1 00:00:00,520 --> 00:00:01,880 All right. Thank you all so much for tuning 2 00:00:01,880 --> 00:00:04,840 into yet another episode of the Professional Pricing Society 3 00:00:05,160 --> 00:00:07,240 podcast. My name is Terrence and today we 4 00:00:07,240 --> 00:00:11,680 have a super special guest with us who is also going to be one 5 00:00:11,680 --> 00:00:15,240 of the keynote speakers at our upcoming conference in Las Vegas 6 00:00:15,240 --> 00:00:19,640 this fall, which is October 22nd through the 25th. 7 00:00:20,000 --> 00:00:22,960 Her name is Stephanie Yee. She is a partner at Bain and 8 00:00:22,960 --> 00:00:26,760 Company where she exclusively serves clients on the topics of 9 00:00:26,760 --> 00:00:30,080 pricing and profitability. She has LED multiple successful 10 00:00:30,080 --> 00:00:33,760 pricing transformation programs and is a former pricing and 11 00:00:33,760 --> 00:00:37,200 sales senior executive at a Fortune 75 company. 12 00:00:37,920 --> 00:00:41,800 Stephanie holds a Management Information Systems degree from 13 00:00:41,800 --> 00:00:45,840 Texas A&M University as well. Miss Stephanie, how are we doing 14 00:00:45,840 --> 00:00:47,520 today? I'm doing great. 15 00:00:47,520 --> 00:00:50,280 How are you, Terrance? Doing very, very well, dear. 16 00:00:50,560 --> 00:00:52,560 We're going to be talking today about Gen. 17 00:00:52,600 --> 00:00:56,640 AI pricing hype or high stakes game changer. 18 00:00:56,640 --> 00:01:00,880 And so you have a plethora of knowledge and you have a 19 00:01:00,880 --> 00:01:04,560 plethora of experience, which is why you're going to be super 20 00:01:05,200 --> 00:01:07,480 popular keynote for this year's conference. 21 00:01:07,480 --> 00:01:09,800 And so I want to thank you first of all for taking the time with 22 00:01:09,800 --> 00:01:14,400 me to kind of share a bit of a teaser, if you will, on this 23 00:01:14,400 --> 00:01:17,600 podcast for what we're going to be discussing in the fall. 24 00:01:17,600 --> 00:01:19,480 Is that correct? That's right. 25 00:01:19,520 --> 00:01:21,400 Terence, thanks for having me today. 26 00:01:21,400 --> 00:01:24,200 I really appreciate the time. Yeah, not a problem at all. 27 00:01:24,200 --> 00:01:27,880 So let's go ahead and jump into the conversation, you know, AI 28 00:01:27,880 --> 00:01:32,280 and pricing. What is new about AI? 29 00:01:32,280 --> 00:01:34,880 About G and AI specifically in your opinion? 30 00:01:35,400 --> 00:01:38,720 Yeah, it's right because it's, it's interesting because AI is 31 00:01:38,800 --> 00:01:42,840 feels like a very old topic, but also a very new topic in the 32 00:01:42,840 --> 00:01:47,040 space. So, you know, it's a matter of 33 00:01:47,040 --> 00:01:50,080 fact, pricing has actually been in a functional area, one of the 34 00:01:50,080 --> 00:01:54,280 most likely places across commercial functions to have 35 00:01:54,320 --> 00:01:58,280 introduced some type of AI and ML capabilities. 36 00:01:58,280 --> 00:02:02,680 So it's not necessarily new. Most of your audience I think, 37 00:02:02,680 --> 00:02:06,520 knows that pricing actually has quite a bit of science behind 38 00:02:06,520 --> 00:02:08,520 it. But there are some new 39 00:02:08,759 --> 00:02:11,720 technologies available with Gen. AI that's bringing this 40 00:02:11,720 --> 00:02:15,640 conversation to the forefront. You know, just to take a step 41 00:02:15,640 --> 00:02:20,840 back, traditional AI can be broadly characterized as really 42 00:02:20,840 --> 00:02:25,120 good at math solving for specific narrow task, you know, 43 00:02:25,120 --> 00:02:28,600 typically requires a lot of data to build and train models. 