1 00:00:00,438 --> 00:00:01,358 So I'm Don Ely. 2 00:00:01,358 --> 00:00:09,638 I'm the Director of Enterprise Services for Cirrus, and Enterprise Services encompasses executive strategy, workforce, supply chain, and operational excellence. 3 00:00:09,718 --> 00:00:14,278 And it's my pleasure to introduce Dave Mahusky, who is going to introduce our speaker. 4 00:00:14,598 --> 00:00:14,998 Yeah. 5 00:00:15,078 --> 00:00:22,278 So as we get started today, this afternoon, in one of the earlier sessions, I had a good, interesting talk, and it's on AI. 6 00:00:22,278 --> 00:00:24,358 So, but it was really interesting. 7 00:00:24,358 --> 00:00:28,678 There was a gentleman that he owns a cybersecurity firm, and he was wearing AI glasses. 8 00:00:29,078 --> 00:00:38,838 And I noticed it and I was like thinking to myself as I was looking at the room, kind of like looking at all you guys, I was like, my gosh, I bet in five to 10 years, could you imagine all of us wearing AI glasses? 9 00:00:39,318 --> 00:00:42,118 And I went and actually had a conversation with him after that session. 10 00:00:42,518 --> 00:00:47,318 And he said, yeah, you know what happened was I was actually recording the session with my AI glasses. 11 00:00:47,718 --> 00:00:51,878 And then as I was recording it in real time, it actually told me the questions I should ask the speaker. 12 00:00:52,278 --> 00:00:53,678 So it was like coming back on his glasses. 13 00:00:53,678 --> 00:00:54,918 That was just fantastic. 14 00:00:54,918 --> 00:00:57,318 So it's interesting how this technology is continually evolving. 15 00:00:57,318 --> 00:00:58,918 So I just want to make a note of that. 16 00:00:59,878 --> 00:01:01,718 I'm Dave Mahusky, like Don said. 17 00:01:01,718 --> 00:01:03,798 So co-founder of the Precision X System. 18 00:01:03,798 --> 00:01:05,878 We are the premier sponsor for this event this year. 19 00:01:06,278 --> 00:01:07,638 And super excited about this. 20 00:01:07,638 --> 00:01:09,638 We're the human-centered AI. 21 00:01:09,638 --> 00:01:15,078 We take human into equation when we implement AI for organizations. 22 00:01:15,078 --> 00:01:16,918 And so we're just excited for this. 23 00:01:17,238 --> 00:01:18,598 Thank you, Sirius, for putting this on. 24 00:01:18,598 --> 00:01:18,838 And 25 00:01:19,318 --> 00:01:24,358 So without further ado, our next session really moves from theory to reality. 26 00:01:24,758 --> 00:01:26,758 And we're joined by Kurt Winegar. 27 00:01:26,918 --> 00:01:33,638 He's a senior data analytics strategist at Pella Corporation, who leads Pella's data and AI literacy program. 28 00:01:33,998 --> 00:01:39,558 He played a central role in the company's Microsoft 365 Copilot rollout. 29 00:01:40,278 --> 00:01:44,998 With more than 25 years of experience solving business problems with data, 30 00:01:45,838 --> 00:01:52,918 Kurt actually helped shift Pella's AI strategy from trying new technology to building real capability. 31 00:01:53,478 --> 00:01:54,438 The result? 32 00:01:55,118 --> 00:02:10,278 A people-first approach to AI that has driven over 95% monthly active usage among licensed Copilot users, placing Pella among top-performing organizations according to Microsoft benchmarks. 33 00:02:11,318 --> 00:02:26,598 In this session, Kurt will walk through how AI is being used in everyday work to save time, improve decisions, and create measurable business value, showing what it really takes to move from pilot to impact. 34 00:02:26,998 --> 00:02:33,318 So on behalf of Cirrus and the PrecisionX System, please join me in welcoming Kurt Winegar. 35 00:02:37,758 --> 00:02:38,438 Thank you, Dave. 36 00:02:38,438 --> 00:02:38,758 Thank you. 37 00:02:38,838 --> 00:02:39,718 And thank you, Dawn. 38 00:02:41,958 --> 00:02:50,278 As we get started, I want you to think about AI not as a tool, not as a human problem to solve, but as a capability. 39 00:02:51,078 --> 00:02:52,678 How many of you are from out of town? 40 00:02:53,798 --> 00:02:54,358 Most of you. 41 00:02:54,358 --> 00:02:56,518 How many of you used GPS to get here? 42 00:02:57,318 --> 00:02:57,798 Okay. 43 00:02:58,278 --> 00:03:01,878 For those of you that use GPS, why did you use GPS to get here? 44 00:03:03,078 --> 00:03:03,878 Just call it out. 45 00:03:05,558 --> 00:03:06,518 Very easy. 46 00:03:07,318 --> 00:03:08,198 Any other reasons? 47 00:03:09,078 --> 00:03:09,718 Trust it. 48 00:03:10,278 --> 00:03:11,158 Why else? 49 00:03:12,438 --> 00:03:13,318 Reliable. 50 00:03:14,918 --> 00:03:17,958 Lack of knowledge, or it's got the knowledge I need, right? 51 00:03:18,838 --> 00:03:19,478 Any others? 52 00:03:19,478 --> 00:03:24,438 How many of you use it because it's going to save you time, right? 53 00:03:24,438 --> 00:03:29,558 If there's construction ahead, if there's an ambulance coming, if there's some other problem, right? 54 00:03:30,038 --> 00:03:35,878 Time is the one element that I've used throughout this entire experience in the last year of rolling out Copilot at Pella. 55 00:03:36,278 --> 00:03:38,118 It's the one resource we can't get back. 56 00:03:38,758 --> 00:03:40,118 I can do an awful lot of things. 57 00:03:40,118 --> 00:03:41,718 I've been at Pella almost 34 years. 58 00:03:41,958 --> 00:03:44,198 We have a lot of resources available. 59 00:03:44,598 --> 00:03:51,638 But the one thing I've never been able to figure out how to do is truly save time and shrink it dramatically. 60 00:03:51,798 --> 00:03:54,238 We've had all kinds of continuous improvement activities. 61 00:03:54,238 --> 00:03:57,878 We'll all go out to an assembly line and save a few minutes of cycle time. 62 00:03:58,038 --> 00:04:05,518 Or we'll save a process by going and hitting it with Kaizen events and taking that time and shrinking it and taking out the non-value add time, right? 63 00:04:05,518 --> 00:04:06,518 Taking out the waste. 64 00:04:07,838 --> 00:04:09,638 But with AI, you can go even faster. 65 00:04:10,358 --> 00:04:14,478 I believe it's not just a tool you plug into your system, but it's that capability that you learn to build. 66 00:04:14,478 --> 00:04:17,238 It's asking the question of, how else could I do this? 67 00:04:17,398 --> 00:04:21,638 What else could I do differently with this to change the way we deliver value to the business? 68 00:04:22,478 --> 00:04:29,198 And then when we use that capability, building muscle strength, it's compressing this time across decision cycles. 69 00:04:29,198 --> 00:04:33,478 It's from the time I've got, I've identified a problem till I can make a decision to solve it. 70 00:04:33,878 --> 00:04:40,678 or from the time I have an idea and I want to go to market with it, or it's problem solution challenges, right? 71 00:04:40,838 --> 00:04:51,558 From the time I've identified an issue till the actual root cause of that issue was identified, and using AI in that entire decision cycle, doing that will make a big difference to your customers. 72 00:04:52,918 --> 00:04:57,958 I build this on a foundation of data and AI literacy, and it started with a program I joined with the Data Lodge 73 00:04:58,398 --> 00:04:59,118 a couple of years ago. 74 00:04:59,118 --> 00:05:03,478 It was two years ago in May that I actually started a program with the Data Lodge, and it was fantastic. 75 00:05:03,558 --> 00:05:04,998 It taught me about data literacy. 76 00:05:05,158 --> 00:05:08,078 And there was an argument back then, and it's hard to believe that was just two years ago. 77 00:05:08,078 --> 00:05:14,118 There was an argument of, is it data literacy, or data and AI literacy, or is AI literacy separate? 78 00:05:14,598 --> 00:05:17,718 My answer was, yes, you are now your own data. 79 00:05:18,198 --> 00:05:23,078 That's the craziest thing I've ever witnessed, where you create your own data every time you talk to it. 80 00:05:23,318 --> 00:05:26,278 Every time you communicate with an AI, you're giving it context. 81 00:05:26,518 --> 00:05:35,158 If you're not thinking about the context you're applying to that AI and the data you give it, there's no reason it should ever hallucinate for you, except it still does. 82 00:05:35,158 --> 00:05:35,798 Well, why is that? 83 00:05:35,798 --> 00:05:42,118 Well, because the models were trained on a data set that might be outdated now, even though it's only six weeks old, right? 84 00:05:42,118 --> 00:05:44,518 It doesn't know the context of where we're at right now. 85 00:05:45,078 --> 00:05:46,598 So be mindful of those things. 86 00:05:46,758 --> 00:05:52,478 You've got to understand how it works so you can change the way people think, engage, and act with that AI. 87 00:05:52,478 --> 00:05:54,758 A little bit about myself. 88 00:05:54,798 --> 00:05:56,758 I'm a senior data analytics strategist. 89 00:05:56,918 --> 00:06:00,438 What that doesn't say is, Kurt's the copilot guy, right? 90 00:06:00,758 --> 00:06:02,358 I'm the guy that says, I have a problem. 