บทถอดความฉบับเต็มของ Inside Insights: Navigating Business Transformations with Gen AI with Geoff Scott

knowledge and, and technology know how, which really is what a lot of ASA customers or in general SAP folks who are dealing with technology on a daily basis can, can utilize your, your, your know how and your, your in depth knowledge of what's going on in the industry versus, you know someone with a background in business, you don't really have that kind of depth in terms of what you bring to the table. 52 00:04:12,783.2985 --> 00:04:12,993.2985 So. 53 00:04:13,263.2985 --> 00:04:14,53.2985 You're very kind. 54 00:04:14,98.2985 --> 00:04:14,138.2985 that. 55 00:04:14,313.2985 --> 00:04:14,973.2975 You're very kind. 56 00:04:14,973.2985 --> 00:04:21,413.2985 And in my career, I started off when I was going to college just a little bit West from where you are. 57 00:04:21,673.2985 --> 00:04:22,533.2985 Again, go green. 58 00:04:22,593.2985 --> 00:04:24,493.2985 Then that's the last I'm going to say that. 59 00:04:24,493.3985 --> 00:04:24,538.1985 Yep. 60 00:04:24,858.2985 --> 00:04:29,808.2985 Today, maybe my degrees in accounting and I chose that field. 61 00:04:30,248.2985 --> 00:04:32,558.2985 Because I really wanted to understand how business worked. 62 00:04:32,558.2985 --> 00:04:36,828.2985 And I figured the best way to figure out how business would work is to understand how the money moves around. 63 00:04:37,408.2985 --> 00:04:39,588.2985 And accounting was actually a fallback for me. 64 00:04:39,588.2985 --> 00:04:43,278.2975 I started off in finance and then this will date me tremendously. 65 00:04:43,278.2985 --> 00:04:46,398.2975 And then the stock market collapsed back in the late eighties. 66 00:04:46,398.2975 --> 00:04:47,448.2965 And I went, Oh, wait a minute. 67 00:04:47,528.2975 --> 00:04:48,278.2975 Yeah, I don't know. 68 00:04:48,278.2975 --> 00:04:49,458.2975 I want to be on wall street anymore. 69 00:04:49,458.2975 --> 00:04:51,538.2975 And I grew up in the suburbs of New York city. 70 00:04:51,538.2975 --> 00:04:53,428.2975 So I had this, you know, Delusion. 71 00:04:53,428.2975 --> 00:04:56,38.2975 I would go back into New York and be on wall street. 72 00:04:56,38.2975 --> 00:04:57,418.2975 And I said, I don't think that's going to work so well. 73 00:04:57,418.2975 --> 00:05:03,518.2975 So I went to, I went to accounting and I found I liked it better, a little bit more pragmatic, right? Finance can be fairly esoteric. 74 00:05:03,808.2975 --> 00:05:09,368.2985 So I came into consulting and it, because I always thought about it as a way in which businesses can be more efficient. 75 00:05:09,658.2965 --> 00:05:14,8.2965 And I was always intrigued by how we could use technology to drive business outcomes. 76 00:05:14,388.2965 --> 00:05:16,198.2965 And that has served me throughout my career. 77 00:05:16,198.2965 --> 00:05:19,788.2975 So I, I really think about business outcomes first and technology. 78 00:05:21,348.2975 --> 00:05:22,28.2975 Absolutely. 79 00:05:22,28.2975 --> 00:05:25,708.2975 And

I think that's what really counts, right? How business drives technology. 80 00:05:26,38.2975 --> 00:05:29,588.2975 And that kind of takes me to my next discussion point. 81 00:05:29,598.2975 --> 00:05:31,368.2975 You know, AI. 82 00:05:31,418.2965 --> 00:05:34,253.2975 AI took the business world by storm last Yeah. 83 00:05:34,448.2975 --> 00:05:34,898.2975 know that. 84 00:05:35,938.2965 --> 00:05:44,318.2975 are ASUG and SAP supporting their clients with navigating AI and Gen AI in particular, right? I mean, everybody's about Gen AI. 85 00:05:44,318.2975 --> 00:05:45,898.2975 So I'd like to hear your thoughts on that. 86 00:05:46,38.2975 --> 00:05:46,973.2975 I think, Yeah. 87 00:05:46,973.2975 --> 00:05:52,573.2975 I think that AI generative AI and all the things related to AI, right? Nothing new to that. 88 00:05:52,573.3975 --> 00:05:56,163.2975 We've been around in the SAP ecosystem. 89 00:05:56,613.2965 --> 00:06:05,733.2975 AI has been around for a long time, right? What was new in November of 2022 when chat GPT first came onto the market. 