44 00:02:29,080 --> 00:02:32,480 And Jen AI actually differs from traditional AI in a couple of 45 00:02:32,480 --> 00:02:36,840 different ways. Jen AI at the core of it is 46 00:02:36,840 --> 00:02:41,920 based on large language models. And so therefore it's actually 47 00:02:42,040 --> 00:02:46,920 really good at reading and synthesizing unstructured data. 48 00:02:47,400 --> 00:02:49,840 So what does that mean? It means it's, it's really good 49 00:02:49,840 --> 00:02:54,440 at like reading through call transcripts, articles, basically 50 00:02:54,440 --> 00:02:59,880 text to understand and synthesize and summarize what 51 00:02:59,880 --> 00:03:02,240 that means. And because it's able to do 52 00:03:02,240 --> 00:03:05,640 that, it's all the, it's also really great at creating 53 00:03:05,640 --> 00:03:09,720 unstructured data. And so with this capability, you 54 00:03:09,720 --> 00:03:13,560 know, Jen AI, we're able to actually develop top tracks, 55 00:03:13,720 --> 00:03:17,960 explanations, things like that. So it's actually quite good with 56 00:03:17,960 --> 00:03:21,920 writing text among other things, but it's a little bit different 57 00:03:21,920 --> 00:03:25,280 from the traditional AI capabilities that we've 58 00:03:25,280 --> 00:03:31,240 historically used in pricing. And, you know, AI is just one of 59 00:03:31,240 --> 00:03:34,200 those things that is continuing to evolve as time progresses. 60 00:03:34,760 --> 00:03:37,960 And now that it's in the realm of pricers, in the realm of 61 00:03:37,960 --> 00:03:40,840 pricing, you know, how do you see this, you know, new 62 00:03:40,840 --> 00:03:44,360 technology being applied to to pricing in recent years? 63 00:03:44,360 --> 00:03:46,560 And what do you foresee in the upcoming future? 64 00:03:46,960 --> 00:03:49,840 Yeah, yeah. So we think Gen. 65 00:03:49,840 --> 00:03:54,320 AI actually specifically will unlock new capabilities for 66 00:03:54,320 --> 00:03:57,640 pricing that just really wasn't possible before. 67 00:03:58,440 --> 00:04:02,360 And specifically we see 3 broad use cases that we're 68 00:04:02,360 --> 00:04:06,120 particularly excited about. And I'm, I'm happy to talk 69 00:04:06,120 --> 00:04:07,760 through, you know what these are. 70 00:04:08,120 --> 00:04:11,760 You know, the first one is enabling price setting, which is 71 00:04:11,760 --> 00:04:15,880 one of the core activities you do as a price, as a pricer. 72 00:04:16,360 --> 00:04:20,880 We think this new capability will help bolster existing 73 00:04:20,959 --> 00:04:25,440 traditional AIML capabilities where they have fallen short 74 00:04:25,440 --> 00:04:28,400 when it comes to price optimization and price setting. 75 00:04:28,760 --> 00:04:31,680 So I mean, it's no secret kind of if you look out into the 76 00:04:31,680 --> 00:04:36,160 marketplace, there have been companies that are absolutely 77 00:04:36,280 --> 00:04:40,280 been successful using traditional AIML approaches to 78 00:04:40,320 --> 00:04:42,320 derive pricing. I mean, Amazon's probably the 79 00:04:42,320 --> 00:04:46,360 best example that everybody knows both in B to B and in B to 80 00:04:46,360 --> 00:04:49,040 C, they use a data-driven approach. 81 00:04:49,040 --> 00:04:52,280 They run tests and experiments. They ingest data from many 82 00:04:52,280 --> 00:04:55,560 different places to really optimize price. 83 00:04:56,480 --> 00:05:01,760 But when we take this example of using AIML to drive price 84 00:05:01,760 --> 00:05:06,640 optimization and we survey companies who have undertaken, 85 00:05:06,880 --> 00:05:09,560 you know the project and and the program to actually develop 86 00:05:09,560 --> 00:05:15,520 these capabilities through AIML. We actually see that laggards as 87 00:05:15,560 --> 00:05:21,040 compared to market leaders are 2.5 times more likely to still 88 00:05:21,040 --> 00:05:25,680 lack effective pricing guidance when they use these traditional 89 00:05:25,680 --> 00:05:31,080 AIML approaches. And what we learned is that many 90 00:05:31,080 --> 00:05:33,640 of them failed to get the full value of the program. 91 00:05:34,280 --> 00:05:37,160 And this actually happens for three reasons. 