1 00:00:00,320 --> 00:00:02,440 Hello, everyone. Thank you so much for joining us 2 00:00:02,440 --> 00:00:05,000 on this Professional Pricing Society podcast. 3 00:00:05,000 --> 00:00:08,760 My name is Terrence, and it's a great time to have another 4 00:00:08,760 --> 00:00:12,880 discussion, especially about AI. And so we have two very special 5 00:00:12,880 --> 00:00:15,480 guest speakers with us today who's going to be diving into 6 00:00:15,480 --> 00:00:18,760 this topic with us about what does it take to be successful 7 00:00:19,000 --> 00:00:22,880 with AI. Today we have Kavya Merleder who 8 00:00:22,880 --> 00:00:27,040 is the product Manager at PROS. She leads strategy and 9 00:00:27,040 --> 00:00:30,400 implementation for AI based pricing optimization software 10 00:00:30,640 --> 00:00:33,280 used by some of the largest enterprises in the world. 11 00:00:33,720 --> 00:00:38,160 And we also have Heather Ritchie who is a strategic consultant 12 00:00:38,320 --> 00:00:40,520 with PROS as well. And you guys are going to be 13 00:00:40,520 --> 00:00:42,800 diving into this conversation with AI and we're very excited 14 00:00:42,800 --> 00:00:44,640 to have you. First and foremost, I want to 15 00:00:44,640 --> 00:00:46,360 ask before we start, how are we doing today? 16 00:00:46,440 --> 00:00:49,240 Everybody doing good so far. Really fantastic. 17 00:00:49,240 --> 00:00:50,440 I. Need to be here. 18 00:00:50,760 --> 00:00:51,400 Good. Good. 19 00:00:51,480 --> 00:00:53,960 Glad to have you guys. So we'll just start with the 20 00:00:53,960 --> 00:00:57,160 very first question, then you guys can take the time to, you 21 00:00:57,160 --> 00:01:00,120 know, answer these as best as you can. 22 00:01:00,120 --> 00:01:02,760 But what I want to ask, what is the first thing that you want 23 00:01:02,760 --> 00:01:06,240 people to know about AI and artificial intelligence? 24 00:01:06,240 --> 00:01:08,880 Where should we start our approach and our thinking when 25 00:01:08,880 --> 00:01:11,720 it comes to AI? Yeah, Terrence, I can take that 26 00:01:11,720 --> 00:01:13,640 one. So I think the first thing that 27 00:01:13,640 --> 00:01:17,800 I'd want people to know about AI is that it's it's not new. 28 00:01:18,640 --> 00:01:21,320 There's this idea and it's feeling out right now in the 29 00:01:21,320 --> 00:01:25,200 general public that just because the public is finding 30 00:01:25,280 --> 00:01:29,600 applications for AI, that this must mean that it's it's new and 31 00:01:29,600 --> 00:01:32,600 thus it's inherently risky. But companies have been 32 00:01:32,600 --> 00:01:37,000 developing and implementing AI for decades, specifically in the 33 00:01:37,040 --> 00:01:39,240 pricing space. You know, Kavya and I both work 34 00:01:39,240 --> 00:01:43,000 for Pros and Pros released their first AI pricing product back in 35 00:01:43,000 --> 00:01:45,080 the 80s. So there's been decades of 36 00:01:45,080 --> 00:01:47,960 development and research that have gone into these types of 37 00:01:47,960 --> 00:01:51,600 products so people can be confident in the results, in the 38 00:01:51,640 --> 00:01:54,960 AI results in this application. Yeah, and that's a good point. 39 00:01:54,960 --> 00:01:58,000 Artificial artificial intelligence is not new, so I'm 40 00:01:58,000 --> 00:02:00,360 glad you made that apparent for for listeners. 41 00:02:00,960 --> 00:02:02,760 And I can add a little more to that. 42 00:02:02,760 --> 00:02:06,800 I think one of the important things to know about AI is that 43 00:02:06,800 --> 00:02:09,320 ultimately it's a reflection of humanity. 44 00:02:09,680 --> 00:02:12,720 I think that a lot of responses we've seen towards AI include 45 00:02:12,720 --> 00:02:15,840 both. Excitement about its potential 46 00:02:15,840 --> 00:02:19,600 as well as fear and nervousness. And I actually think both of 47 00:02:19,600 --> 00:02:22,880 those can be valid responses because I think that is a 48 00:02:22,880 --> 00:02:26,760 reflection of humanity's intentions and the potential we 49 00:02:26,760 --> 00:02:32,680 have to do both to do both good and as well as be self-serving. 