90 00:06:06,143.2975 --> 00:06:16,653.2965 Was this thing of generative AI, right? Well, that was different, right? But you know, most SAP practitioners, the people you and I are talking to today would say, Hey, you know, we've been filling around AI for a long time. 91 00:06:16,663.2975 --> 00:06:27,813.2975 You know, understanding PDF documents, understanding pictures, converting, you know, pictures to text, scanning documents, scanning invoices, making sure we can convert all that, that none of that is terribly new. 92 00:06:28,373.2975 --> 00:06:30,603.2975 I think generative AI made it mainstream. 93 00:06:31,118.2975 --> 00:06:40,198.2975 And what was kind of back office technology that was used to achieve business outcomes all of a sudden became available to the masses. 94 00:06:41,298.2975 --> 00:06:43,598.2975 And it became available to the masses in a very simple way. 95 00:06:43,868.2975 --> 00:06:51,828.2975 I can sit down, I can write a sentence into a computer and it will produce paragraphs of very eloquent text. 96 00:06:52,248.2975 --> 00:07:08,168.2975 We can have a whole conversation about how accurate it is, but I could finally get this star tracky type of thing where I could type a sentence in and I would get this back and I could do cute things, you know, Tell me, you know, tell me how to bake a cake in Shakespearean English and it would do it right. 97 00:07:08,168.2975 --> 00:07:27,728.2975 Or, you know, so I think it became a, a piece of technology that everybody could connect to and that everybody includes the board of directors, the CEO, the rest of your business peers who can now say, I get it, I understand how this works and I want that for my business. 98 00:07:28,918.2975 --> 00:07:30,748.2975 We can make this work for all of us. 99 00:07:31,363.2975 --> 00:07:32,788.2975 and I think it's a very interesting point. 100 00:07:32,788.2975 --> 00:07:38,638.2965 You mentioned Geoff talk about c-suite, right? And, and you know, you always, you know that in the, in, in I'm one of them. 101 00:07:39,208.2975 --> 00:07:40,828.2975 Yeah, exactly right. 102 00:07:40,828.2975 --> 00:07:48,283.2975 So you know, I still get involved with a lot of implementations and, you know, put some ground, and I know that a lot of these. 103 00:07:48,908.2975 --> 00:07:55,833.2965 technology implementations, have this gap between the C suite and the, and folks who are actually Hmm. 104 00:07:56,138.2975 --> 00:08:27,418.1975 in, in the technology day to day, right? Do you think Gen AI is going to close that gap? Or what is your take on that perspective? Like bringing these two worlds together? I think generative AI is going to be an incredibly interesting diversion or, or departure for all of us in the sense that we've talked about for a long time, the importance of some things in the SAP ecosystem that are near and dear to our heart, master data, accuracy of data. 105 00:08:27,738.2975 --> 00:08:33,278.2975 Archiving, right? Things that, you know, warm our hearts that make the business run for cover. 106 00:08:33,468.2975 --> 00:08:39,48.2975 You want to watch paint dry, have a conversation about archiving, right? And. 107 00:08:39,268.2975 --> 00:08:39,588.2975 absolutely. 108 00:08:39,728.2975 --> 00:08:52,898.2975 challenge with all of that is if we really want to get the most value from a generative AI solution, whether it be SAP's Jewel or ChatGPT or everything in between, our enterprise data has to be lined up correctly. 109 00:08:53,158.2975 --> 00:09:05,228.2975 And I think this is where we're going to see A tremendous amount of energy and effort to understand how this enterprise data will form these models and make them work. 110 00:09:06,208.2975 --> 00:09:15,888.2975 There was a article in the New York Times, I think two weeks ago, and this is topical because last week I was in Las Vegas for a few days at Google Next. 111 00:09:16,553.2975 --> 00:09:29,883.2975 And I always go to Google next and I, you know, and I also like to try to make it, you know, to AWS and Microsoft's events as well, because it refreshes me and it makes me think about how to tackle these problems from different perspectives. 112 00:09:30,423.2965 --> 00:09:41,443.2975 And that coupled with the New York times article was very interesting to me in that it appears we're running out of trainable data for these models that our models. 113 00:09:41,763.2975 --> 00:09:46,333.2975 now are demanding so much data that we just, we can't fill them. 114 00:09:46,393.2975 --> 00:09:46,803.2975 Right. 115 00:09:46,823.2975 --> 00:09:54,603.2975 And so there was an interesting topic in, in Las Vegas about synthetic data, which I'm still wrapping my head around and what that means. 116 00:09:54,963.2975 --> 00:09:58,653.2975 And I'm trying to understand how we get to the levels of data. 117 00:09:58,673.2975 -->