92 00:05:37,760 --> 00:05:41,640 One is that AIML approaches typically use a lot of 93 00:05:41,640 --> 00:05:44,400 historical internal data to develop the right price 94 00:05:44,400 --> 00:05:48,800 recommendation, which altogether isn't wrong or bad because those 95 00:05:48,800 --> 00:05:52,080 historical price points are actually tested in the 96 00:05:52,080 --> 00:05:55,720 marketplace. But sometimes it doesn't really 97 00:05:55,720 --> 00:05:58,880 account for things that are happening here and right now and 98 00:05:58,880 --> 00:06:02,240 other external data sources that actually might improve your 99 00:06:02,240 --> 00:06:05,920 outcome. The second is we still see that 100 00:06:05,920 --> 00:06:08,520 there is a disconnect between pricing and sales. 101 00:06:08,520 --> 00:06:12,880 This is an age-old issue in the world of pricing where sales 102 00:06:12,880 --> 00:06:14,400 doesn't fully trust the guidance. 103 00:06:14,400 --> 00:06:16,400 So then therefore they're not using it. 104 00:06:17,560 --> 00:06:21,320 And we also see that there's disparage and fragmented data 105 00:06:21,320 --> 00:06:25,760 across the ecosystem that can be used in pricing systematically, 106 00:06:25,760 --> 00:06:29,320 but is not usually incorporated because it's difficult to use. 107 00:06:30,160 --> 00:06:34,080 And so the capability that is unlock through Gen. 108 00:06:34,080 --> 00:06:38,080 AI can really help address each of these shortcomings. 109 00:06:38,640 --> 00:06:42,640 And, and here's how. So this first one I talked about 110 00:06:42,640 --> 00:06:47,120 where traditional AIML really focuses on historical data. 111 00:06:48,840 --> 00:06:52,400 You know, imagine a world in which you're actually able to 112 00:06:52,400 --> 00:06:55,600 bolster your price recommendations or more on more 113 00:06:55,600 --> 00:06:58,120 recent data that could impact price. 114 00:06:58,120 --> 00:07:02,720 So say for example, you know, tomorrow out in the news, your 115 00:07:02,720 --> 00:07:05,480 competitor announces that they're going to build new 116 00:07:05,480 --> 00:07:10,480 supply, you know, or if there's a supply chain disruption 117 00:07:10,480 --> 00:07:12,960 somewhere, you know, in the marketplace. 118 00:07:13,920 --> 00:07:17,720 As an example, we worked with a chemicals client that previously 119 00:07:17,720 --> 00:07:21,320 invested a significant amount of time in building a pricing 120 00:07:21,320 --> 00:07:24,920 guidance tool based on historical AIML capabilities. 121 00:07:25,640 --> 00:07:30,680 But through the pandemic and the Ukraine war, the supply demand 122 00:07:30,680 --> 00:07:33,600 dynamics changed massively, as you can imagine. 123 00:07:34,240 --> 00:07:38,480 And the price recommendations based on past data just wasn't 124 00:07:38,480 --> 00:07:39,480 good. You know, because it doesn't, 125 00:07:39,480 --> 00:07:41,200 it's not relevant. There's new things that are 126 00:07:41,200 --> 00:07:44,280 happening in the marketplace that actually should drive a 127 00:07:44,280 --> 00:07:47,800 different price recommendation. So they ended up, you know, kind 128 00:07:47,800 --> 00:07:52,360 of moving away from their a AIML tool and started doing things in 129 00:07:52,360 --> 00:07:56,120 in spreadsheets in Excel to try to like, you know, really 130 00:07:56,120 --> 00:08:01,040 account for these latest trends. Well, fast forwarded now with 131 00:08:01,040 --> 00:08:04,120 this capability with Gen. AI, they're actually able to 132 00:08:04,120 --> 00:08:07,360 capture data from some of these external sources. 