50 00:02:33,600 --> 00:02:37,520 But I think that the what's really exciting about this 51 00:02:37,520 --> 00:02:40,960 conversation is US collectively developing ways. 52 00:02:41,480 --> 00:02:46,000 To use AI as a tool for good, as an and as a tool for helping us 53 00:02:46,760 --> 00:02:50,360 save time, progress to a better place, even save lives. 54 00:02:50,360 --> 00:02:54,560 So I'm really excited about the ways that can help us move 55 00:02:54,560 --> 00:02:58,480 forward as as a community and as a planet. 56 00:03:00,080 --> 00:03:04,680 OK, that's good. Yeah, AI, you know, it is a 57 00:03:04,680 --> 00:03:07,440 means of being more efficient in a lot of aspects. 58 00:03:07,720 --> 00:03:11,120 Now, since this is a pricing podcast, you know if a company 59 00:03:11,120 --> 00:03:14,640 was looking for an AI solution to fit their pricing 60 00:03:14,640 --> 00:03:18,120 application, what should they consider when evaluating 61 00:03:18,520 --> 00:03:23,760 different AI solutions? Yeah, Terence, great question. 62 00:03:23,760 --> 00:03:26,920 We see this a lot with the people that we work with. 63 00:03:27,440 --> 00:03:31,840 I think ultimately when you look at what AI is, it is a system 64 00:03:31,840 --> 00:03:35,800 that takes in data. It processes the data and learns 65 00:03:35,800 --> 00:03:39,240 from it and it provides outputs along with. 66 00:03:40,080 --> 00:03:44,840 A software or a system around it to help you interact with those 67 00:03:44,840 --> 00:03:47,680 outputs. So I think when you're a company 68 00:03:47,680 --> 00:03:53,400 that is looking at how to evaluate what to look for in an 69 00:03:53,400 --> 00:03:58,080 AI solution, you're really looking for, you're really 70 00:03:58,080 --> 00:04:01,200 looking for those same three elements. 71 00:04:01,440 --> 00:04:04,960 You're looking for what data it takes in, what it does with the 72 00:04:04,960 --> 00:04:09,400 data, and how it does it. So you know, diving into each of 73 00:04:09,400 --> 00:04:13,320 these, looking at the data that it takes in a lot of companies 74 00:04:13,320 --> 00:04:16,959 and organizations may have had a lot of difficulty with 75 00:04:16,959 --> 00:04:20,240 collecting data as well as with having the data in a very 76 00:04:20,240 --> 00:04:22,320 specific format. So you really want to look for 77 00:04:22,320 --> 00:04:26,280 solutions that are very flexible with their schema of data, that 78 00:04:26,280 --> 00:04:30,160 have integrations with different CRMS and ER, PS, so you're able 79 00:04:30,160 --> 00:04:33,520 to flow your data in easily and have a lot of automation built 80 00:04:33,520 --> 00:04:35,120 in, whether that's automation around. 81 00:04:35,600 --> 00:04:39,360 Data cleansing, automation around scheduling, how often 82 00:04:39,360 --> 00:04:44,200 your data comes in or or any modifications you want to do to 83 00:04:44,200 --> 00:04:48,280 your data before it feeds into the AI with what it does with 84 00:04:48,280 --> 00:04:50,040 the data. This is really the heart of the 85 00:04:50,240 --> 00:04:53,240 AI, and I think this is really important to know that not all 86 00:04:53,440 --> 00:04:56,600 AI is equal, and AI doesn't just mean one thing. 