00:10:05,633.2975 We do know one thing that these generative AI models require a lot of data in order to give Effective answers. 118 00:10:05,843.2975 --> 00:10:08,153.2975 And even when they have a lot of data, they can still hallucinate. 119 00:10:08,153.2975 --> 00:10:17,313.2975 I mean, there's no greater data source than the English language over the last 300 years, right? And the cool thing about it is it hasn't really changed all that much. 120 00:10:17,313.2975 --> 00:10:25,83.2965 You know, I can take all of that stuff and I can pour it in and yeah, there's different dialects, but the English language or pick a language, French, whatever, it hasn't moved all that much. 121 00:10:25,583.2965 --> 00:10:27,533.2965 So the data is fairly stable. 122 00:10:28,943.2965 --> 00:10:52,353.2965 Is that true when we think about our enterprise data and the problem that I see coming is if we have lots of historical data, what does it really mean? How accurate is it? And then the second big question is how relevant is it? And if both of those are not at the top of their game, you run a huge risk that your model is being trained. 123 00:10:52,813.2975 --> 00:10:54,973.2975 On data that isn't accurate, it isn't relevant. 124 00:10:54,973.2975 --> 00:10:57,163.2975 And then you expect it to give you amazing results. 125 00:10:57,593.2975 --> 00:11:22,983.2975 The, the thing that makes me chuckle is the notion of saying to a model running on top of your SAP data, Hey, what's the best product I should sell? And it spits back a product that you made 15 years ago, because it might've been at the time, the most profitable based in parts that you don't even have access to anymore, and the model doesn't know that I think there's another really important part of this whole equation, and that is something that I call gray data. 126 00:11:23,603.2975 --> 00:11:34,73.2975 And gray data is the data that's in our heads, in our minds, which is what we use to make decisions that the AI models have zero knowledge of. 127 00:11:34,503.2975 --> 00:11:44,173.2975 And the only way, long term, an AI model will be able to replicate what you do, what I do, what anyone listening today does, is it has exactly what's up in your head. 128 00:11:44,453.2975 --> 00:11:45,273.2975 And it's not going to. 129 00:11:45,283.2975 --> 00:11:49,623.2975 We still know today in, in, in, you know, you're involved in SAP implementations all the time. 130 00:11:50,333.2975 --> 00:11:52,373.2965 It takes someone interpreting that data. 131 00:11:52,973.2975 --> 00:11:56,823.2975 Oftentimes to understand what it's saying and what cues it's giving. 132 00:11:57,183.2975 --> 00:12:01,343.2965 AI doesn't understand that because it's missing all the stuff that's in your gray space. 133 00:12:01,893.2975 --> 00:12:09,423.2975 And if that's the case, and how much, how much of the data that you use to run your enterprise is gray data versus bits and bytes. 134 00:12:09,663.2975 --> 00:12:18,593.2975 And if the answer is greater than 50%, 60%, 70%, wow, we got a lot of missing data and the model's not going to be that effective. 135 00:12:20,58.2965 --> 00:12:20,618.2965 For sure. 136 00:12:20,628.2965 --> 00:12:20,938.2965 For sure. 137 00:12:20,938.2965 --> 00:12:25,988.2975 I think it's an interesting point you mentioned about historical data and the quality of data. 138 00:12:26,28.2975 --> 00:12:28,658.2975 And that kind of leads me into this next conversation about. 139 00:12:29,413.2975 --> 00:12:52,53.2975 know, I'm, I'm an analytics person and data focus and, and, you know, it's all about good information will produce good results, right? So from that perspective, I'm curious, what are you seeing with ASOC members as it applies to their approach, you know, especially to real time data and analytics? And also the move to the cloud, because a lot of things are happening in the cloud. 140 00:12:52,83.2975 --> 00:13:26,578.2975 So what is your take on this whole space? It's Well, certainly I believe that if you are going to want to participate, play in a generative AI, AI space, and you say, and probably before you make that conclusion, you have to ask a question, which is where do you and your organization want to be on the innovation curve? Do you want to be on the very front of it? Do you want to be in the middle of it? You want to be, you know, where do you want to be now? If you want to be on the very, very back end of the innovation curve, continue doing what you're doing today. 