91 00:06:02,358 --> 00:06:03,718 Is there data to solve it? 92 00:06:03,798 --> 00:06:13,318 How can I get you in front of software development, senior developers of AI, AI engineers, architects, my data science team? 93 00:06:14,038 --> 00:06:18,278 I wanted to turn those problems that people were faced with every day into a solution. 94 00:06:18,358 --> 00:06:19,718 I just wanted to solve problems. 95 00:06:19,718 --> 00:06:20,278 That was it. 96 00:06:20,358 --> 00:06:24,438 And I found AI to be a really neat way to do that in mass. 97 00:06:25,398 --> 00:06:30,278 On our data team, we've got a DataOps team that is enabling our data with AI. 98 00:06:30,518 --> 00:06:36,918 We're thinking of how do we make the data more AI-ready, using Graph, using Databricks, using other tools. 99 00:06:37,678 --> 00:06:43,078 And then we've got an AI and advanced analytics team that JC Hoyer, the keynote earlier, he's my direct manager. 100 00:06:43,318 --> 00:06:48,318 So JC and I constantly are challenged with enterprise level versus grassroots. 101 00:06:48,318 --> 00:06:54,758 How do I upskill the organization at the same time he's trying to get them to buy off on, okay, we want to go do a major initiative. 102 00:06:54,998 --> 00:06:56,198 This is going to take time and money. 103 00:06:56,198 --> 00:06:57,078 How do you do that? 104 00:07:01,318 --> 00:07:01,878 Look forward. 105 00:07:02,198 --> 00:07:02,598 There we go. 106 00:07:03,278 --> 00:07:07,078 For those of you that don't know Pella, we celebrated 100 years in business last year. 107 00:07:07,318 --> 00:07:14,198 We've got a little over 10,000 team members across this entire North America system, and we're an industry leader in residential windows and doors. 108 00:07:14,198 --> 00:07:15,158 We've tried commercial. 109 00:07:15,158 --> 00:07:16,438 Commercial's not so much fun. 110 00:07:16,838 --> 00:07:18,838 You know, they've got very custom things. 111 00:07:19,278 --> 00:07:21,478 for every architect in every market. 112 00:07:21,718 --> 00:07:23,718 So residential is where we live. 113 00:07:23,718 --> 00:07:24,278 We love it. 114 00:07:24,278 --> 00:07:26,918 It's been a fantastic part of our journey through Pella. 115 00:07:27,718 --> 00:07:30,758 I like Pella Windows because they're awesome to look through. 116 00:07:30,758 --> 00:07:37,318 I often tell people we compete with walls because like I'm standing in this room, I would love to have Pella Windows in here, right? 117 00:07:37,638 --> 00:07:39,958 The view out here, the view on campus, right? 118 00:07:40,118 --> 00:07:41,078 That's what we do. 119 00:07:41,158 --> 00:07:43,078 We compete with that process. 120 00:07:44,918 --> 00:07:46,438 So why AI, why now? 121 00:07:46,518 --> 00:07:52,118 It really goes down to how do you lead with that AI literacy foundation and building it with people first. 122 00:07:52,118 --> 00:07:56,918 I believe that the people at our business make the biggest difference in our business. 123 00:07:57,238 --> 00:08:00,758 It's why people come back all the time to buy products from us. 124 00:08:00,918 --> 00:08:03,078 It's why people are impressed with the community. 125 00:08:03,078 --> 00:08:06,358 If you haven't had plans for this upcoming weekend, head down to Pella. 126 00:08:06,518 --> 00:08:07,398 It's Tulip time. 127 00:08:07,638 --> 00:08:11,718 It's a great time to go get some food, enjoy some Dutch costumes, have some fun. 128 00:08:12,758 --> 00:08:13,878 It aligns with our values. 129 00:08:14,038 --> 00:08:15,398 How do I align this? 130 00:08:15,398 --> 00:08:21,078 Well, it's because training reflects our values of being curious and being role-based. 131 00:08:21,798 --> 00:08:26,358 And then our AI-first organization structure of how do I do this, aligning it with the vision? 132 00:08:26,838 --> 00:08:31,638 Well, if I don't teach the people how to use AI and use the right data to answer the right questions, 133 00:08:32,358 --> 00:08:33,798 I'm not really helping anybody. 134 00:08:33,798 --> 00:08:38,038 So I jumped in front of this last year saying, how can I do this differently? 135 00:08:38,038 --> 00:08:40,198 So we had a pilot program at Pella. 136 00:08:40,198 --> 00:08:43,878 How many of you have been through a pilot program that just ended in pilot purgatory? 137 00:08:44,518 --> 00:08:44,838 Okay. 138 00:08:45,558 --> 00:08:48,598 We didn't have that, fortunately, but we did have a challenge with the pilot. 139 00:08:49,318 --> 00:08:52,358 We launched the pilot and it grew bigger than the normal size. 140 00:08:52,358 --> 00:08:53,958 It should have been 30 to 40 people. 141 00:08:53,958 --> 00:08:55,878 Then it got to 50, then it got to 70. 142 00:08:55,878 --> 00:08:59,078 I was in the pilot group and I'm like, when are we getting licenses? 143 00:08:59,638 --> 00:09:02,118 because there's value in this tool and this technology. 144 00:09:03,238 --> 00:09:07,958 We scaled to 1000 licenses and everything changed. 145 00:09:08,038 --> 00:09:09,078 That was May 1st. 146 00:09:09,078 --> 00:09:10,598 It was a year ago this week. 147 00:09:10,998 --> 00:09:14,518 And I looked back on that year and I'm like, wow, what a difference a year has made. 148 00:09:15,238 --> 00:09:19,238 I jumped in front of it and said, I've learned how to use Copilot to save me a couple of hours every week. 149 00:09:19,558 --> 00:09:24,518 Let me just teach department by department, group by group, just like I would be doing in this room. 150 00:09:24,758 --> 00:09:28,118 Let me teach you as a small group, but like individuals. 151 00:09:28,518 --> 00:09:36,758 So if you've got this understanding of how to use AI, I can get you up to that, what that tech is, but it's really not a technology you need to know about. 152 00:09:36,998 --> 00:09:42,118 You need to know how to use it, how to build it with context, how to understand what it's capable and what it's not. 153 00:09:42,758 --> 00:09:44,998 And then leading with that AI literacy background. 154 00:09:45,398 --> 00:09:55,158 Again, passionate about solving problems, not necessarily because I wanted to teach people about AI, but I believed if I taught 1000 people how to use AI, man, what a world it would be in the following year. 155 00:09:55,238 --> 00:09:56,198 And it has been. 156 00:09:58,118 --> 00:09:59,798 So what is data and AI literacy? 157 00:10:00,758 --> 00:10:01,718 This is our definition. 158 00:10:01,718 --> 00:10:07,558 It's the ability to read, write, and communicate with data and AI in context, in both work and life. 159 00:10:07,878 --> 00:10:12,678 It's one of the most unique things about the data and AI literacy program that I went through with the Data Lodge. 160 00:10:12,678 --> 00:10:15,958 It was around not just a work skill. 161 00:10:15,958 --> 00:10:17,558 How many of you use Excel at home? 162 00:10:18,758 --> 00:10:19,238 Right? 163 00:10:19,238 --> 00:10:22,038 A few of you, of course, because we're data nerds, right? 164 00:10:22,918 --> 00:10:33,318 But when you really get down to what other things in the world do you do that you learned in your personal life or that can apply to work or vice versa as quickly, the only thing I can think of is the internet, right? 165 00:10:33,478 --> 00:10:37,638 Using those kind of technologies changed the way we do everything. 166 00:10:37,798 --> 00:10:38,918 AI is the same thing. 167 00:10:39,478 --> 00:10:42,598 Building the right mindset, getting the right language. 168 00:10:42,638 --> 00:10:49,238 When I first saw AI, and this is crazy, 2 1/2 years ago, the big talk was on prompt engineering. 169 00:10:49,238 --> 00:10:52,358 How many of you remember all the prompt engineering classes that everybody needed to take? 170 00:10:53,198 --> 00:10:57,718 How many of you just talk to your AI now and just do it in natural language, right? 171 00:10:58,038 --> 00:10:59,558 Prompt engineering was the thing. 172 00:10:59,558 --> 00:11:01,958 You had to figure out how to do it in so many words or less. 173 00:11:02,918 --> 00:11:03,798 And then the skills. 174 00:11:04,198 --> 00:11:06,358 When does it apply and when does it not? 175 00:11:07,158 --> 00:11:13,238 The challenge with most things is you want to upskill into that space, and yet not everybody knows how. 176 00:11:13,638 --> 00:11:14,998 They don't feel safe doing it. 177 00:11:15,478 --> 00:11:17,158 Selena did a great job of talking about that. 178 00:11:17,158 --> 00:11:17,758 That's the risk. 179 00:11:17,758 --> 00:11:21,238 It's the human side of the emotional attachment to the way I've always done it. 180 00:11:23,398 --> 00:11:25,558 So I'm going to offer you this idea. 181 00:11:25,958 --> 00:11:30,998 This is the slide that I believe changed the way in which we deliver AI literacy at Pella. 182 00:11:31,318 --> 00:11:32,878 And it's going to be so foundational. 