87 00:04:57,680 --> 00:05:01,400 So when you look at what an AI system actually does with the 88 00:05:01,400 --> 00:05:04,760 data, it is really important to dive in a step further and 89 00:05:04,760 --> 00:05:10,200 consider is it, is it a solution that is really good at 90 00:05:10,200 --> 00:05:13,840 accounting for sparsity? Is it a solution that is really 91 00:05:13,840 --> 00:05:17,080 that, that is actually using AI to look at trends over time, 92 00:05:17,080 --> 00:05:19,800 which is really important as we've seen in pricing in the 93 00:05:19,800 --> 00:05:23,880 last few years? How does it navigate elements 94 00:05:23,880 --> 00:05:26,800 that are really changing frequently, like changing costs 95 00:05:26,800 --> 00:05:30,560 or changing market indices? And what scientific measures is 96 00:05:30,560 --> 00:05:32,760 it using? Is it using the most recent, the 97 00:05:32,760 --> 00:05:36,280 most innovative solutions that show you the best statistical 98 00:05:36,280 --> 00:05:40,000 measures of success? So we really urge customers to 99 00:05:40,000 --> 00:05:43,920 look into these questions and evaluate AI for what it really 100 00:05:43,920 --> 00:05:45,640 is doing. I think that's not. 101 00:05:46,080 --> 00:05:48,640 That's not a mystery. It is doing what humans can do, 102 00:05:48,640 --> 00:05:51,680 but better, and it's really important to ask those questions 103 00:05:52,320 --> 00:05:56,360 and look at what it's attempting to do with the data to get these 104 00:05:56,360 --> 00:05:59,600 to these outputs. And I think finally, this last 105 00:05:59,600 --> 00:06:03,000 point is often something that isn't given very much importance 106 00:06:03,000 --> 00:06:07,000 in the conversation around AI. But when you're a company that's 107 00:06:07,000 --> 00:06:10,640 actually using AI software for pricing or for anything else, 108 00:06:11,040 --> 00:06:14,680 looking at the surrounding system, at the how is really 109 00:06:14,680 --> 00:06:18,200 important. So not only do you want your AI 110 00:06:18,200 --> 00:06:22,480 to be giving you the perfect best results, but you also want 111 00:06:22,480 --> 00:06:24,640 a system that is really explainable. 112 00:06:24,640 --> 00:06:28,120 You want your user to be able to go in and actually understand 113 00:06:28,120 --> 00:06:30,200 why the AI is recommending what it does. 114 00:06:30,520 --> 00:06:33,320 You want them to be able to go in and provide feedback to the 115 00:06:33,320 --> 00:06:37,040 system, or even be able to change certain parameters to be 116 00:06:37,040 --> 00:06:39,560 able to change the data that's going going in. 117 00:06:40,120 --> 00:06:42,400 If they have a new strategic Business School, you want them 118 00:06:42,400 --> 00:06:44,800 to be able to apply that by themselves. 119 00:06:45,520 --> 00:06:48,760 So looking at self-service, transparency, explain ability, 120 00:06:49,080 --> 00:06:52,520 all of these are really key to being able to use AI 121 00:06:52,520 --> 00:06:55,560 successfully and I would say are part of the AI software as a 122 00:06:55,560 --> 00:06:58,320 whole. So when you look at these three, 123 00:06:58,320 --> 00:07:02,960 I think you've really done a comprehensive view into into 124 00:07:02,960 --> 00:07:05,080 understanding what an AI system can offer you. 125 00:07:05,080 --> 00:07:11,840 That's good that there's a lot that goes into AI. 126 00:07:12,400 --> 00:07:15,560 And based on my understanding of what you just said, there's 127 00:07:15,560 --> 00:07:18,560 different levels or different types of AI. 128 00:07:19,480 --> 00:07:22,680 And then of course, I would imagine different companies can 129 00:07:22,680 --> 00:07:26,520 use AI for different reasons. But the three different points 130 00:07:26,520 --> 00:07:28,960 you made made mention of, you know, that makes a whole lot of 131 00:07:28,960 --> 00:07:32,640 sense to intake the data, what it what it uses the data for and 132 00:07:32,640 --> 00:07:35,600 how does it present the output. So it's a lot to think about 133 00:07:35,600 --> 00:07:39,520 when when considering AI. And then you have to also ask 134 00:07:39,520 --> 00:07:42,560 yourself the question as a company, how do you even no you 135 00:07:42,560 --> 00:07:44,840 are ready to use AI as a company? 