141 00:13:27,523.2975 --> 00:13:32,73.2975 If you want to be to the middle of the innovation curve or the front end, and I think about it as a bell curve. 142 00:13:32,793.2975 --> 00:13:41,193.2475 If you want to be to the middle to the front end of that curve, and most people don't want to be at the front, that's a, that, you know, you got, you need a lot of courage and a lot of strength to be at. 143 00:13:41,193.2475 --> 00:13:46,343.2965 That's the scary place, but there are organizations that are there, right? Let's say you want to be safely in the middle. 144 00:13:46,363.2975 --> 00:13:47,373.2975 I don't want to lead the pack. 145 00:13:47,373.2975 --> 00:13:49,593.2975 I don't want to, I don't want to trail the pack right in the middle. 146 00:13:49,913.2975 --> 00:13:51,273.2975 It necessitates three things. 147 00:13:51,793.2975 --> 00:13:54,503.2975 I firmly believe Number one, you have to be in the cloud. 148 00:13:55,493.2975 --> 00:14:00,173.2975 Number two, You have to really think about your software investments as software as a service. 149 00:14:01,3.2975 --> 00:14:15,593.2975 Right? So you're moving the, the requirement to, for changes and updates to the software vendor in this, in this world, SAP, and number three, as little customization as possible. 150 00:14:16,253.2975 --> 00:14:32,213.2975 If you can do those three things and you can do them well, you have the greatest likelihood that you will be

able to take all this innovation, absorb it and go, which to your question is when you talk about analytics, when you talk about predictive analytics, that's what you're going to need. 151 00:14:33,713.2975 --> 00:14:40,283.2975 For many, many SAP customers, that is a tectonic shift in perspective. 152 00:14:40,703.2975 --> 00:14:43,383.2975 And there's certainly the longer you have been. 153 00:14:43,928.2975 --> 00:14:51,638.2975 SAP customer and the more customizations you have made, for whatever reason, your business process doesn't line up with SAPs. 154 00:14:52,498.2975 --> 00:14:54,988.2975 SAP didn't have a solution for you at the time. 155 00:14:55,168.2975 --> 00:15:04,688.2975 We talk about this thing of technical debt and where I quibble with some of the, the leading thought people is we tend to say and infer the technical debt is bad. 156 00:15:05,408.2975 --> 00:15:11,738.2975 Well, I don't think any of us as SAP practitioners wake up in the morning and say, today is the day I'm gonna build a lot of technical debt. 157 00:15:13,348.2975 --> 00:15:14,698.2975 There are some good reasons for it. 158 00:15:15,13.2975 --> 00:15:16,563.2975 There might be some bad reasons for it too. 159 00:15:16,563.2975 --> 00:15:17,603.2975 I don't know how to do something. 160 00:15:17,603.2975 --> 00:15:19,673.2975 So I'm just gonna, I'm just gonna code it. 161 00:15:20,133.2975 --> 00:15:20,733.2975 I get it. 162 00:15:21,93.2975 --> 00:15:21,323.2975 Right. 163 00:15:21,323.2975 --> 00:15:25,153.2965 But I don't necessarily believe that with a technical debt is something that we all strive for. 164 00:15:25,583.2975 --> 00:15:29,13.2975 You know, motherhood and apple pie, as few customizations as possible. 165 00:15:29,153.2975 --> 00:15:38,253.2975 The problem now is the stakes are way up because we've learned that you have to be in cloud, you have to be in SAS and you have to be almost no customization in order to adopt fast. 166 00:15:38,928.2975 --> 00:15:42,978.2975 And that means, right, that we have to be super careful about customization. 167 00:15:43,608.2975 --> 00:15:54,538.2975 That creates another problem inside most organizations, and that is how do you handle change control and how do you handle organizational change management? So the IT folks say, Hey, this is good for me. 168 00:15:54,558.2975 --> 00:15:55,618.2975 No customization. 169 00:15:55,638.2975 --> 00:15:56,408.2975 I'm good to go. 170 00:15:56,408.2975 --> 00:15:58,218.2975 And the business says, well, wait a minute here. 171 00:15:58,478.2975 --> 00:16:03,468.2975 I have to retrain thousands of people across 16 time zones in 32 different geographies. 