183 00:11:32,878 --> 00:11:34,758 You're going to be like almost disappointed in me. 184 00:11:36,438 --> 00:11:38,838 I start with the question of how can I do this? 185 00:11:39,358 --> 00:11:43,318 And I evolve it to just expand to how can AI help me do this? 186 00:11:44,118 --> 00:11:50,678 The key being help me, not do it for me, not do it instead of me, not replace me, but help me. 187 00:11:51,478 --> 00:11:59,718 That simple thing, as silly as it sounds, on a post-it changed the way we rolled out AI at Pella. 188 00:12:00,358 --> 00:12:02,358 Now you might say, well, Kurt, come on, it's not that easy. 189 00:12:03,078 --> 00:12:04,678 For me, this is all it took. 190 00:12:04,998 --> 00:12:06,038 Honest to God's truth. 191 00:12:06,278 --> 00:12:10,918 Once I put this on my laptop and I said, okay, AI, help me write a better e-mail. 192 00:12:11,478 --> 00:12:12,518 Clear my calendar. 193 00:12:12,518 --> 00:12:14,278 Tell me what I should attend or what I shouldn't. 194 00:12:14,918 --> 00:12:19,878 Help me communicate up to the C-suite, down to the management levels. 195 00:12:20,278 --> 00:12:21,398 How do I do that? 196 00:12:22,438 --> 00:12:23,478 And do it for me? 197 00:12:23,558 --> 00:12:24,918 No, do it with me. 198 00:12:25,078 --> 00:12:26,838 Help me do it more effectively. 199 00:12:27,238 --> 00:12:29,638 Okay, now I've sent an e-mail to my C-suite. 200 00:12:29,638 --> 00:12:31,078 I've got the CFO in mind. 201 00:12:31,078 --> 00:12:32,838 I've sent a note to my CFO. 202 00:12:33,398 --> 00:12:34,758 How's he likely to receive it? 203 00:12:35,798 --> 00:12:37,558 What's his answer likely to be? 204 00:12:37,558 --> 00:12:38,678 What should I expect? 205 00:12:39,078 --> 00:12:40,758 If I were him, how would I respond? 206 00:12:41,478 --> 00:12:45,158 act like a CFO, have a bad day, tell me what I'm thinking, right? 207 00:12:45,158 --> 00:12:47,158 Like all of that became part of that journey. 208 00:12:47,598 --> 00:12:57,078 And by doing that, it also made me a better communicator, more willing to expose the vulnerabilities in the process, because at the end of the day, we're all human, vulnerable to this experience. 209 00:12:59,358 --> 00:13:01,278 And the other thing I did was I taught people this. 210 00:13:01,278 --> 00:13:03,038 was the first prompt I teach everyone. 211 00:13:03,038 --> 00:13:08,598 And I get a kick out of it because it follows the goal, context, expectations, and source that Copilot teaches. 212 00:13:08,878 --> 00:13:13,558 And it's a very similar prompt to what Copilot's training will teach you if you go through one of their online courses. 213 00:13:14,118 --> 00:13:15,558 It's write a thank you. 214 00:13:16,038 --> 00:13:17,238 That's the one thing. 215 00:13:17,238 --> 00:13:18,038 That's the goal. 216 00:13:18,678 --> 00:13:26,918 To Kurt in this presentation, in a tone that's friendly but sounds like a pirate, which is kind of funny and quirky, right? 217 00:13:27,038 --> 00:13:30,918 And then use this meeting that I did for the sales organization. 218 00:13:31,718 --> 00:13:34,918 That was my prompt, and it outputs in the pirate style. 219 00:13:35,478 --> 00:13:37,878 Kurt, you gave a fine talk at AI Impala, right? 220 00:13:37,878 --> 00:13:40,838 And it walks away with that pirate speak. 221 00:13:41,238 --> 00:13:43,158 Now, that's interesting, right? 222 00:13:43,238 --> 00:13:44,398 But why is that important? 223 00:13:44,918 --> 00:13:48,438 I think appreciation goes so far when you start talking to people. 224 00:13:49,158 --> 00:13:53,798 I like to go get birthday cards from my family, but I will admit I don't write them all by hand. 225 00:13:54,038 --> 00:13:56,358 I will go through the aisle and pick two or three that I like. 226 00:13:56,438 --> 00:13:57,158 So I'll tell people, 227 00:13:57,558 --> 00:14:01,798 Have Copilot write 3 to 4 different thank yous in different styles so you can pick the one you like. 228 00:14:02,198 --> 00:14:03,558 And then send thanks. 229 00:14:04,038 --> 00:14:04,518 Change it. 230 00:14:04,598 --> 00:14:04,998 Edit it. 231 00:14:04,998 --> 00:14:05,638 It's okay. 232 00:14:05,718 --> 00:14:09,398 Learn how to modify it to meet your needs and the way that you talk. 233 00:14:09,558 --> 00:14:17,558 And I teach people how to tailor it to listen to them and learn how to speak like them so that when it writes it, they're not editing very much. 234 00:14:17,878 --> 00:14:22,758 Most of the time they get up to about 98 or 99% accuracy and they rarely change words. 235 00:14:22,758 --> 00:14:24,278 It now sounds like them. 236 00:14:26,278 --> 00:14:29,318 The other thing I did was I showed them how you can make a graphic and make it fun. 237 00:14:29,478 --> 00:14:31,158 Not everything at work has to be hard. 238 00:14:31,638 --> 00:14:33,838 So you can have a creative component to it. 239 00:14:33,838 --> 00:14:41,198 And for people that were in the marketing department, they saw this and they were like, oh my gosh, we can now use these tools to build little icons. 240 00:14:41,198 --> 00:14:44,678 One of my plant managers took and said, okay, we've got a safety initiative. 241 00:14:44,998 --> 00:14:50,598 He used it to create icons that made it memorable for safety and put those on the walls in his plant. 242 00:14:50,678 --> 00:14:52,438 And I thought it was an awesome use case. 243 00:14:52,678 --> 00:14:56,518 because now he's got a tool that he can say, I want to display this. 244 00:14:56,518 --> 00:14:57,238 How do I do that? 245 00:14:57,238 --> 00:14:59,878 And I create an icon in that tool. 246 00:14:59,918 --> 00:15:04,278 And I don't have to get marketing involved or I don't have to get other people involved for a simple process. 247 00:15:07,318 --> 00:15:11,398 Another example I get, and I love this one, this is a longer prompt, but same concept. 248 00:15:11,958 --> 00:15:14,358 But look at way this prompt is done. 249 00:15:14,918 --> 00:15:20,358 If you haven't thought about your prompting skills, the pieces I highlighted are the key parts. 250 00:15:20,678 --> 00:15:23,758 But the biggest challenge most people have is, I don't know what to say to an AI. 251 00:15:23,758 --> 00:15:25,718 I wouldn't even know how to use it in my job. 252 00:15:26,598 --> 00:15:27,078 You know what? 253 00:15:27,078 --> 00:15:33,718 The easiest answer is, let AI interview you and ask you as an expert in deploying AI. 254 00:15:34,118 --> 00:15:38,678 Ask me three to five questions, not the end of the world, one question at a time. 255 00:15:39,078 --> 00:15:43,558 Slow down and answer it like it's a one-on-one conversation with your chatbot. 256 00:15:44,238 --> 00:15:48,358 And then make two obvious and two non-obvious recommendations. 257 00:15:48,678 --> 00:15:53,878 The obvious ones you'll know, you'll be like, oh yeah, of course I could use that to save time when I'm writing emails or organizing my week. 258 00:15:54,358 --> 00:16:00,118 But the non-obvious ones will be like, and you'd be more effective if you said it clearly the first time, right? 259 00:16:00,118 --> 00:16:02,118 You didn't have to have follow-ups to that meeting. 260 00:16:02,118 --> 00:16:03,718 Maybe you should set a better agenda. 261 00:16:04,038 --> 00:16:06,278 Oh, well, yeah, Right? 262 00:16:06,278 --> 00:16:08,958 Those non-obvious ones will come back and haunt you at times. 263 00:16:08,958 --> 00:16:11,238 You'll be like, well, of course, I should know how to do this. 264 00:16:12,518 --> 00:16:15,878 Doing this prompt pushes people beyond the scope of, 265 00:16:16,518 --> 00:16:18,838 Gosh Kurt, I don't know how I'd use it, but I'd love to talk with you. 266 00:16:18,918 --> 00:16:20,678 I've forced them down this path now. 267 00:16:21,078 --> 00:16:23,958 You will use this first and then come back to me after you've done that. 268 00:16:23,958 --> 00:16:25,478 None of them have ever come back. 269 00:16:25,878 --> 00:16:28,838 They've all used this prompt and you're like, did they use it? 270 00:16:29,518 --> 00:16:31,238 Yeah, absolutely they used it. 271 00:16:31,398 --> 00:16:35,158 Because they found that interaction changed the way that they thought about working. 272 00:16:39,198 --> 00:16:41,878 What's key about this is the AI literacy component. 273 00:16:42,358 --> 00:16:45,158 It's communicating with that in context. 274 00:16:45,398 --> 00:16:52,878 If your chat doesn't have enough context, if your AI does not have enough context, it will not do the work the way you expect it to. 275 00:16:52,878 --> 00:16:59,878 If you're getting a lot of hallucinations or you're disappointed in the results, make sure it's not because you couldn't answer the next question and get it more context. 276 00:16:59,878 --> 00:17:04,558 How many of you have heard of the change management principle of ADKAR? 277 00:17:05,398 --> 00:17:12,438 I was blessed to be a part of something the Data Lodge did, and they had a session and somebody came in and talked about this, and I'm like, this is perfect timing. 278 00:17:12,758 --> 00:17:16,358 I want to roll out AI and data literacy training. 279 00:17:16,518 --> 00:17:18,598 How can I do this awareness? 280 00:17:18,598 --> 00:17:20,518 How do I start dripping on an awareness? 