136 00:07:45,880 --> 00:07:48,520 That's a great question to think about right now because everyone 137 00:07:48,520 --> 00:07:50,960 wants to use it, but not everyone's necessarily ready to 138 00:07:50,960 --> 00:07:52,640 use it. Because I think it's all about 139 00:07:52,720 --> 00:07:55,840 really the the mindset of of your company. 140 00:07:56,160 --> 00:07:59,040 You know once you've identified a part of your business that 141 00:07:59,360 --> 00:08:04,000 that can be improved with AI. You also need to identify at a 142 00:08:04,000 --> 00:08:08,000 high level what type of data you you might need to feed into the 143 00:08:08,000 --> 00:08:11,480 tool. Who's going to be impacted by by 144 00:08:11,480 --> 00:08:15,840 this process change and are you as a company open to that 145 00:08:15,840 --> 00:08:18,280 change? You know, knowing what type of 146 00:08:18,280 --> 00:08:21,520 data you need, Again, at a very high level it tells you what 147 00:08:21,520 --> 00:08:26,840 teams need to be involved. So knowing this will let you 148 00:08:26,840 --> 00:08:29,920 know at you know the very beginning who to get in contact 149 00:08:29,920 --> 00:08:34,320 with, who to bring into certain key meetings, and who you need 150 00:08:34,320 --> 00:08:36,799 to get buy in from. You know at the at the 151 00:08:36,799 --> 00:08:39,720 beginning. You also need to know who's 152 00:08:39,720 --> 00:08:43,960 going to be impacted by this change because it lets you know 153 00:08:43,960 --> 00:08:46,800 who's going to be available for more high value work. 154 00:08:47,200 --> 00:08:52,720 So once you have an AI solution actually in place, it's going to 155 00:08:53,840 --> 00:08:58,400 speed up many of your processes. It's going to take up the place 156 00:08:58,400 --> 00:09:01,360 of some of those those processes and that means that your team 157 00:09:01,360 --> 00:09:05,040 can actually spend time working on things like analytics and 158 00:09:05,040 --> 00:09:08,280 strategy and those really like high value, high level thinking. 159 00:09:08,960 --> 00:09:11,880 Detailed work. So I think that's really 160 00:09:11,880 --> 00:09:15,360 important to understand who's going to impacted by that that 161 00:09:15,360 --> 00:09:19,680 you can start working on additional future projects that 162 00:09:20,320 --> 00:09:23,160 you know can take advantage of those skills and that can be 163 00:09:23,160 --> 00:09:27,040 used for motivation for for these types of projects. 164 00:09:28,520 --> 00:09:31,760 And then my last my last thing I think that you need to consider 165 00:09:31,760 --> 00:09:34,440 is you know are you open to change. 166 00:09:34,600 --> 00:09:37,280 So it's really important for these types of. 167 00:09:38,040 --> 00:09:42,120 Those these types of projects that your teams are are open to 168 00:09:42,120 --> 00:09:44,760 change and that's not only the change in their current process 169 00:09:45,040 --> 00:09:49,400 but also potential changes to strategy to the new processes 170 00:09:49,400 --> 00:09:53,080 that will be built. You know based on your your 171 00:09:53,080 --> 00:09:57,440 findings from AI. One of my favorite things that I 172 00:09:57,440 --> 00:10:01,000 that I loved when I was actually on the implementation side of of 173 00:10:01,000 --> 00:10:05,080 the house was being a part of an AI pricing implementation when 174 00:10:05,080 --> 00:10:08,680 the customers would. Understand something new or 175 00:10:08,680 --> 00:10:12,960 learn something new from the results that they probably 176 00:10:12,960 --> 00:10:15,440 couldn't have, you know, easily found those connections 177 00:10:15,440 --> 00:10:17,720 otherwise. So seeing connections in their 178 00:10:17,720 --> 00:10:21,120 data that actually influenced, influenced a change in their 179 00:10:21,120 --> 00:10:24,440 strategy that they then implemented and then that fed 180 00:10:24,440 --> 00:10:27,720 back into the AI tools. So just being really open to 181 00:10:27,720 --> 00:10:30,880 change, open to to the results and what you can do with them I 182 00:10:30,880 --> 00:10:34,920 