172 00:16:03,788.2975 --> 00:16:04,798.2975 And that's hard. 173 00:16:05,198.2975 --> 00:16:06,38.2975 And it is. 174 00:16:06,508.2975 --> 00:16:07,398.2975 And it is. 175 00:16:07,488.2975 --> 00:16:15,78.2965 So how do we find that necessary balance? And I think if you've been on SAP a long time, that transition is not going to happen overnight. 176 00:16:15,88.2975 --> 00:16:20,448.2965 It's going to be multiple years, maybe even a decade, dare I say. 177 00:16:20,958.2975 --> 00:16:28,73.2975 And if you haven't started your S4 migration yet, You are fast running out of time. 178 00:16:28,703.2975 --> 00:16:35,183.2975 And so there's no time like the present to start working on that because absent that you were going to be perpetually behind. 179 00:16:35,573.2975 --> 00:16:39,803.2975 And I don't want to be cavalier here who stands there because what I just described is an Epic undertaking. 180 00:16:40,863.2975 --> 00:16:50,113.2975 Yes, If you get there, predictive analytics is super interesting, right? We have got to figure out a way to take our technology professionals and find ways for them to have more time. 181 00:16:50,623.2975 --> 00:16:54,893.2975 Because if we really want to do predictive analytics, it requires us to jump into data sets. 182 00:16:54,893.2975 --> 00:17:11,33.2975 It requires us to look at data, plant floor data, log data, all these other things where we haven't traditionally looked for things in order to find those patterns and those indications and those clues that help us sell more, get more efficient, do other things, right? And that requires time. 183 00:17:11,153.2975 --> 00:17:13,473.2975 And in order to get that time, we have to be more efficient. 184 00:17:13,473.2975 --> 00:17:18,993.2965 So if we're going to spend all of our time working on customizations of SAP, we are not going to be doing predictive analytics. 185 00:17:20,193.2975 --> 00:17:20,733.2965 for sure. 186 00:17:20,743.2975 --> 00:17:29,143.2975 And I think, I think that's one of the key, key points you mentioned about that, right? Stop spending time on doing things that are not adding any value, especially in this fast pace. 187 00:17:30,828.2975 --> 00:17:38,148.2975 Changing constantly on a daily basis and you put AI in the middle of all this, a sudden, you know your stakes are different. 188 00:17:38,158.2975 --> 00:17:40,68.2965 Your, your challenges are different. 189 00:17:40,98.2965 --> 00:17:45,198.2975 And at the same time, the time to make those decisions is shrinking for you. 190 00:17:45,198.2975 --> 00:17:59,68.2975 So for organizations to be nimble and be able to act quickly, all the things you just mentioned, I think they, Go hand in hand, especially a lot of times folks think about analytics as a, as a byproduct, right? It's after the fact. 191 00:17:59,108.2975 --> 00:18:25,268.2985 And then what we're thinking, or what are you talking here at this point is, Put analytics in front because that will drive that, that, that whole behavior of change of exactly what is important to me, you know, predictive is one part of it, you know, so many different aspects of information, which you can put your right brains and your geeks, right? I mean, has got geeks in the organization, right? I mean, you want

to put those folks to good use. 192 00:18:25,268.2985 --> 00:18:29,418.2985 And the best way you can do it is get ahead of the curve, right? Don't wait. 193 00:18:29,418.2985 --> 00:18:29,468.2985 Okay. 194 00:18:29,678.2985 --> 00:18:31,533.2985 Basically, that's A hundred, a hundred percent. 195 00:18:31,553.2975 --> 00:18:38,633.2975 And I'm excited about the potential of AI to help us migrate systems faster. 196 00:18:39,218.2985 --> 00:18:52,128.2985 I'd like to see us use AI to help understand quality in data, to help us understand how we lift and shift business processes out of legacy systems into new systems. 197 00:18:52,438.2975 --> 00:19:00,458.2975 I'd like to understand how we use AI to drive business test cases, quality assurance. 198 00:19:00,528.2975 --> 00:19:02,428.2975 I, I, I believe. 199 00:19:04,428.2985 --> 00:19:09,38.2985 We are at a massive inflection point where the upgrading of these systems. 200 00:19:09,38.2985 --> 00:19:11,788.2985 If you asked a question earlier about digital transformation. 201 00:19:12,158.2985 --> 00:19:12,568.2985 Right. 202 00:19:12,888.2985 --> 00:19:15,818.2985 We have to move to the next generation of SAP software. 203 00:19:15,818.2985 --> 00:19:19,108.2985 I believe that unlocks the gateway to everything we're talking about today. 