281 00:17:20,518 --> 00:17:24,598 Not like, hey, it's coming May 1st, but how can I start a monthly newsletter? 282 00:17:24,598 --> 00:17:27,638 How can I start a weekly drip of knowledge? 283 00:17:28,678 --> 00:17:34,118 I created an awareness campaign, literally as simple as in six months, we're going to continue to drip things on you. 284 00:17:34,118 --> 00:17:38,358 You're going to want to come because you're going to hear it every week and you won't even realize that you want to know more. 285 00:17:39,798 --> 00:17:41,958 Desire, fear of missing out. 286 00:17:42,118 --> 00:17:45,078 As we started to roll Copilot out, we did it department by department. 287 00:17:45,398 --> 00:17:47,798 I asked Copilot, I only have a couple hours a week. 288 00:17:48,198 --> 00:17:53,398 Help me structure this program so that I can do this because this is not my full-time job. 289 00:17:53,878 --> 00:17:56,278 How do I deploy Copilot across the enterprise? 290 00:17:56,438 --> 00:17:58,838 This company deserves to have this rolled out. 291 00:17:58,838 --> 00:18:00,118 We've got 1000 licenses. 292 00:18:00,438 --> 00:18:01,318 Let's get started. 293 00:18:01,878 --> 00:18:03,078 Here's the plan I've got. 294 00:18:03,398 --> 00:18:04,598 Make it more effective. 295 00:18:04,598 --> 00:18:07,158 Help me get in front of those people and set the communication standard. 296 00:18:07,318 --> 00:18:14,438 I would use Copilot to help set the emails out to those directors, get their teams together, and I'd start setting up one or two trainings every week. 297 00:18:14,918 --> 00:18:18,198 And it was a great way to start building that and building the awareness and the desire. 298 00:18:18,438 --> 00:18:19,158 Then the knowledge 299 00:18:19,278 --> 00:18:20,358 was the workshop, right? 300 00:18:20,358 --> 00:18:22,358 Getting A hands-on session, doing this kind of work. 301 00:18:22,918 --> 00:18:26,958 Ability, again, more hands-on, more Friday learning sessions. 302 00:18:26,958 --> 00:18:28,998 We did an every other week Friday learning session. 303 00:18:29,158 --> 00:18:31,198 It was a great way to engage the community. 304 00:18:31,198 --> 00:18:33,958 And then reinforcement through those demos. 305 00:18:33,958 --> 00:18:39,398 To do this most effectively, though, what I found was awesome is... 306 00:18:40,518 --> 00:18:43,718 I know certain parts of the business extremely well. 307 00:18:43,718 --> 00:18:44,998 I grew up in operations. 308 00:18:45,158 --> 00:18:46,278 I lived in supply chain. 309 00:18:46,518 --> 00:18:49,958 I know a lot more about IT than I probably ever needed to. 310 00:18:50,478 --> 00:18:55,078 And yet I don't know anything about marketing, sales, or maybe even legal. 311 00:18:55,318 --> 00:18:58,518 I know enough to know that I don't know, but I don't know what I need to know. 312 00:18:58,838 --> 00:18:59,718 Does that make sense? 313 00:19:00,558 --> 00:19:01,078 Copilot. 314 00:19:01,078 --> 00:19:06,678 I'm going to be giving a conversation to Copilot on this topic with our legal team. 315 00:19:06,998 --> 00:19:10,758 Could you help me build demos that are appropriate for them and prompts that are appropriate for them? 316 00:19:11,238 --> 00:19:14,598 The minute I did that, my legal team was like, heck yeah, I'm in. 317 00:19:14,758 --> 00:19:16,278 I want to learn how I can use this. 318 00:19:16,438 --> 00:19:18,598 Because all of a sudden it was speaking their language. 319 00:19:18,918 --> 00:19:25,638 Even though I didn't do it to manipulate them, but I really did do it so that I could talk to them in the language they're used to hearing. 320 00:19:25,878 --> 00:19:27,878 It didn't just make generic prompts then. 321 00:19:28,038 --> 00:19:29,158 It made it specific to them. 322 00:19:30,238 --> 00:19:36,078 Targeted training sessions, getting that role-based training at the right time for those users, and then getting those leaders involved. 323 00:19:36,078 --> 00:19:40,598 If those directors and above were involved, those teams were more effective at deploying it. 324 00:19:43,398 --> 00:19:48,918 Getting those users to be proactive was hardest, but we still did it by answering questions. 325 00:19:48,918 --> 00:19:50,278 I had this community of practice. 326 00:19:50,678 --> 00:19:53,878 I never dismissed a question or a frustration. 327 00:19:53,878 --> 00:19:56,438 How many of you think Copilot's the best AI you've ever seen? 328 00:19:57,158 --> 00:19:58,358 Good, we're on the same page. 329 00:19:59,078 --> 00:20:02,118 But who thinks it's good in some ways, right? 330 00:20:02,198 --> 00:20:05,878 One of the things it does really great is it covers the entire Microsoft ecosystem. 331 00:20:06,118 --> 00:20:06,838 I love that. 332 00:20:07,238 --> 00:20:10,998 But it's not the best at any one thing, but it's good at a lot of things. 333 00:20:12,198 --> 00:20:14,358 How do you measure things for productivity? 334 00:20:14,518 --> 00:20:15,478 Back to time. 335 00:20:15,638 --> 00:20:26,118 If I can take the amount of time I spend on things and cut it in half, we had a supply chain example where it was every week they had an hour meeting with their supplier and another hour meeting with the manufacturing team. 336 00:20:26,678 --> 00:20:34,038 They cut it to half an hour of each, then they cut to 1/2 an hour every other week because they were getting more done in less time using these tools. 337 00:20:36,758 --> 00:20:42,678 Three Cs are the behaviors that we really enabled, and I put this all into Copilot and I said, help me out. 338 00:20:43,158 --> 00:20:45,878 I want to get people to be more curious. 339 00:20:46,198 --> 00:20:48,758 Curiosity killed the cat, but it doesn't kill the human. 340 00:20:49,318 --> 00:20:53,478 People need to know how to ask the question, even if it's, I don't know what questions to ask. 341 00:20:54,038 --> 00:21:00,358 I taught people how in a live meeting in Copilot being recorded, I should probably ask a question here. 342 00:21:00,358 --> 00:21:02,678 Can you give me 3 questions I could consider asking? 343 00:21:03,798 --> 00:21:07,638 Teaching people how to be curious is not native, but that's important. 344 00:21:08,678 --> 00:21:10,358 Being courageous is the next step. 345 00:21:10,998 --> 00:21:14,838 Okay, now you've thought about the question, you've asked Copilot to help you develop those questions. 346 00:21:15,198 --> 00:21:16,438 Are you willing to actually ask it? 347 00:21:17,478 --> 00:21:18,918 That takes a lot of faith. 348 00:21:19,238 --> 00:21:24,438 that the people in the room are going to listen when you ask it, and the courage to stand up and say, I'm willing to try. 349 00:21:24,598 --> 00:21:28,118 For me, it was learn how to stand in front of an audience and don't be afraid of it. 350 00:21:28,358 --> 00:21:29,398 You may not say it right. 351 00:21:29,398 --> 00:21:29,958 That's okay. 352 00:21:29,958 --> 00:21:31,238 They need to hear what you're sharing. 353 00:21:31,318 --> 00:21:32,598 So just keep going. 354 00:21:32,678 --> 00:21:33,318 Just keep going. 355 00:21:33,318 --> 00:21:34,038 It'll get better. 356 00:21:34,718 --> 00:21:35,558 And then collaborate. 357 00:21:35,558 --> 00:21:38,998 How do we scale this at speed and across the business? 358 00:21:39,558 --> 00:21:42,558 Again, we didn't do this at Pella as a requirement. 359 00:21:42,558 --> 00:21:46,358 Copilot was not something that was like, you will use AI by the end of. 360 00:21:46,358 --> 00:21:48,038 We didn't do that at all. 361 00:21:48,358 --> 00:21:51,798 So he started asking, Kurt, how did you get 97% adoption? 362 00:21:52,118 --> 00:21:54,038 And that's on a four-week rolling scale. 363 00:21:54,038 --> 00:21:57,238 So it's never been below 97% in the last six months. 364 00:21:58,678 --> 00:22:15,318 Last week, I pulled the deck, and it was 97% adoption in a four-week window, 84% adoption in a one-week window, and 34% of those people are such high users, they're considered power users by Microsoft's own metrics. 365 00:22:15,678 --> 00:22:22,038 which means they're using AI at least 15 times each week in more than one application. 366 00:22:22,598 --> 00:22:23,478 Well, that's huge. 367 00:22:23,718 --> 00:22:26,438 That doesn't exist when somebody hasn't been told they have to, right? 368 00:22:26,438 --> 00:22:26,958 They got to. 369 00:22:26,958 --> 00:22:31,718 The community of practice was an interesting one. 370 00:22:31,718 --> 00:22:33,718 I was told nobody will ever come. 371 00:22:34,038 --> 00:22:36,878 If they do, well, it will dwindle at some point. 372 00:22:37,158 --> 00:22:38,598 But if you want to try it, go ahead. 373 00:22:39,238 --> 00:22:39,958 Great, I'm all in. 374 00:22:39,958 --> 00:22:40,678 I'm going to sign this up. 375 00:22:40,678 --> 00:22:44,278 We're going to start inviting everybody, all 1000 users every time. 376 00:22:44,998 --> 00:22:46,438 Do some people feel like it's spam? 377 00:22:46,518 --> 00:22:46,998 Maybe. 