think is extremely helpful that's that's really good and 183 00:10:34,920 --> 00:10:39,160 it's, it's unfortunate but a lot of companies may not be open to 184 00:10:39,160 --> 00:10:44,320 change and that's a real thing. But as we know the I guess 185 00:10:44,320 --> 00:10:47,080 companies or even just the individual people who are open 186 00:10:47,080 --> 00:10:51,400 to change are the typically the ones that are going to be more 187 00:10:51,400 --> 00:10:55,160 successful than than not because they are adjusting to the 188 00:10:55,160 --> 00:10:59,360 changing Times. Now when you think about change 189 00:10:59,880 --> 00:11:03,200 and transformations, what can companies do to be successful 190 00:11:03,560 --> 00:11:06,160 regarding their digital transformation of the growing 191 00:11:06,160 --> 00:11:08,560 and changing times? Yeah, my last response, I 192 00:11:08,560 --> 00:11:11,800 mentioned teams countless times, mentioned a bunch of different 193 00:11:11,800 --> 00:11:15,160 teams. It's critical that you have 194 00:11:15,160 --> 00:11:18,520 alignment like alignment is key for digital transformation and 195 00:11:18,520 --> 00:11:21,600 you need both vertical and horizontal alignment. 196 00:11:22,080 --> 00:11:25,680 So the goals of your your AI project within this digital 197 00:11:25,680 --> 00:11:30,040 transformation, they need to align with the vertical goals of 198 00:11:30,040 --> 00:11:33,120 your company, because otherwise they they won't be successful. 199 00:11:33,720 --> 00:11:37,160 But you also need to have alignment between your teams 200 00:11:37,240 --> 00:11:41,320 horizontally. So if your sales team has has 201 00:11:41,360 --> 00:11:45,840 one goal with how to use AI, but it needs to be integrated into 202 00:11:45,840 --> 00:11:50,040 your architecture that your IT team is in charge of. 203 00:11:50,600 --> 00:11:52,840 Well, you need to make sure that the goals of your IT team 204 00:11:53,560 --> 00:11:56,240 aligned with you know, this tool that you want to implement for 205 00:11:56,240 --> 00:11:58,880 sales or for you know another part of of your business. 206 00:11:58,880 --> 00:12:03,520 So as long as you have this, this, this horizontal alignment, 207 00:12:03,520 --> 00:12:06,400 you can really avoid those conflicting initiatives that I 208 00:12:06,400 --> 00:12:10,440 see, you know teams not be as successful with a digital 209 00:12:10,480 --> 00:12:13,200 transformation. And also having specifically 210 00:12:13,200 --> 00:12:16,960 that horizontal alignment, it really helps your team build 211 00:12:17,000 --> 00:12:20,000 build trust. And you can't have success 212 00:12:20,000 --> 00:12:22,640 without that, that trust between the different teams. 213 00:12:24,600 --> 00:12:26,400 Yeah. And I I think to add to 214 00:12:26,400 --> 00:12:30,800 Heather's point, it's really important to trust your team 215 00:12:30,800 --> 00:12:33,560 members. I think an example of a negative 216 00:12:33,560 --> 00:12:37,440 digital transformation would be just going in and insisting that 217 00:12:37,440 --> 00:12:42,240 all of your teams constantly and only do exactly what this new 218 00:12:42,240 --> 00:12:45,880 technology tells them without really taking their expertise. 219 00:12:46,360 --> 00:12:49,960 Their intentions, their motivations and what they've 220 00:12:50,040 --> 00:12:52,240 learned so far in their trade into account. 