204 00:19:19,588.2985 --> 00:19:21,418.2985 That cannot be a five year project. 205 00:19:21,738.2975 --> 00:19:40,218.2985 We have got to figure out as technology professionals, how to automate it, how to make it faster, how to do it faster, and how to make sure we can get an unlock value faster is my biggest ask of SAP and in conversations that I have with their CEO and their leadership team, please stop making new SKUs. 206 00:19:40,643.2985 --> 00:19:42,963.2985 For new software licenses. 207 00:19:42,983.2985 --> 00:19:50,513.2985 I, I, I implore you to make your software easier to migrate and uplift and move to the next generation. 208 00:19:50,513.2985 --> 00:19:55,423.2985 And can we use some of these AI ML tools to achieve that? It's essential. 209 00:19:56,218.2985 --> 00:19:56,788.2985 For sure. 210 00:19:56,858.2985 --> 00:19:57,638.2985 No, for sure. 211 00:19:57,998.2985 --> 00:19:58,218.2985 No. 212 00:19:58,218.2985 --> 00:20:04,78.1985 And I think about all this technology and SAP, I mean, let's come back to our conversation ASOC. 213 00:20:04,78.2985 --> 00:20:06,793.2975 ASOC is Hey, so fantastic. 214 00:20:06,898.2975 --> 00:20:13,738.2985 start in 2024, right? I mean, personally, I know we had over 150 people at our Michigan chapter meeting back in February. 215 00:20:14,908.2985 --> 00:20:23,753.2975 And, and yeah, that is absolutely is, is amazing, right? And so what can ASEC members expect this year from their membership? Can you, can you delve into that? 100%. 216 00:20:23,783.2975 --> 00:20:27,833.2985 First and foremost, I think you said the most important thing where we're seeing the most interest. 217 00:20:27,833.2985 --> 00:20:31,233.2985 The most excitement is in our 39 chapters. 218 00:20:32,368.2985 --> 00:20:41,168.2985 So if you are an SAP professional and you want to be at the top of your game and you want to learn, connect and grow, you don't have to jump on an airplane. 219 00:20:41,178.2985 --> 00:20:42,868.2985 Of course, we're welcome you to do that. 220 00:20:42,868.2985 --> 00:20:48,358.2985 You don't have to spend hotel room nights, go to your local ASAP chapter and become involved. 221 00:20:48,758.2985 --> 00:20:54,128.2985 You will meet people like you who want to get ahead and understand how to solve problems using SAP. 222 00:20:54,878.2985 --> 00:21:00,428.2985 And I, you know, your, You're in the middle of the, of the Michigan, you know, SCP scene. 223 00:21:00,428.2985 --> 00:21:01,448.2985 It's amazing. 224 00:21:01,988.2985 --> 00:21:13,48.2985 So go spend time there, which is a huge pitch for what you do and why you volunteer is because you want to be part of, you know, what's happening on the ground, real time in geography. 225 00:21:13,88.2975 --> 00:21:15,688.2975 And that is what the chapter organization is here to do. 226 00:21:15,898.2985 --> 00:21:18,78.2985 And I would really like to see that over the next. 227 00:21:18,493.2985 --> 00:21:21,563.2985 Three to five years grow to epic proportions. 228 00:21:21,853.2985 --> 00:21:22,833.2985 I have a challenge. 229 00:21:22,853.2985 --> 00:21:26,563.2985 I want to see your Michigan meeting, not be just 150 people. 230 00:21:26,803.2985 --> 00:21:29,143.2985 I want it to be 350 people. 231 00:21:29,743.2985 --> 00:21:52,63.2985 That to me is exciting, which is a very different change of, of perspective from us, but I think in a post pandemic world, what a great opportunity to get out from behind your laptop and whether you're back in the office or still working remotely, go spend time with your friends in a, in an ASIC chapter event in Michigan or in California or in Florida, pick, pick a place and just go and have fun and meet, meet your peers. 232 00:21:52,73.2985 --> 00:21:53,673.2985 It'll be so wonderful for you. 233 00:21:53,973.2985 --> 00:21:55,403.2985 If that's not good enough. 234 00:21:56,108.2985 --> 00:22:00,808.2985 Then enjoy some of the other events that we do get online and do some research and education there. 235 00:22:01,168.2985 --> 00:22:08,838.2985 We'll be, we have you know, ASUG annual conference and SAP Sapphire coming up in June in the, in the fall, we have SAP for utilities. 236 00:22:08,858.2985 --> 00:22:15,108.2985 We have ASUG best practices, which is a whole source of, of industry based events, and then we cap off the year. 237 00:22:15,438.2985 --> 00:22:17,78.2985 This is my most exciting event. 238 00:22:17,898.2985 --> 00:22:22,658.2985 We cap off the year in West Palm beach, Florida, November 12th through 14th with ASUG TechConnect. 239 00:22:23,228.2985 --> 00:22:26,808.2985 It used to be called