133 00:08:07,680 --> 00:08:10,480 So imagine being able to bring that in Gen. 134 00:08:10,480 --> 00:08:13,560 AI as a language model, being able to synthesize, hey, this is 135 00:08:13,560 --> 00:08:17,200 happening in the marketplace. There's new capacity coming up. 136 00:08:17,680 --> 00:08:20,520 There's competitor price actions we, you know, that that we're 137 00:08:20,520 --> 00:08:25,680 now learning about and ingesting that can help them basically 138 00:08:25,680 --> 00:08:28,000 alter their price recommendation that they would have 139 00:08:28,000 --> 00:08:32,360 historically provided to really kind of answer questions around 140 00:08:32,960 --> 00:08:35,400 is the market going to be long or short? 141 00:08:36,240 --> 00:08:38,799 What's this company's position in the marketplace? 142 00:08:39,280 --> 00:08:42,679 Should they be pushing towards like a spot deal or should they 143 00:08:42,679 --> 00:08:44,960 actually tie in volumes on contract because things are 144 00:08:44,960 --> 00:08:47,720 going to be long, you know what, what should the prices really 145 00:08:47,720 --> 00:08:50,760 be? And so by ingesting some of this 146 00:08:50,760 --> 00:08:54,520 external data that was hard to really synthesize and, and, and 147 00:08:54,520 --> 00:08:58,480 then bring into the pricing recommendations, able to 148 00:08:58,480 --> 00:09:02,520 actually improve, improve the quality of their recommendations 149 00:09:02,840 --> 00:09:06,600 by bringing both, you know, the traditional AI and, and some of 150 00:09:06,600 --> 00:09:08,960 these newer jet AI capabilities together. 151 00:09:10,680 --> 00:09:14,320 I'll give you another example of one that we've been working on 152 00:09:14,320 --> 00:09:17,040 with a different client where they're actually using their own 153 00:09:17,040 --> 00:09:21,560 customer service data to inform pricing decisions. 154 00:09:22,000 --> 00:09:25,760 So we've recently worked with a client and they're using their 155 00:09:25,760 --> 00:09:28,600 Gen. AI capabilities to expand their 156 00:09:28,600 --> 00:09:32,440 data set and bring an insight on customer service issues and 157 00:09:32,440 --> 00:09:36,280 delays because actually when you're doing pricing, very often 158 00:09:36,280 --> 00:09:38,720 times it's a reflection of the value prop and the service that 159 00:09:38,720 --> 00:09:40,920 you provide. And so if there's been issues 160 00:09:40,920 --> 00:09:43,920 with the services that you're providing, that context is 161 00:09:43,920 --> 00:09:48,480 actually really important. And so we're giving those kinds 162 00:09:48,720 --> 00:09:51,760 of information to the account executives so they have a much 163 00:09:51,760 --> 00:09:55,200 clearer picture of the negotiation landscape when they 164 00:09:55,200 --> 00:09:58,360 go to talk about pricing and do their negotiations. 165 00:09:59,880 --> 00:10:03,120 And so as I said before, we're finding that using both 166 00:10:03,120 --> 00:10:07,120 traditional AIML techniques to lean the best you can from the 167 00:10:07,120 --> 00:10:11,560 past, do the math to get a data-driven decisions, but also 168 00:10:11,640 --> 00:10:14,600 combining that with some of these newer capabilities with 169 00:10:14,600 --> 00:10:18,520 these language models really provides the most powerful 170 00:10:18,600 --> 00:10:24,040 outcomes for price optimization. The second thing is any price is 171 00:10:24,040 --> 00:10:26,040 like, it's not enough just to set prices. 172 00:10:26,040 --> 00:10:28,680 You actually have to work on getting the prices. 173 00:10:29,080 --> 00:10:32,400 And this is especially, especially relevant in B to B 174 00:10:32,400 --> 00:10:35,320 where there's typically, you know, some kind of salesperson 175 00:10:35,320 --> 00:10:38,640 that sits between, you know, the price and negotiating with the 176 00:10:38,640 --> 00:10:41,040 customer. And so you don't always actually 177 00:10:41,040 --> 00:10:44,800 get the prices that you set because if some of that value 178 00:10:44,800 --> 00:10:49,320 gets negotiated away. So the second use case we see 179 00:10:49,840 --> 00:10:52,560 that Jenny, I can really help out with is, is actually with 180 00:10:52,560 --> 00:10:56,320 price getting. So one of the greatest sources 181 00:10:56,320 --> 00:11:01,840 of margin leakage comes from contract non compliance. 