378 00:22:47,478 --> 00:22:48,918 Not my problem if they do, right? 379 00:22:49,158 --> 00:22:54,598 I record them, I save them, we have a demo, we have intermediate and basic skills. 380 00:22:54,598 --> 00:22:56,838 We're not getting into advanced skills at this point. 381 00:22:57,798 --> 00:22:59,638 But we share best practices every time. 382 00:22:59,638 --> 00:23:02,118 It's a place where we have two demos every time. 383 00:23:02,278 --> 00:23:06,038 Somebody brings a demo of, I just use it to save me time in this meeting. 384 00:23:06,198 --> 00:23:08,358 I use it to set up an agenda for that meeting. 385 00:23:08,678 --> 00:23:17,238 Or we'll get into, okay, I built an agent, and here's what my agent knows how to do, and it talks between legal and marketing, and it does this work, and we named it Mallory, right? 386 00:23:17,398 --> 00:23:19,958 Like you name your agents so that you give it a persona. 387 00:23:20,118 --> 00:23:20,558 That's fine. 388 00:23:20,558 --> 00:23:22,998 And then we troubleshoot challenges. 389 00:23:23,158 --> 00:23:25,158 If somebody says, hey, this isn't working well, 390 00:23:25,678 --> 00:23:26,758 that's a great point. 391 00:23:26,758 --> 00:23:27,878 Let's talk about that. 392 00:23:28,038 --> 00:23:29,958 If we can solve it during this meeting, we will. 393 00:23:29,958 --> 00:23:35,318 But otherwise, if it's more than 5 minutes, usually we are like, okay, we'll set up a follow-up and we'll share with the update. 394 00:23:38,118 --> 00:23:43,558 Three key ways that I delivered this was through workshops of skill development, so going department by department. 395 00:23:43,558 --> 00:23:45,718 I started doing internal webinars. 396 00:23:45,878 --> 00:23:47,958 Hey, Copilot, help me do this more effectively. 397 00:23:48,118 --> 00:23:52,998 There still seems to be a gap in the way people are assuming that, you know, that they understand this information. 398 00:23:53,398 --> 00:23:58,078 People would reach out and be like, can't it create a video for me or can it do some audio stuff? 399 00:23:58,078 --> 00:23:59,238 And I'm like, yeah, sure. 400 00:23:59,398 --> 00:24:01,998 I'll do a webinar for that and invite people to attend. 401 00:24:01,998 --> 00:24:03,158 And if they come, that's great. 402 00:24:03,158 --> 00:24:05,598 If not, it's recorded and it's published on our SharePoint site. 403 00:24:05,598 --> 00:24:07,638 And then blog posts. 404 00:24:08,998 --> 00:24:09,718 Two blog posts. 405 00:24:09,718 --> 00:24:10,838 One started as Mindset. 406 00:24:10,918 --> 00:24:12,518 So Mindset Mondays, I call it. 407 00:24:12,518 --> 00:24:19,638 And it's every Monday, I drip just a simple one or two sentences of shifting your mindset around data. 408 00:24:19,798 --> 00:24:20,998 Data to make decisions. 409 00:24:21,478 --> 00:24:23,478 Courage to use the right data to make decisions. 410 00:24:23,478 --> 00:24:26,118 Courage to acknowledge if you have bad data, how to fix it. 411 00:24:26,798 --> 00:24:33,398 And then the second one was from another company that I heard from and I'm like, I love the idea of a Tuesday tidbit that's more tactical. 412 00:24:33,638 --> 00:24:35,318 So what can I do? 413 00:24:35,398 --> 00:24:37,878 So that one includes all the Copilot prompts. 414 00:24:38,238 --> 00:24:39,798 Drop this prompt into Copilot. 415 00:24:39,958 --> 00:24:41,638 Let it help you answer the question. 416 00:24:41,638 --> 00:24:42,838 And this year was kind of fun. 417 00:24:42,838 --> 00:24:44,598 Copilot, how can I make this even more effective? 418 00:24:44,838 --> 00:24:48,998 Well, you could tie your mindset on Monday to an action that you can take on Tuesday. 419 00:24:49,318 --> 00:24:50,038 Oh, that's great. 420 00:24:50,278 --> 00:24:57,238 Can you write all those posts for the next quarter and make sure you include courage and curiosity and collaboration and drip that throughout? 421 00:24:57,558 --> 00:24:57,958 Yeah. 422 00:24:58,518 --> 00:25:00,758 I don't have time to write all these posts, right? 423 00:25:00,998 --> 00:25:05,558 And yet one of the funny things about it was, why does Kurt take the time to write all these posts? 424 00:25:06,838 --> 00:25:16,998 Silly thing is, I wrote the quarters were the posts in an hour, reviewed them, agreed that, yeah, that's about the right cadence, and then there's times I'll even stop in the middle of the quarter and say, is this still landing well? 425 00:25:16,998 --> 00:25:18,598 Are there still people giving it thumbs up? 426 00:25:18,598 --> 00:25:21,318 If they're not, I'll change the last half of the quarter. 427 00:25:21,998 --> 00:25:23,358 This isn't a lot of work, right? 428 00:25:23,358 --> 00:25:26,518 This is like I spent an hour, I developed weeks of content. 429 00:25:28,918 --> 00:25:30,358 And it's not about learning Copilot. 430 00:25:30,838 --> 00:25:32,998 This technique, which has really been interesting, is 431 00:25:33,478 --> 00:25:36,758 as we've developed the need for more advanced AI. 432 00:25:37,318 --> 00:25:39,238 And I'm sure some of you use more than one. 433 00:25:39,238 --> 00:25:40,678 We use Claude now as well. 434 00:25:40,918 --> 00:25:42,358 We've got people using Claude. 435 00:25:42,358 --> 00:25:43,878 We've got GitHub Copilot. 436 00:25:44,358 --> 00:25:45,998 But it's building that capability. 437 00:25:45,998 --> 00:25:51,718 The minute that they had these other AI tools, they were jumping in and able to run successfully with it. 438 00:25:51,958 --> 00:25:54,998 So the techniques that we're teaching aren't really about Copilot. 439 00:25:54,998 --> 00:25:57,478 The Copilot just happened to be the mechanism we were using at the time. 440 00:25:59,638 --> 00:26:03,078 So how do you do this and do it well, especially when it's not your full-time gig? 441 00:26:03,318 --> 00:26:04,358 For me, it was iterative. 442 00:26:04,918 --> 00:26:07,558 Every training I gave every week, I had recorded. 443 00:26:07,758 --> 00:26:11,318 And I asked Copilot after every training, how could I make this training better for next time? 444 00:26:11,958 --> 00:26:15,078 I ran a little tight on time because they asked a lot of questions. 445 00:26:15,238 --> 00:26:20,038 Should I pivot the context so that they ask fewer questions, or do I want that interaction? 446 00:26:20,078 --> 00:26:21,158 What's the right approach? 447 00:26:21,638 --> 00:26:24,358 One of my favorites is, how could I be a better presenter? 448 00:26:25,198 --> 00:26:29,478 When I'm presenting to a new audience, the hardest thing for me to do is slow down. 449 00:26:30,198 --> 00:26:31,558 I tend to go fast. 450 00:26:31,558 --> 00:26:35,158 I have a lot of ideas and therefore a lot of words that need to try to come out. 451 00:26:35,558 --> 00:26:42,838 And it said, the first thing you do when you get the audience to engage, it's so much better if you can slow down and actually raise your hand. 452 00:26:43,238 --> 00:26:44,438 Do you all feel this way? 453 00:26:44,838 --> 00:26:45,798 Yes, good. 454 00:26:46,038 --> 00:26:47,398 Okay, that helped. 455 00:26:47,558 --> 00:26:50,678 Giving me that feedback made it iterative in the training process. 456 00:26:51,558 --> 00:26:53,718 And it improved that entire user experience. 457 00:26:54,118 --> 00:26:59,158 The teams that got taught 2 weeks ago, I'm so blessed because I now know how to do this more effectively. 458 00:26:59,478 --> 00:27:02,518 But that didn't mean that the teams that got it last year didn't get a good training. 459 00:27:02,918 --> 00:27:04,598 But they only got a good training, I would argue. 460 00:27:04,598 --> 00:27:09,638 So I'm going back and hitting some of those a second time, taking them from an individual to a team. 461 00:27:11,158 --> 00:27:18,438 So when we think about success, everybody wants the dollars ROI, but dollars are so hard to measure when you're doing it at an individual level. 462 00:27:18,918 --> 00:27:22,758 Thousand employees enabled to save a little bit of time, what's that really worth? 463 00:27:24,118 --> 00:27:25,238 We struggled with that. 464 00:27:26,518 --> 00:27:32,558 But when I came back from Microsoft back in December, they came to our facility and we got to talking and they said, I think this is good. 465 00:27:32,558 --> 00:27:34,198 And they're like, oh my God, you're not kidding. 466 00:27:34,198 --> 00:27:34,678 This is good. 467 00:27:34,678 --> 00:27:39,798 This is amazing because you haven't made it a requirement, but you're getting over 95% adoption. 468 00:27:40,918 --> 00:27:44,918 With really strong adoption, people aren't just playing with it anymore. 469 00:27:44,918 --> 00:27:45,878 They're using it. 470 00:27:46,198 --> 00:27:49,878 People won't use it month after month after month if it fails and hallucinates. 471 00:27:49,958 --> 00:27:50,758 They just don't. 472 00:27:50,758 --> 00:27:52,038 They stop using it that way. 473 00:27:52,278 --> 00:27:53,798 But they've learned where they can use it. 474 00:27:55,078 --> 00:27:57,238 It's integrated now into some of the workflows. 