TechEd, but we've kind of reconfigured TechEd with SAP. 240 00:22:27,328.2985 --> 00:22:31,298.2985 So TechEd is a virtual program, but in North America, it's ASUG TechConnect. 241 00:22:31,308.2985 --> 00:22:41,478.2995 So if you want to wrap up 2023, sorry, 2024, getting my years all confused and get ready for an amazing 2025, ASUG TechConnect is the place to be. 242 00:22:41,818.2975 --> 00:22:42,848.2975 And I think those are fine. 243 00:22:43,83.2985 --> 00:22:46,753.2985 What else can you do? First five newsletter comes out every Monday morning. 244 00:22:46,883.2985 --> 00:22:52,983.2985 It is an amazing place to just get a recap of the top five articles that happened in the SAP ecosystem over the last week. 245 00:22:53,918.2985 --> 00:22:54,468.2985 Podcasts. 246 00:22:54,478.2985 --> 00:22:55,798.2985 You and I are in a podcast today. 247 00:22:55,798.2985 --> 00:22:56,368.1985 Everyone's doing podcast. 248 00:22:56,368.2985 --> 00:22:58,818.2985 ASUG does podcasts, man. 249 00:22:58,918.2985 --> 00:22:59,498.2985 Be there. 250 00:22:59,638.2985 --> 00:23:10,138.397 Let's get together at Campus Connect, right? Citadel University University of Texas at Dallas, Fayetteville State University and then my favorite Michigan State University. 251 00:23:10,178.397 --> 00:23:14,608.398 There's my last plug for go green are all very much in the Campus Connect program. 252 00:23:14,608.398 --> 00:23:22,18.398 What a great way to have this next generation of talent, get excited about the careers that we've been so fortunate to have in the SAP ecosystem. 253 00:23:23,28.398 --> 00:23:23,718.397 No, for sure. 254 00:23:23,718.398 --> 00:23:27,78.398 I think there is a lot to learn and it's the best thing about it. 255 00:23:27,78.398 --> 00:23:30,933.398 Like you said, there's so many mediums, like, you know, you pick what makes You bet. 256 00:23:31,88.398 --> 00:23:35,508.397 know what, what really floats your boat, especially after the pandemic. 257 00:23:35,508.497 --> 00:23:41,908.398 A lot of folks are open to coming out and meeting others and kind of getting to know what's coming. 258 00:23:42,228.398 --> 00:23:42,978.398 Excited. 259 00:23:43,118.398 --> 00:23:44,478.398 No, it this way. 260 00:23:44,568.398 --> 00:23:47,608.398 You know, excitement is one thing you get to meet people. 261 00:23:47,888.398 --> 00:23:48,963.398 And Just meet people. 262 00:23:49,518.398 --> 00:23:51,988.398 either it's in person or you're traveling somewhere else. 263 00:23:52,98.398 --> 00:23:54,498.398 Or I you mentioned, June and June, big event. 264 00:23:54,888.398 --> 00:24:04,208.398 A lot of new things are being shared and you understand and know exactly where SAP is going, where ASIC wants to take you in your journey. 265 00:24:04,268.398 --> 00:24:09,603.398 And as an organization, you want to learn from your peers, right? And that's the best so. 266 00:24:10,328.398 --> 00:24:17,148.398 And one thing I like about your, your, your plug for the you know, the, the November event, cannot go wrong with it. 267 00:24:17,593.398 --> 00:24:21,888.398 No, It's like you, you end your year on something that you really want to take into next year. 268 00:24:22,148.398 --> 00:24:24,768.397 And that kind of sets your basis for exactly what you want to do. 269 00:24:24,768.398 --> 00:24:26,328.397 So a lot of opportunities. 270 00:24:26,328.398 --> 00:24:29,308.398 I really love the whole platform that you kind of explained so well. 271 00:24:29,923.398 --> 00:24:30,423.398 thank you. 272 00:24:30,433.398 --> 00:24:44,523.398 And if you, there is a lot going on inside the SAP ecosystem, it is a wonderful place for professionals like you, me, and everyone else, 130, 000 of us in North America to make our home, to learn, connect, grow, to thrive. 273 00:24:44,973.397 --> 00:24:58,408.398 And all you got to do is just, you know, raise your hand and go to a chapter meeting, you know, meet with people outside of your, your, Your standard core team that you might be working on SCP for, and the whole world will be unlocked for you. 274 00:24:58,408.398 --> 00:25:00,878.398 And it'll make you feel like what you're doing has value. 