182 00:11:03,000 --> 00:11:06,120 And so historically, you know, when you're working with a 183 00:11:06,120 --> 00:11:08,920 customer, you know, if you've got contracts, you develop 184 00:11:08,920 --> 00:11:12,440 contracts and inside of these contracts you'll have different 185 00:11:12,440 --> 00:11:17,000 terms and details. And most of this stuff is 186 00:11:17,000 --> 00:11:20,960 actually locked up in PD, FS and Word documents. 187 00:11:21,080 --> 00:11:23,960 And that makes it really hard to know if the customers are 188 00:11:24,400 --> 00:11:26,720 compliant against your agreed upon terms. 189 00:11:27,120 --> 00:11:30,960 So terms like payment, you know, you're supposed to pay in a 190 00:11:30,960 --> 00:11:33,960 certain amount of days, you have the ability to do price 191 00:11:33,960 --> 00:11:37,760 escalations if you know input costs raises above a certain 192 00:11:37,760 --> 00:11:41,680 level or there's like right delivery terms, it's OK, I'm 193 00:11:41,840 --> 00:11:44,600 able to charge you if I need to, you know, rush deliver something 194 00:11:44,600 --> 00:11:48,480 to you. Using both AIML and Gen. 195 00:11:48,480 --> 00:11:53,480 AI, we're actually now able to systematically read through 196 00:11:53,960 --> 00:11:57,520 these contracts and extract those terms out. 197 00:11:57,680 --> 00:12:01,640 And that's really powerful because it's much easier to 198 00:12:01,720 --> 00:12:04,920 analyze whether or not there's compliance against the terms 199 00:12:04,920 --> 00:12:07,880 once you've extracted that. Now I can take that and compare 200 00:12:07,880 --> 00:12:10,000 that to like, well, how many times have I charged you for 201 00:12:10,000 --> 00:12:11,840 freight? Am I, you know, getting the full 202 00:12:11,840 --> 00:12:15,080 value out of it when it's in a Word document, it's very 203 00:12:15,080 --> 00:12:18,960 difficult at scale to do that. But when you're able to extract 204 00:12:18,960 --> 00:12:21,600 those terms, imagine to like an Excel or something like that, 205 00:12:21,600 --> 00:12:25,000 then it becomes a lot easier to say, hey, these sets of 206 00:12:25,000 --> 00:12:28,400 customers, we agreed to these terms, but they're not following 207 00:12:28,400 --> 00:12:30,920 it. And therefore have this much 208 00:12:30,920 --> 00:12:34,200 margin dollars that I could be getting that I'm not getting. 209 00:12:35,680 --> 00:12:39,560 So as an example of this, I recently worked with a 210 00:12:39,560 --> 00:12:44,160 healthcare client and we helped them identify 300 bits of 211 00:12:44,200 --> 00:12:48,400 improvement, uplift a money owed to them to contract clients. 212 00:12:48,760 --> 00:12:53,120 It was collecting on late fees that they could have, making 213 00:12:53,120 --> 00:12:55,920 sure that people were paying on time, things like that. 214 00:12:56,280 --> 00:13:00,160 And when you identify this kind of value, we were, it actually 215 00:13:00,240 --> 00:13:01,840 be able to do 2 things with them. 216 00:13:02,160 --> 00:13:05,240 One is what we call kind of like ringing the cash register. 217 00:13:05,240 --> 00:13:07,920 So it's like, hey, the your customers owe you money on these 218 00:13:07,920 --> 00:13:09,480 things. Like actually go get that. 219 00:13:09,480 --> 00:13:11,840 That's like money that drops straight to the bottom line. 220 00:13:12,840 --> 00:13:16,120 But the second thing we were able to do from a longer term 221 00:13:16,120 --> 00:13:20,200 perspective was say, Oh, well, you have these really beneficial 222 00:13:20,200 --> 00:13:24,400 terms, but it's only in these five, you know, contracts or 10 223 00:13:24,400 --> 00:13:27,000 contracts. Like why shouldn't you be 224 00:13:27,000 --> 00:13:30,160 