathan Lasnoski May I go prepare that for me? 0:54:49.340 –> 0:54:53.790 Nathan Lasnoski Is not something most people know how to do, so this is a transition for your team. 0:54:54.0 –> 0:55:2.150 Nathan Lasnoski So when you think about your executives and how they need to be the starting point, it’s what does the mission of my organization look like through the lens of AI? 0:55:2.210 –> 0:55:10.190 Nathan Lasnoski How do I prepare my organization to be able to enter into new age people, Services, capabilities to be able to deliver more value? 0:55:10.200 –> 0:55:15.500 Nathan Lasnoski So on the examples today, there’s numerous ways the companies are already creating outcomes using AI. 0:55:15.760 –> 0:55:22.30 Nathan Lasnoski Are your organization needs to prepare for the same challenge your organization to enable AI for every employee? 0:55:22.140 –> 0:55:24.310 Nathan Lasnoski That’s not just like, hey, turn on copilot. 0:55:24.320 –> 0:55:31.560 Nathan Lasnoski Tomorrow it’s we need to prepare a plan that leads us down the path to gain outcomes that are tied to every person’s job. 0:55:32.640 –> 0:55:34.210 Nathan Lasnoski That means I have new skills. 0:55:34.340 –> 0:55:36.250 Nathan Lasnoski I’m challenging my employees to be more. 0:55:36.260 –> 0:55:40.110 Nathan Lasnoski I’m helping them to think about their job in a different way. 0:55:40.120 –> 0:55:48.270 Nathan Lasnoski Maybe they are doing completely new job tomorrow based upon what I’m able to do in an automated sense now and then I’m measuring that engagement. 0:55:48.280 –> 0:55:55.710 Nathan Lasnoski Both of AI impact across the company and across every employee, and that’s how I’m judging success back to the mission of the organization. 0:55:55.720 –> 0:55:58.410 Nathan Lasnoski Did I achieve my strategic plan and did this help? 0:55:58.680 –> 0:56:0.530 Nathan Lasnoski Like that’s got to be the net thing. 0:56:0.540 –> 0:56:1.930 Nathan Lasnoski You’re actually measuring this against. 0:56:2.610 –> 0:56:11.720 Nathan Lasnoski So ultimately, when we think about how you take next steps, we know that what you need to do is organize these core ideas to be able to put in front of your organization. 0:56:11.730 –> 0:56:15.780 Nathan Lasnoski How I take advantage of commodity AI and take advantage of mission driven AI? 0:56:15.990 –> 0:56:24.540 Nathan Lasnoski So what we want is suggest you do and as you close down today, we want you to take next steps we love for you to do an AI copilot executive envisioning workshop. 0:56:24.630 –> 0:56:25.720 Nathan Lasnoski We do that for free. 0:56:25.830 –> 0:56:31.160 Nathan Lasnoski We invest in you to come out and do these workshops with you to help you drive value in your Executive Team. 0:56:31.450 –> 0:56:33.680 Nathan Lasnoski Just today I have. 0:56:33.820 –> 0:56:36.570 Nathan Lasnoski I had a 6:00 AM Executive meeting with a company. 0:56:36.620 –> 0:56:46.170 Nathan Lasnoski I have a a Executive meeting about an hour and a half after this with the CEO, CFO, COO to drive true outcomes within these organizations. 0:56:46.300 –> 0:56:48.910 Nathan Lasnoski This is the kind of the way you get started. 0:56:48.920 –> 0:56:50.890 Nathan Lasnoski It’s not just an IT project. 0:56:50.900 –> 0:56:52.670 Nathan Lasnoski It is an organizational project. 0:56:53.0 –> 0:57:0.190 Nathan Lasnoski There is Microsoft funded use case validation that we engage in, and there’s also AI and copilot company. 0:57:0.200 –> 0:57:2.720 Nathan Lasnoski Wide envisioning sessions will do that, up to 100 people. 0:57:3.150 –> 0:57:15.780 Nathan Lasnoski We’ve done that in person and virtual to widen the tent and get people excited, so we love to do this with you as you close down, fill out our form regarding that, but also give us feedback like I just want to know what this was valuable to you. 0:57:15.790 –> 0:57:17.720 Nathan Lasnoski Like, did you leave this learning something? 0:57:17.810 –> 0:57:19.190 Nathan Lasnoski Did it provide you value? 0:57:19.250 –> 0:57:20.680 Nathan Lasnoski Please provide us some feedback. 0:57:20.690 –> 0:57:22.720 Nathan Lasnoski I just want to hear like, did you love it? 0:57:22.730 –> 0:57:23.300 Nathan Lasnoski Did you hate it? 0:57:23.310 –> 0:57:29.720 Nathan Lasnoski Is there something we could have done better that allows us to keep improving this as we go forward as we present this to other people? 0:57:29.910 –> 0:57:30.550 Nathan Lasnoski So thank you. 0:57:31.480 –> 0:57:36.630 Nathan Lasnoski And we can stick around if you have some questions, we would love for you to put those into the chat. 0:57:36.820 –> 0:57:40.700 Nathan Lasnoski And Brian and I will hit those as you have some questions. 0:57:54.370 –> 0:57:54.780 Nathan Lasnoski All right. 0:57:54.790 –> 0:57:55.700 Nathan Lasnoski Thank you everyone. 0:57:55.910 –> 0:57:59.910 Nathan Lasnoski Hope you have a great rest of your day and we will see you on the flip side. 0:58:2.100 –> 0:58:2.480 Brian Haydin Thank you.