221 00:12:52,840 --> 00:12:55,320 And I think a lot of when we look at other industries that 222 00:12:55,480 --> 00:12:59,800 have adopted AI successfully, for example, you can look at 223 00:13:00,480 --> 00:13:04,840 healthcare where physicians may use AI to help detect cancerous 224 00:13:04,840 --> 00:13:07,800 lesions. This isn't a replacing of a 225 00:13:07,800 --> 00:13:10,160 physician with AI, absolutely not. 226 00:13:11,040 --> 00:13:15,080 What this is really doing is. Using AI to help make their work 227 00:13:15,080 --> 00:13:19,000 faster, more efficient and being able to point out things that 228 00:13:19,000 --> 00:13:22,400 maybe they may not have noticed, but you still continue to give 229 00:13:22,400 --> 00:13:25,480 power to the physician to interpret and to make the final 230 00:13:25,480 --> 00:13:27,680 decision. So in this case, an AI system 231 00:13:27,680 --> 00:13:32,320 may be able to point out certain variables that in an MRI image 232 00:13:32,320 --> 00:13:36,320 that may be suspicious or may point towards it being a 233 00:13:36,320 --> 00:13:39,040 cancerous lesion, but the physician really gets to look at 234 00:13:39,040 --> 00:13:42,000 that in context of the patient overall and. 235 00:13:42,360 --> 00:13:44,560 Interpret it and make that final call. 236 00:13:44,960 --> 00:13:49,920 I think we've seen similar examples with IBM Watson IoT, 237 00:13:49,920 --> 00:13:56,520 for example, where IBM Watson is used to be able to identify if 238 00:13:56,520 --> 00:14:00,680 certain machines or equipment may be malfunctioning and any 239 00:14:00,680 --> 00:14:03,120 prescriptive guidance that a technician might do. 240 00:14:03,440 --> 00:14:07,440 But again, it's providing recommendations as to certain 241 00:14:07,440 --> 00:14:11,200 action steps that they can take along with a confidence score 242 00:14:11,200 --> 00:14:13,960 and along with. The reasons why it thinks those 243 00:14:13,960 --> 00:14:16,720 are the right action and the technician can look at those, 244 00:14:16,880 --> 00:14:18,880 they're definitely probably going to see things that maybe 245 00:14:18,880 --> 00:14:21,000 they hadn't, they wouldn't have been able to come up with by 246 00:14:21,000 --> 00:14:24,840 themselves, but now they have this tool that's really 247 00:14:24,960 --> 00:14:28,120 providing them that experience. At the same time, the technician 248 00:14:28,120 --> 00:14:32,160 really is there in that moment, and they have the power to 249 00:14:32,440 --> 00:14:35,640 really use that AI, interpret it according to their expertise, 250 00:14:35,960 --> 00:14:39,480 and make the final call. And we see this a lot with you 251 00:14:39,480 --> 00:14:42,240 know, successful pricing digital transformations as well. 252 00:14:42,520 --> 00:14:45,640 This isn't a replacing of the pricing team and we never want 253 00:14:45,640 --> 00:14:47,480 it to be. We never expected to be 254 00:14:47,480 --> 00:14:52,200 presented as such. It is a tool to help our pricing 255 00:14:52,200 --> 00:14:55,760 and our sales teams really be able to use the power of data, 256 00:14:55,760 --> 00:14:59,160 to use the power of what we've seen historically and to use the 257 00:14:59,160 --> 00:15:01,080 power of something that can think. 258 00:15:02,400 --> 00:15:05,840 Think faster and think, think better than them in certain ways 259 00:15:06,560 --> 00:15:09,520 along with supplementing their own expertise of what they're 260 00:15:09,520 --> 00:15:12,120 seeing in the industry and what they're seeing with the customer 261 00:15:12,120 --> 00:15:13,560 or the prospect in front of them. 262 00:15:13,960 --> 00:15:17,640 So we really believe that this is a partnership and in a 263 00:15:17,640 --> 00:15:20,320 digital transformation, what we're doing is we're not 264 00:15:20,320 --> 00:15:24,280 replacing, we are adding and we are augmenting the power of what 265 00:15:24,560 --> 00:15:29,480 the company can already do. That's really good, That's 266 00:15:29,480 --> 00:15:30,720 really good. That and that makes perfect 267 00:15:30,720 --> 00:15:32,440 sense as well. It's not something that's 268 00:15:32,640 --> 00:15:36,280 necessarily meant to be a replacement, but it's a tool to 269 00:15:36,280 --> 00:15:40,840 help strengthen the organization, to help allow them 270 00:15:40,840 --> 00:15:44,520 to become more efficient, quicker and also like you said, 271 00:15:44,520 --> 00:15:46,840 just to think differently in certain ways. 272 00:15:47,280 --> 00:15:49,840 And then also Heather, I'm glad you mentioned the whole aspect 273 00:15:49,840 --> 00:15:54,600 of teamwork, you know regarding digital transformations or even 274 00:15:54,600 --> 00:15:57,560 just reaching a goal as a company. 