thinking about applying it to all of your contracts, you know, 225 00:13:30,160 --> 00:13:33,520 and how do you in your negotiation process and as you 226 00:13:33,520 --> 00:13:36,920 work with that customer, move them towards these beneficial 227 00:13:36,920 --> 00:13:41,440 terms or at least drive like a give, get conversation on that. 228 00:13:41,920 --> 00:13:46,080 And so we find that most of the times our clients are pretty 229 00:13:46,080 --> 00:13:49,680 inconsistent in the way that they apply these beneficial 230 00:13:49,680 --> 00:13:54,000 clauses in their contracts. And this exercise really helped 231 00:13:54,000 --> 00:13:57,600 bring to life where there could be more consistent and we were 232 00:13:57,600 --> 00:14:01,440 able to like communicate, you know what that value of being 233 00:14:01,440 --> 00:14:06,240 more consistent would be. So that's another exciting use 234 00:14:06,240 --> 00:14:09,880 case that Gen. AII think will unlock in the in 235 00:14:09,880 --> 00:14:14,360 terms of price getting. And then the last and the third 236 00:14:14,360 --> 00:14:17,160 use case is with sales enablement. 237 00:14:17,760 --> 00:14:20,960 And this is where I think Gen. AI actually really shines and 238 00:14:20,960 --> 00:14:23,600 can help in several different right ways. 239 00:14:24,520 --> 00:14:28,680 Most pricers will tell you that getting you know sales to trust 240 00:14:28,680 --> 00:14:32,720 and use pricing guidance is one of the toughest changes to make 241 00:14:32,720 --> 00:14:36,080 in an organization. And one of the things that Jenny 242 00:14:36,080 --> 00:14:41,600 I can do is provide because of that text language capability 243 00:14:41,800 --> 00:14:46,560 summaries and explanations of price that can help with seller 244 00:14:46,560 --> 00:14:49,240 gain confidence in price recommendations. 245 00:14:49,640 --> 00:14:53,640 So there's a lot of focus right now on how do you develop sales 246 00:14:53,640 --> 00:14:57,240 Co pilots that help them really like improve and be better at 247 00:14:57,240 --> 00:14:58,760 the things that they're doing in their job. 248 00:14:59,080 --> 00:15:02,960 Well, you can imagine a world in which these capabilities 249 00:15:03,320 --> 00:15:05,840 actually help a seller understand why is it priced this 250 00:15:05,840 --> 00:15:08,120 way. Going back to some of the stuff 251 00:15:08,120 --> 00:15:11,080 I said earlier, what's happening in the marketplace that's 252 00:15:11,080 --> 00:15:16,240 driving these prices, you know, and it's able to have a two way 253 00:15:16,440 --> 00:15:19,240 almost chat like dialogue to say, hey, give me a summary of, 254 00:15:19,360 --> 00:15:22,360 of why we've we've, why are prices what it is, you know, 255 00:15:22,360 --> 00:15:24,880 what are the talking points and all those good things. 256 00:15:24,920 --> 00:15:30,240 And in today's world, a lot of that stuff is very manual. 257 00:15:30,680 --> 00:15:34,320 Some pricing or sales OPS team is trying to build that stuff 258 00:15:34,640 --> 00:15:36,920 and it's not very dynamic, you know? 259 00:15:36,920 --> 00:15:40,760 And so you can imagine a world in which you can actually 260 00:15:40,760 --> 00:15:44,840 greatly increase like the trust and understanding of pricing 261 00:15:45,040 --> 00:15:47,440 with the seller through some of these capabilities. 262 00:15:48,040 --> 00:15:51,200 And this really creates what we call a democratization of 263 00:15:51,240 --> 00:15:54,280 insights, which is really the fancier way of just saying that 264 00:15:54,440 --> 00:15:58,520 they have access to insights that previously they would have 265 00:15:58,520 --> 00:16:02,640 had to go to a pricing analyst or somebody like that to get and 266 00:16:02,640 --> 00:16:04,600 to understand. And now they can, you know, 267 00:16:04,600 --> 00:16:08,640 self-serve on, on, on, on some of these capabilities. 