475 00:27:57,238 --> 00:28:05,518 My favorite examples of workflows, an engineer took a product that took him from end to end, 3 weeks to do, and he does it in 30 minutes now. 476 00:28:05,518 --> 00:28:08,758 And it took him two afternoons of playing around with Copilot to learn how to do it. 477 00:28:09,238 --> 00:28:13,238 It's human, AI in the loop, human, AI in the loop, human. 478 00:28:14,638 --> 00:28:24,678 three steps that he has to use and includes Excel, code written in Python that he'd never written before, and a few macros that it was enabling as well. 479 00:28:25,158 --> 00:28:25,878 Blew me away. 480 00:28:25,878 --> 00:28:27,078 He's like, I didn't know how to do this. 481 00:28:27,078 --> 00:28:29,238 And I'm like, well, let's make sure we do it the right way. 482 00:28:29,238 --> 00:28:32,038 Let me take your code and give it to a data scientist, make sure it's built right. 483 00:28:32,198 --> 00:28:33,078 Yep, it was. 484 00:28:33,118 --> 00:28:33,798 Everything's good. 485 00:28:34,278 --> 00:28:35,798 So at least we trust the system. 486 00:28:36,918 --> 00:28:38,838 You might ask, well, why didn't you just automate it? 487 00:28:39,158 --> 00:28:41,478 Because that wasn't the biggest thing to save that day. 488 00:28:41,798 --> 00:28:44,998 Automating all of those steps will come probably in six months anyway. 489 00:28:45,078 --> 00:28:47,718 This technology is going to do it all at some point. 490 00:28:48,278 --> 00:28:52,238 But right now it was, he took a problem, made a standard solution. 491 00:28:52,238 --> 00:29:00,158 And not only did he make a standard solution, copilot, when I'm done with this, make a reusable work instruction that I can teach my team how to do the same thing. 492 00:29:00,158 --> 00:29:03,078 And he's now taught six or seven different engineers to do the same thing. 493 00:29:03,638 --> 00:29:04,718 That's the win, right? 494 00:29:04,718 --> 00:29:05,678 That's collaboration. 495 00:29:05,678 --> 00:29:07,238 That's sharing that knowledge with others. 496 00:29:10,358 --> 00:29:12,678 So how does it work in our scale of things? 497 00:29:12,678 --> 00:29:13,798 It's A productivity driver. 498 00:29:13,798 --> 00:29:15,238 It's really measuring time. 499 00:29:15,718 --> 00:29:18,678 I don't measure things in dollars because people don't believe the dollars. 500 00:29:18,678 --> 00:29:19,198 When I tell them... 501 00:29:19,478 --> 00:29:24,118 Last year we enabled $5 million in productivity, and this year I'm on target to do 9. 502 00:29:24,198 --> 00:29:26,998 They look at it and like, yeah, but it's individual productivity. 503 00:29:27,478 --> 00:29:31,478 So that's still I didn't have to hire teams to do things, right? 504 00:29:31,478 --> 00:29:32,438 That's worth something. 505 00:29:33,478 --> 00:29:37,318 Streamlining processes and automation is so powerful. 506 00:29:37,478 --> 00:29:44,198 Finding those right pieces, fitting AI in the right spot, making the AI in the loop, and then empowering employees. 507 00:29:44,718 --> 00:29:48,438 What was cool about the way I taught people, how many of you like to be told how to do things? 508 00:29:49,558 --> 00:29:50,118 Yeah, right? 509 00:29:50,118 --> 00:29:50,838 None of us. 510 00:29:51,478 --> 00:29:53,238 I don't like to be told how to do things either. 511 00:29:53,878 --> 00:30:00,838 But if I can teach you that your problem's worth solving, and I can teach you how to talk to an AI, and that AI can be a thought partner. 512 00:30:01,078 --> 00:30:02,918 It can give that challenge back to you. 513 00:30:02,918 --> 00:30:05,238 It can beat up a presentation you're about to give. 514 00:30:05,638 --> 00:30:11,798 If it can do that and empower you to do it differently, do it more effectively, that's a huge win. 515 00:30:12,158 --> 00:30:16,038 And so people would listen and they would adapt and they would adopt. 516 00:30:18,638 --> 00:30:20,438 So what did I learn through all this? 517 00:30:20,438 --> 00:30:26,038 Having done this now for 12 months, two things I would do if I could do it all over again. 518 00:30:26,038 --> 00:30:28,358 One is get that leadership adoption earlier. 519 00:30:29,078 --> 00:30:33,078 It was six months before I heard from our C-suite, hey, what are we doing with AI literacy? 520 00:30:33,798 --> 00:30:35,398 I should have had them on board. 521 00:30:35,638 --> 00:30:38,118 That was a mistake on my part, but it was more than that. 522 00:30:38,118 --> 00:30:41,078 It was, they were part of the initial adoption campaign. 523 00:30:41,398 --> 00:30:42,318 They didn't buy in. 524 00:30:42,318 --> 00:30:44,998 Now, I will admit, I think we started with Copilot too early. 525 00:30:44,998 --> 00:30:46,758 We were probably six months too early. 526 00:30:47,398 --> 00:30:51,878 And so they got to experience really bad copilot before they got to experience it getting better. 527 00:30:52,718 --> 00:30:56,278 And the other one is engage those AI influencers or champions earlier. 528 00:30:56,758 --> 00:30:58,838 We had a few people we thought were champions. 529 00:30:58,998 --> 00:31:00,518 They were just power users. 530 00:31:00,678 --> 00:31:06,758 There is a distinct difference between a person that can influence others and someone that's a power user in the platform. 531 00:31:07,238 --> 00:31:12,918 Don't mix those up and say, oh, because they use it 100 times a day, they must be a really good influencer. 532 00:31:13,238 --> 00:31:14,118 They're not always. 533 00:31:16,918 --> 00:31:19,718 To scale, we went, like I mentioned, team by team. 534 00:31:19,718 --> 00:31:22,598 It was focused on small groups, individual productivity. 535 00:31:22,918 --> 00:31:25,238 This year, I'm going back and hitting them as a team. 536 00:31:25,718 --> 00:31:27,278 How is it changing the way you work as a team? 537 00:31:27,278 --> 00:31:28,678 Are you sharing your prompts with each other? 538 00:31:28,758 --> 00:31:32,918 Have you created an agent to do some of the hard lifting things that need to be standardized? 539 00:31:33,678 --> 00:31:35,478 What about enterprise enablement? 540 00:31:35,718 --> 00:31:38,678 Where are we enabling the enterprise to go better and faster? 541 00:31:38,838 --> 00:31:42,358 That's where we're getting into multi-agentic solutions that we're implementing in places. 542 00:31:42,598 --> 00:31:46,838 And it's changing the way we deliver value to ourselves as well as to our customers. 543 00:31:46,838 --> 00:31:51,678 How many of you have heard of the Create Framework? 544 00:31:51,678 --> 00:31:53,238 This was a fun one for me. 545 00:31:53,958 --> 00:31:54,438 Couple? 546 00:31:54,438 --> 00:31:55,158 Only a couple. 547 00:31:55,158 --> 00:31:55,478 Okay. 548 00:31:56,198 --> 00:31:57,798 This was brought to me in January. 549 00:31:57,798 --> 00:32:05,558 I did this same presentation to the Des Moines Data and Analytics Group, and it was kind of in preparation for knowing I was doing this talk at Cirrus. 550 00:32:06,998 --> 00:32:10,358 Somebody came up to me afterwards and said, hey, have you heard of Dave Burce's create framework? 551 00:32:10,358 --> 00:32:12,278 I'm like, no, but I'd love to know more. 552 00:32:12,438 --> 00:32:14,918 He said, we started using this to build agents. 553 00:32:15,478 --> 00:32:16,518 That's a neat idea. 554 00:32:16,798 --> 00:32:17,638 Guess what I did? 555 00:32:17,958 --> 00:32:19,718 I took this back to Pella the next week. 556 00:32:20,038 --> 00:32:21,918 I showed it to one of my AI influencers. 557 00:32:21,918 --> 00:32:23,238 He's like, that's a great idea. 558 00:32:23,398 --> 00:32:25,718 He created an agent that does this. 559 00:32:27,198 --> 00:32:28,998 You want to talk about power of people, right? 560 00:32:29,158 --> 00:32:35,238 He took a problem that I said, we need to probably have an agent that can help us create better instructions for better agents. 561 00:32:35,718 --> 00:32:42,278 And he built an agent that did it using this framework with the same restrictions that Copilot has of 8,000 characters. 562 00:32:42,598 --> 00:32:45,878 But then we also added pieces of it like suggested prompts. 563 00:32:46,198 --> 00:32:49,718 Build it with AI, robust processes inside of it. 564 00:32:49,878 --> 00:32:51,478 Make sure it's got high quality. 565 00:32:51,638 --> 00:32:55,718 Make sure it's asking questions one at a time for more context if it gets lost. 566 00:32:56,838 --> 00:32:58,598 That's a really cool tool. 567 00:32:59,558 --> 00:33:02,198 But how many of you have thought about using AI to create more AI? 568 00:33:02,918 --> 00:33:05,718 The first thing we did was, okay, let's see how this works. 569 00:33:05,718 --> 00:33:09,158 We did a demo to our consumers, right, our community of practice. 570 00:33:10,278 --> 00:33:13,398 Within 2 weeks, we had 100 more agents created. 571 00:33:14,318 --> 00:33:19,878 We've got over 800 agents currently across the system at Pella, and most of those agents have been built in the last six months. 