275 00:15:57,960 --> 00:16:00,640 A lot of departments have to be on the same page and that can be 276 00:16:00,640 --> 00:16:04,240 a process moving you know to to get all the all the departments 277 00:16:04,240 --> 00:16:07,760 on the same page. But that's how the dream works. 278 00:16:07,760 --> 00:16:09,840 Teamwork makes the dream work and that's how you know 279 00:16:10,760 --> 00:16:14,240 transformations successfully happen in the long run in the 280 00:16:14,240 --> 00:16:17,920 long larger scheme of things. Let me ask you guys this, once a 281 00:16:17,920 --> 00:16:21,920 company has decided that they're ready for AI and they've already 282 00:16:21,920 --> 00:16:25,320 selected AI solution and the solution has been implemented, 283 00:16:25,760 --> 00:16:28,080 how do they know it's going to be successful? 284 00:16:30,720 --> 00:16:36,320 Yeah, I could take that turns. So this is a great question 285 00:16:36,320 --> 00:16:39,880 because it is crucially important to constantly be 286 00:16:39,880 --> 00:16:42,520 measuring adoption and be measuring value. 287 00:16:43,480 --> 00:16:46,320 A digital transformation or adopting an AI solution isn't 288 00:16:46,320 --> 00:16:50,400 just a plug. And forget about it forever kind 289 00:16:50,400 --> 00:16:53,560 of play here. We really want to make sure that 290 00:16:53,760 --> 00:16:57,200 our we we want to make sure that companies continue to really 291 00:16:57,200 --> 00:17:00,760 measure what value they're seeing from it and know that for 292 00:17:00,760 --> 00:17:03,600 themselves. So some ways to measure value, I 293 00:17:03,600 --> 00:17:05,760 think there are some core questions to ask. 294 00:17:06,560 --> 00:17:09,800 One is, even during your implementation or before you 295 00:17:09,800 --> 00:17:13,079 really start using it, the importance of engaging with the 296 00:17:13,079 --> 00:17:17,200 system, looking at the results. Really looking at why those, why 297 00:17:17,200 --> 00:17:19,599 those particular results or recommendations are showing up 298 00:17:19,720 --> 00:17:23,119 and for you and your team to feel comfortable and confident 299 00:17:23,280 --> 00:17:28,079 of those because that provides a really strong starting ground to 300 00:17:28,079 --> 00:17:31,680 then six months later or one year later when you're looking 301 00:17:31,680 --> 00:17:35,440 at adoption which is I've, you know, I've had my system in 302 00:17:35,440 --> 00:17:38,560 place, it's providing AI based recommendations for pricing. 303 00:17:38,880 --> 00:17:41,320 How do I know that this is these recommendations are actually 304 00:17:41,320 --> 00:17:43,720 being used, or are they being ignored? 305 00:17:44,560 --> 00:17:47,360 So when you when you know that the results are reasonable 306 00:17:47,360 --> 00:17:50,520 because you've, you know validated them yourself, now you 307 00:17:50,520 --> 00:17:53,600 have this next step or of checking whether your team or 308 00:17:53,600 --> 00:17:57,320 your company is really adopting those those recommendations. 309 00:17:57,720 --> 00:18:02,000 And if not, you have a starting point to know well you know 310 00:18:02,000 --> 00:18:05,000 which which department or which team are you not really seeing 311 00:18:05,000 --> 00:18:08,040 adoption and what's going on. I think it's really important to 312 00:18:08,040 --> 00:18:10,040 bring curiosity to this kind of question. 313 00:18:10,040 --> 00:18:12,880 Is there is there something the team knows that maybe isn't? 314 00:18:13,520 --> 00:18:17,680 Included in the data that we need to start bringing in or is 315 00:18:17,680 --> 00:18:20,040 is there something else going on where they aren't necessarily 316 00:18:20,040 --> 00:18:22,160 familiar with the explainability tools. 