275 00:25:01,168.398 --> 00:25:05,398.398 You know, that the things you're learning can have a place in this broader ecosystem. 276 00:25:05,658.398 --> 00:25:11,788.398 We are going to need a lot more talent who stands there in the next 10 years than we have today. 277 00:25:12,98.398 --> 00:25:14,958.398 It frightens me about how much change is happening. 278 00:25:14,968.398 --> 00:25:19,208.398 And I believe we are all find very rewarding careers inside of SAP. 279 00:25:20,358.398 --> 00:25:24,858.398 Now, I think the future is really bright and, and I know we can, we can talk for hours, Geoff. 280 00:25:24,878.398 --> 00:25:26,868.398 I We could, your knowledge, your. 281 00:25:27,163.398 --> 00:25:28,738.398 Your, your passion so kind. 282 00:25:29,123.398 --> 00:25:54,503.497 and SAP I'd like to leave with this one question for you, as far as topics and discussions you covered, what is the one key takeaway that you want our listeners to, to leave with? I believe the key takeaway today is generative AI is real and the faster you get in and start contemplating what it can and can't do, we are trying inside of ASUG lots of different technologies and we're fiddling and we have this kind of, you know, experimental culture. 283 00:25:54,503.498 --> 00:25:55,773.497 Let's go try some stuff. 284 00:25:56,893.498 --> 00:25:57,563.497 It's good. 285 00:25:57,703.497 --> 00:26:03,503.498 And I think, you know, we are, we're doing a lot with text, you know, video to text recaps. 286 00:26:03,708.498 --> 00:26:04,498.498 Things like that. 287 00:26:04,508.498 --> 00:26:07,348.498 I think there's a ton of upside to all of this. 288 00:26:07,348.498 --> 00:26:09,258.498 Go get yourself immense in AI. 289 00:26:12,263.498 --> 00:26:13,383.498 I think that's, great advice. 290 00:26:13,383.498 --> 00:26:28,318.498 And it seems like a lot of folks who are still on on the edges, you know, it's time for them to kind of move on on this bandwagon because this, this train has started rolling and there's no stopping, at least I don't see it in the near future, Today is the worst day AI ever will be. 291 00:26:28,328.498 --> 00:26:31,738.498 It will get better from here and it's going to be on an exponential scale. 292 00:26:31,898.497 --> 00:26:34,608.498 So don't wait another three, four weeks or months or years. 293 00:26:34,628.498 --> 00:26:35,338.498 Get in now. 294 00:26:37,23.498 --> 00:26:37,823.498 great advice. 295 00:26:37,883.498 --> 00:26:38,453.498 Thank you. 296 00:26:38,708.497 --> 00:26:39,218.498 Thank you. 297 00:26:39,773.498 --> 00:26:40,568.498 an Thank you. 298 00:26:41,843.498 --> 00:26:43,53.498 I really enjoyed the talk. 299 00:26:43,123.498 --> 00:26:51,193.497 And I would love to get you back, you know in the Whenever you need, more feedback on how things have settled down, once we traverse through the 2024. 300 00:26:51,973.498 --> 00:26:59,553.498 we are here for you and we, I appreciate greatly everything you do for the community, for the SAP community, for ASUG and everything you do in Michigan. 301 00:26:59,703.498 --> 00:27:00,63.498 Thank you. 302 00:27:00,123.498 --> 00:27:01,33.498 Thank you so much. 303 00:27:05,225.8147815 --> 00:27:09,815.8147815 Thanks for listening to Tech Driven Business, brought to you by Innovative Solution Partners. 304 00:27:10,475.8147815 --> 00:27:14,675.8147815 Geoff delved deep into the transformative power of gen ai. 305 00:27:15,115.8147815 --> 00:27:21,775.8147815 He shared valuable insights on how organizations can transform business with generative ai. 306 00:27:22,375.8147815 --> 00:27:25,545.8147815 His main takeaway, generative AI is real. 307 00:27:26,145.8147815 --> 00:27:28,535.8147815 Go get yourself immersed in AI as today as. 308 00:27:28,535.8147815 --> 00:27:31,305.8147815 today is the worst day AI will ever be. 309 00:27:32,195.8147815 --> 00:27:33,345.8147815 We'd love to hear from you. 310 00:27:33,685.8147815 --> 00:27:37,65.8147815 Continue the conversation by connecting with me on LinkedIn or Twitter. 311 00:27:37,665.8147815 --> 00:27:43,745.8137815 Learn more about Innovative Solution Partners and schedule a free consultation by visiting isolutionpartners. 312 00:27:44,5.8147815 --> 00:27:44,215.8147815 com. 313 00:27:45,15.8147815 --> 00:27:48,575.8137815 Never miss a podcast by subscribing to our YouTube channel. 314 00:27:49,95.8137815 --> 00:27:50,745.6527815 Information is in the show notes.