572 00:33:20,358 --> 00:33:22,758 People are building their own agents to solve their own problems. 573 00:33:22,918 --> 00:33:24,518 It is the coolest thing I've ever seen. 574 00:33:24,758 --> 00:33:27,478 They're solving their challenges with AI. 575 00:33:27,878 --> 00:33:32,278 Not because I told them how, because I gave them the guidelines and the guardrails around it. 576 00:33:38,688 --> 00:33:41,328 Three sources that I would recommend you consider looking into. 577 00:33:42,398 --> 00:33:43,798 and I would have included others. 578 00:33:44,118 --> 00:33:45,718 So Data Society that's in the room. 579 00:33:46,518 --> 00:33:50,198 The Data Lodge really gave me the foundation to know how to do this differently. 580 00:33:50,278 --> 00:33:55,198 To think in terms of, I have a problem, do I have data to find to solve that problem? 581 00:33:55,198 --> 00:33:59,398 And then how do I deliver that back to the organization to change the way we behave? 582 00:33:59,798 --> 00:34:01,238 It's all about behavior. 583 00:34:01,558 --> 00:34:04,838 If I can change the way we behave, I'll change the outcomes we produce. 584 00:34:05,158 --> 00:34:07,638 But you can't get there without changing something. 585 00:34:08,678 --> 00:34:13,318 IIA was a partner in this space because I would say, I think we're leading in a lot of these things. 586 00:34:13,718 --> 00:34:19,558 Find me other people that are also leading because when you're on the tip of the spear, you need those other people with you on the tip of the spear. 587 00:34:19,638 --> 00:34:26,198 It's really hard to find companies that are like all into AI and still trying to do it safely and smartly. 588 00:34:26,518 --> 00:34:28,598 There's companies that are doing it stupidly, right? 589 00:34:28,598 --> 00:34:31,638 We see those in the news and those ones scare me, right? 590 00:34:31,638 --> 00:34:37,958 It's they enabled the AI and all of a sudden it deleted their database or they did something crazy in their process. 591 00:34:38,678 --> 00:34:40,398 And then the AI Driven Leader podcast. 592 00:34:40,398 --> 00:34:42,118 How many of you have heard Jeff Woods? 593 00:34:42,478 --> 00:34:43,478 Okay, a few of you. 594 00:34:44,438 --> 00:34:53,878 The idea of the Post-it note came from one of his podcasts and building an AI tool that helps me think clearly, to think about problems differently. 595 00:34:54,278 --> 00:34:56,998 I built my own agentic AI solution. 596 00:34:57,078 --> 00:34:58,358 I call it my North Star. 597 00:34:58,918 --> 00:35:05,158 It consists of about 15 individuals, both present and past, that change the way that I consume 598 00:35:05,558 --> 00:35:06,918 and analyze information. 599 00:35:07,078 --> 00:35:08,678 Now does that take me out of the loop? 600 00:35:08,758 --> 00:35:09,558 Absolutely not. 601 00:35:10,198 --> 00:35:19,718 But it gives me the opportunity to hear from Steve Jobs, Michael Jordan, Leonardo da Vinci, and others to say, how can you do this more effectively? 602 00:35:20,038 --> 00:35:22,118 Who are you missing when you're communicating? 603 00:35:22,598 --> 00:35:26,118 Because you're really passionate, but are you losing people on the journey? 604 00:35:26,838 --> 00:35:32,118 And it gives me that confidence to go back and say, okay, let's double check that this message lands. 605 00:35:32,358 --> 00:35:38,038 And one thing it picked up is it added the literacy comments earlier in the slide deck. 606 00:35:38,518 --> 00:35:42,918 Because if it wasn't for AI literacy, none of this would be true today at Pella. 607 00:35:43,238 --> 00:35:48,118 It's not because I forced behavior at Pella, and it's not because I took away licenses from people that weren't using it. 608 00:35:48,358 --> 00:35:49,558 That's the other thing that's crazy. 609 00:35:49,558 --> 00:35:52,358 When you see Microsoft benchmarks, you're like, well, how did they do that? 610 00:35:52,358 --> 00:35:54,038 Well, they started taking licenses away. 611 00:35:54,198 --> 00:35:56,598 If you're not using it within 30 days, it's gone, right? 612 00:35:56,598 --> 00:35:58,278 We're not paying for something you're not using. 613 00:35:59,718 --> 00:36:02,518 I got 984 people that used it in the last week. 614 00:36:02,918 --> 00:36:03,878 That's amazing. 615 00:36:04,758 --> 00:36:06,758 But why did the other 30 or 40 not? 616 00:36:06,758 --> 00:36:07,958 I don't know, right? 617 00:36:07,958 --> 00:36:11,078 Some of them might be on vacation, some of them just might not use it yet. 618 00:36:11,318 --> 00:36:12,118 That's okay. 619 00:36:12,278 --> 00:36:13,638 We'll take them away eventually. 620 00:36:13,878 --> 00:36:14,598 We're getting close. 621 00:36:14,598 --> 00:36:16,918 When we run out of licenses, we'll start taking away. 622 00:36:17,318 --> 00:36:19,638 And God be with the people that thought that they needed it later. 623 00:36:22,998 --> 00:36:26,998 To lead this adoption, I think there's a few key things that are most important. 624 00:36:26,998 --> 00:36:29,398 One is passionate for people. 625 00:36:29,718 --> 00:36:32,998 Make it about the people first, not about the technology. 626 00:36:33,798 --> 00:36:35,798 Just like we saw in the internet days, right? 627 00:36:35,798 --> 00:36:38,038 Like which search engine is the best? 628 00:36:38,438 --> 00:36:39,958 How many of you liked Ask Jeeves? 629 00:36:40,118 --> 00:36:41,798 Anybody old enough to remember Ask Jeeves? 630 00:36:41,958 --> 00:36:42,998 I loved Ask Jeeves. 631 00:36:42,998 --> 00:36:43,238 Why? 632 00:36:43,238 --> 00:36:45,718 Because I could do natural language questions. 633 00:36:46,038 --> 00:36:47,238 How many of us love Google? 634 00:36:47,238 --> 00:36:50,118 Well, because it's right most of the time, right? 635 00:36:50,198 --> 00:36:51,718 It gets it right most of the time. 636 00:36:51,718 --> 00:36:54,678 How many of them think that Google's Gemini sucks? 637 00:36:56,038 --> 00:36:57,558 Okay, a little harsh, right? 638 00:36:57,558 --> 00:37:01,558 Because Google's Gemini was coming late to the party, right, with AI. 639 00:37:03,078 --> 00:37:05,558 Think about high-value business cases, though. 640 00:37:05,958 --> 00:37:07,558 It can't just be grassroots. 641 00:37:07,958 --> 00:37:12,758 It has to come from the top down with high-level organizational plans as well. 642 00:37:13,078 --> 00:37:16,758 Big AI use cases generate a lot of momentum. 643 00:37:16,918 --> 00:37:17,958 Vijay's in the back. 644 00:37:18,118 --> 00:37:22,518 He's been involved in some of our biggest initiatives with AI in vision systems. 645 00:37:23,318 --> 00:37:24,518 Can Copilot do vision? 646 00:37:24,998 --> 00:37:26,438 Not real well, right? 647 00:37:26,838 --> 00:37:27,718 Can it in the future? 648 00:37:27,718 --> 00:37:28,278 Maybe. 649 00:37:28,598 --> 00:37:37,398 But partnering with a company that can do that and letting us learn how to do those technologies and enable that in a way that the business hears AI more often makes it part of the conversation. 650 00:37:38,158 --> 00:37:41,798 And then make sure you're thinking change management to empower employees. 651 00:37:42,278 --> 00:37:45,878 Don't be afraid of giving your people the power to make decisions. 652 00:37:46,118 --> 00:37:53,398 The hardest thing for most directors in this space has been letting their people make good decisions without their permission. 653 00:37:54,358 --> 00:38:02,758 So if you're in one of those leadership roles and you're sitting here going, okay, I want to let my people do it, but I don't know that I trust their judgment, let them get good judgment. 654 00:38:03,158 --> 00:38:04,358 Teach them good judgment. 655 00:38:04,358 --> 00:38:05,238 That's your job. 656 00:38:05,478 --> 00:38:08,118 It's not to stop them from making good decisions. 657 00:38:08,278 --> 00:38:12,358 Your goal would be fewer of you need to make the big decision every time. 658 00:38:12,438 --> 00:38:13,958 Let them make some of those decisions. 659 00:38:14,438 --> 00:38:16,838 Let AI help them make it more effectively. 660 00:38:19,438 --> 00:38:21,798 And with that, my presentation is wrapping. 661 00:38:22,198 --> 00:38:26,038 If you'd love to learn more about Pella, I'm happy to talk with you more about it. 662 00:38:26,118 --> 00:38:29,558 We're always hiring good talent, doesn't have to be in the AI space. 663 00:38:29,878 --> 00:38:34,158 As we're enabling all across the organization, we are really growing, right? 664 00:38:34,158 --> 00:38:35,478 We're trying to find ways to grow. 665 00:38:35,718 --> 00:38:37,478 We're continually expanding. 666 00:38:38,198 --> 00:38:41,238 And I'm happy to have been a part of this journey for the last 34 years. 667 00:38:41,238 --> 00:38:42,758 It's been quite a wild ride. 668 00:38:42,758 --> 00:38:44,918 So any questions? 669 00:38:46,838 --> 00:38:47,998 5 minutes for questions. 670 00:38:48,038 --> 00:38:50,198 Okay, I'm going to try to get to you with the mic. 671 00:38:50,198 --> 00:38:50,998 We have a full house today. 