317 00:18:22,720 --> 00:18:26,760 So and having those allow them to much interact with the system 318 00:18:26,760 --> 00:18:28,760 much better and have better adoption. 319 00:18:29,480 --> 00:18:32,560 So looking at adoption and diagnosing adoption is really 320 00:18:32,560 --> 00:18:34,840 crucial. But beyond that, I think the 321 00:18:34,840 --> 00:18:37,200 final step is really measuring value. 322 00:18:37,520 --> 00:18:40,680 So you want to know that when you are seeing adoption, you're 323 00:18:40,680 --> 00:18:43,000 seeing value and in the case of pricing? 324 00:18:43,440 --> 00:18:45,840 This means that you're meeting your financial goals. 325 00:18:46,080 --> 00:18:48,760 Are you making the kinds of revenue, are you making the 326 00:18:48,760 --> 00:18:53,840 kinds of margin that you and the uplift that you expect to see 327 00:18:54,160 --> 00:18:56,480 when you're following this pricing solution and when you 328 00:18:56,480 --> 00:19:00,920 are seeing adoption. A lot of times we've, you know 329 00:19:00,920 --> 00:19:05,800 we've seen with our customers at PROS we have a measure of. 330 00:19:06,200 --> 00:19:09,520 Where you see adoption, what is the revenue that you're seeing 331 00:19:09,520 --> 00:19:13,880 and how do you compare that to, you know, a similarly sized unit 332 00:19:13,880 --> 00:19:18,280 or similarly sized set of sales where you weren't seeing 333 00:19:18,280 --> 00:19:20,040 adoption? And is there, is there more 334 00:19:20,040 --> 00:19:23,520 revenue there or less revenue? And I think this, this is an 335 00:19:23,520 --> 00:19:26,240 example, it really depends on this, the company's goals and 336 00:19:26,240 --> 00:19:28,360 what they're trying to achieve with their pricing. 337 00:19:28,840 --> 00:19:33,680 But when you have that kind of that kind of comparison and when 338 00:19:33,680 --> 00:19:35,920 you are able to really see for yourself. 339 00:19:36,320 --> 00:19:39,600 Whether this AI solution is providing value to you, how much 340 00:19:39,600 --> 00:19:41,800 is it providing? Is there opportunity to increase 341 00:19:41,800 --> 00:19:44,440 it? Is there opportunity to be maybe 342 00:19:44,440 --> 00:19:48,520 be educating your company and really aligning your teams more? 343 00:19:48,760 --> 00:19:51,560 I think that is crucial and that's a journey that goes 344 00:19:51,960 --> 00:19:57,720 beyond implementing and really ensuring the continued growth 345 00:19:57,720 --> 00:20:00,840 and success of how an AI solution functions in your 346 00:20:00,840 --> 00:20:04,240 company. Very well said, Miss Covey, I 347 00:20:04,240 --> 00:20:07,560 want to thank you both so much for being a part of this podcast 348 00:20:07,560 --> 00:20:10,920 today for just engaging with me on this discussion about 349 00:20:11,360 --> 00:20:14,280 utilizing AI in the realm of pricing. 350 00:20:14,720 --> 00:20:19,320 Before I let you go both of you, I want to ask is there any type 351 00:20:19,320 --> 00:20:23,920 of resources that listeners can visit or grab a hold of to learn 352 00:20:23,920 --> 00:20:27,880 more about you individually or pros and what pros stands for 353 00:20:27,880 --> 00:20:29,120 and different things of that nature? 354 00:20:29,560 --> 00:20:32,440 Yeah, people are welcome to reach out to us on on LinkedIn 355 00:20:32,440 --> 00:20:36,480 to connect and chat. And you also can go to the pros 356 00:20:36,480 --> 00:20:42,480 website pros.com that's pros.com to find more information about 357 00:20:42,480 --> 00:20:46,720 the company that we work for specifically and it's the AAI 358 00:20:46,720 --> 00:20:50,120 tools that PROS has available. Awesome. 359 00:20:50,120 --> 00:20:53,280 Thank you so much guys for your time today and until next time, 360 00:20:53,600 --> 00:20:54,760 we'll see you guys then. Bye, bye.