672 00:38:51,078 --> 00:38:53,718 Well, I want to get it for the transcription because AI. 673 00:38:56,038 --> 00:39:01,998 So when you said you rolled out the 1000 licenses, what would you say was your buy-in at the initial point of that? 674 00:39:01,998 --> 00:39:06,038 And what did you present to get buy-in for that? 675 00:39:06,038 --> 00:39:07,078 Can I tell you the truth? 676 00:39:07,638 --> 00:39:08,598 Buy-in was hard. 677 00:39:08,758 --> 00:39:11,158 Everybody looked at me like, well, you're not an official trainer. 678 00:39:11,158 --> 00:39:13,038 You're not in the HR training and development program. 679 00:39:13,038 --> 00:39:14,518 What gives you the permission to do this? 680 00:39:15,158 --> 00:39:16,358 Just let me do it once. 681 00:39:16,798 --> 00:39:17,718 Let me do it once. 682 00:39:17,798 --> 00:39:19,158 I think my passion will come through. 683 00:39:19,158 --> 00:39:19,958 People will follow. 684 00:39:20,278 --> 00:39:23,718 If I start setting up these meetings and people stop coming, I'll know. 685 00:39:24,518 --> 00:39:25,558 Crazy thing happened. 686 00:39:26,438 --> 00:39:27,398 Kurt did a great training. 687 00:39:28,118 --> 00:39:29,558 I'd like to get in on the next one. 688 00:39:29,718 --> 00:39:31,158 Hey, Kurt, when are you coming to sales? 689 00:39:31,238 --> 00:39:32,598 Hey, Kurt, when are you coming to marketing? 690 00:39:33,238 --> 00:39:38,198 And it happened because I wasn't afraid of going to each department and tailoring it specific to them. 691 00:39:38,918 --> 00:39:41,718 And their adoption was, they were anxious for it. 692 00:39:41,718 --> 00:39:44,918 The things that were crazy were the teams that were already using an AI on the side. 693 00:39:44,918 --> 00:39:51,798 Marketing was using AI on the side to create content so they could create it and recreate it in our own systems. 694 00:39:52,438 --> 00:39:56,078 But they were able to say, oh, now I don't need to keep this on the side as a tag-along AI. 695 00:39:56,078 --> 00:39:58,438 And I'm like, okay, we won't tell anybody that you did that. 696 00:39:58,438 --> 00:40:00,038 Yes, please stop doing that. 697 00:40:00,918 --> 00:40:04,518 But the first one was, no, Kurt shouldn't do this. 698 00:40:04,878 --> 00:40:05,958 And I'm like, just let me do it. 699 00:40:06,038 --> 00:40:06,918 We've got the licenses. 700 00:40:06,918 --> 00:40:08,598 We're spending $25,000 a month. 701 00:40:08,838 --> 00:40:14,038 on these licenses, that's an expense we can't continue to do if people don't use it. 702 00:40:14,118 --> 00:40:18,838 And if you do it wrong, my opinion was, if you did this wrong and you didn't enable it with people first, 703 00:40:19,278 --> 00:40:20,758 you were going to totally miss the mark. 704 00:40:21,398 --> 00:40:27,478 And then you wouldn't solve problems the way humans solve problems, which is let you solve your problem, not me. 705 00:40:27,478 --> 00:40:32,038 Let me talk to Copilot with you how to solve your problem and not take Kurt's advice. 706 00:40:33,758 --> 00:40:34,078 Awesome. 707 00:40:34,078 --> 00:40:35,398 Other questions? 708 00:40:35,958 --> 00:40:36,838 Oh, we got a couple. 709 00:40:37,158 --> 00:40:37,558 Hang. 710 00:40:41,158 --> 00:40:44,758 How do you make sure that the agents that people are building 711 00:40:46,278 --> 00:40:49,718 work together and work over like business continuity. 712 00:40:49,758 --> 00:40:54,038 When somebody leaves, what happens to the agents that they develop that sort of thing? 713 00:40:54,038 --> 00:40:55,398 You build an agent, you can't leave. 714 00:40:58,358 --> 00:41:03,158 To be honest, we've had, of the people that have created agents, the craziest thing has happened. 715 00:41:03,638 --> 00:41:08,358 As we've enabled this, we've had less turnover, which is almost weird, but less turnover. 716 00:41:08,358 --> 00:41:13,958 It's the biggest concern we have right now, though, is if we've created an agent in one department and that person moves, what will happen? 717 00:41:14,438 --> 00:41:15,638 We've got a process for that. 718 00:41:15,638 --> 00:41:16,438 We're working through that. 719 00:41:16,438 --> 00:41:21,038 We're looking at the Agent 365 product right now to try to build that agent registry. 720 00:41:21,038 --> 00:41:24,838 We've also got AWS on the wings and we want to build that solution. 721 00:41:25,158 --> 00:41:27,718 We've got an admin team that will manage that for me. 722 00:41:29,118 --> 00:41:29,278 Awesome. 723 00:41:29,278 --> 00:41:31,398 There were other, there was another question right back here. 724 00:41:32,678 --> 00:41:33,398 Very quick one. 725 00:41:34,118 --> 00:41:37,238 The C-suite folks that were not early adapters, are they in? 726 00:41:38,998 --> 00:41:40,438 Some of them, yes. 727 00:41:41,238 --> 00:41:42,278 So fun fact, 728 00:41:42,878 --> 00:41:46,918 And I was going to do a demo, but I didn't want to pull it up and do it during this because I was afraid of time. 729 00:41:47,558 --> 00:41:49,398 I created a podcast this year. 730 00:41:49,398 --> 00:41:51,558 So one of the things was, how can I get even better? 731 00:41:52,038 --> 00:41:56,598 One of my colleagues at another company that's doing data literacy, she said, she started doing a podcast. 732 00:41:56,918 --> 00:41:57,238 Great. 733 00:41:57,318 --> 00:41:59,878 Hey, can you be my, so I created an agent for a podcast. 734 00:42:00,038 --> 00:42:02,358 Be my podcast manager, help me write questions. 735 00:42:02,438 --> 00:42:04,518 The goal is AI literacy and data literacy. 736 00:42:04,758 --> 00:42:08,838 I want to talk to C-suite people, and I want to also get down to directors and managers. 737 00:42:09,078 --> 00:42:10,518 How do I do that effectively? 738 00:42:10,518 --> 00:42:11,758 And I'm starting to get answers. 739 00:42:11,758 --> 00:42:12,718 So I've talked to the 740 00:42:12,798 --> 00:42:14,998 CFO and the CMO in my podcast. 741 00:42:15,478 --> 00:42:17,758 I've got a VP of engineering involved next. 742 00:42:17,758 --> 00:42:24,678 I've got a director of our product and EPMO, our enterprise portfolio management team. 743 00:42:24,838 --> 00:42:29,478 So as we look at these things, it's getting those people, they're buying in. 744 00:42:29,998 --> 00:42:31,478 But are they buying in at the rate I'd like? 745 00:42:31,758 --> 00:42:33,798 No, But they have a different problem. 746 00:42:33,958 --> 00:42:38,118 Their challenge is different than what an AI today of development might be. 747 00:42:38,438 --> 00:42:39,158 So I get it. 748 00:42:39,158 --> 00:42:43,638 They don't see the value yet, or they're really worried about the confidential nature of it. 749 00:42:44,198 --> 00:42:47,158 I don't want them to drop things in there that are confidential yet. 750 00:42:47,238 --> 00:42:51,078 We're still telling them, no, if it's really innovative, stay away from AI. 751 00:42:51,238 --> 00:42:55,718 If it's really something that you wouldn't talk to Google about, don't talk to Copilot about it either. 752 00:42:56,198 --> 00:42:58,118 So some of that is just the behavior. 753 00:42:59,638 --> 00:43:01,158 One more question I think we could take. 754 00:43:01,198 --> 00:43:01,558 Okay. 755 00:43:01,878 --> 00:43:02,758 Come in right up here. 756 00:43:05,078 --> 00:43:06,678 And I can stay all night if you guys want. 757 00:43:06,678 --> 00:43:06,998 So. 758 00:43:08,358 --> 00:43:10,598 what does your data governance look like? 759 00:43:11,318 --> 00:43:15,958 You talked about those people that you don't want dropping that information in. 760 00:43:17,478 --> 00:43:18,838 It's, well, it's Copilot. 761 00:43:18,918 --> 00:43:21,158 Someone, everything that they drop in. 762 00:43:21,158 --> 00:43:26,438 So if they drop in a document to Copilot, and we give advice on it, but can you really monitor it? 763 00:43:26,598 --> 00:43:27,078 Sure. 764 00:43:27,318 --> 00:43:29,438 But it's only through their own tenant. 765 00:43:29,478 --> 00:43:33,478 So we've got Microsoft admins that are watching for really nasty behaviors. 766 00:43:33,798 --> 00:43:35,718 We're building an agentic solution for that. 767 00:43:36,118 --> 00:43:38,038 Most of it's been all on your person. 768 00:43:38,398 --> 00:43:39,318 So it's education. 769 00:43:39,438 --> 00:43:43,798 And every two weeks when I have these conversations, there's always a security conversation. 770 00:43:44,438 --> 00:43:46,758 I bring the security team into that all the time. 771 00:43:46,998 --> 00:43:50,758 The other thing has been our master data management and data governance side. 772 00:43:50,998 --> 00:43:54,998 So as we think about building tools, are those AI enabled? 773 00:43:54,998 --> 00:43:57,238 Is it a single source of truth we can trust? 774 00:43:57,798 --> 00:44:02,518 But like any 100 year old company, we've got data that we don't like and the AI really doesn't like. 775 00:44:03,198 --> 00:44:04,358 We're working through it.