268 00:16:10,240 --> 00:16:14,240 The second thing I think that Jenny I can do that really 269 00:16:14,240 --> 00:16:19,760 supports sales is Jenny I can develop really compelling 270 00:16:20,040 --> 00:16:24,920 marketing and sales collateral that really speaks to the value 271 00:16:24,920 --> 00:16:29,360 proposition of the product, the service that's in line with the 272 00:16:29,360 --> 00:16:32,640 prices paid. So you can imagine, you know, if 273 00:16:32,640 --> 00:16:37,720 a seller is able to compellingly articulate the value of the 274 00:16:37,720 --> 00:16:42,520 product or service that they're selling, then you know, the 275 00:16:42,560 --> 00:16:45,280 customer feels good about the pricing that they're actually 276 00:16:45,280 --> 00:16:46,640 getting. It makes sense. 277 00:16:46,720 --> 00:16:50,320 The prices paid are consistent with the value that they think 278 00:16:50,320 --> 00:16:54,400 that they're getting. And this capability is not only 279 00:16:54,640 --> 00:16:57,600 better with Jen AI, it's a lot faster. 280 00:16:58,160 --> 00:17:03,120 So in fact, we recently worried what worked with a client to 281 00:17:03,120 --> 00:17:06,520 increase the speed at which they're able to create these 282 00:17:06,520 --> 00:17:08,599 good sales and marketing collateral. 283 00:17:08,920 --> 00:17:12,000 And think about this in terms of like the emails you need to 284 00:17:12,000 --> 00:17:15,760 send, you know, the, the PowerPoint presentations, all of 285 00:17:15,760 --> 00:17:20,240 those good things. Their original turn around time 286 00:17:20,240 --> 00:17:24,720 for initial copy was basically reduced from 5 days using a 287 00:17:24,720 --> 00:17:26,960 marketing agency to two days. Wow. 288 00:17:28,840 --> 00:17:32,400 So great efficiency gains. And you know, if you kind of 289 00:17:32,400 --> 00:17:34,480 read up on Gen. AI, they'll say that one of the 290 00:17:34,960 --> 00:17:38,560 most compelling capabilities is that they are really good at 291 00:17:39,160 --> 00:17:44,160 developing just comprehensive and compelling arguments for, 292 00:17:44,400 --> 00:17:46,880 you know, whatever it is that that you've prompted them to do. 293 00:17:48,560 --> 00:17:52,480 And then I think the third way in which an AI is really helping 294 00:17:52,880 --> 00:17:57,200 enable sellers is helping them prepare through negotiation 295 00:17:57,200 --> 00:17:59,720 training. So a lot of pricing value is 296 00:17:59,720 --> 00:18:03,320 actually eroded away during the negotiation process with the 297 00:18:03,320 --> 00:18:07,960 customer, as you can imagine. And now there are actually AI 298 00:18:07,960 --> 00:18:13,000 assisted self learning modules that can take into account a 299 00:18:13,000 --> 00:18:15,840 sales reps like previous responses as they're going 300 00:18:15,840 --> 00:18:20,360 through this training and it'll generate a customer response for 301 00:18:20,360 --> 00:18:23,760 them to practice with. So I think in all of these 302 00:18:23,760 --> 00:18:27,680 different ways, Jen AI is actually going to really help 303 00:18:27,920 --> 00:18:32,400 upskill the seller, which in turn will actually increase. 304 00:18:32,400 --> 00:18:37,480 I think you know the customer value at what the you know what 305 00:18:37,480 --> 00:18:39,800 the customer value is to the work. 306 00:18:40,440 --> 00:18:43,400 OK. So essentially the new 307 00:18:43,400 --> 00:18:46,920 technology being applied in pricing, specifically with 308 00:18:47,640 --> 00:18:50,640 generative AI is super beneficial. 309 00:18:50,920 --> 00:18:54,760 And according to you, you know, it helps with different time 310 00:18:54,760 --> 00:18:59,160 efficiency, marketing, collateral, negotiation, 311 00:18:59,160 --> 00:19:00,720 trainings. I mean, there's just a a 312 00:19:00,720 --> 00:19:04,120 plethora of things that this is going to help pricers out with 313 00:19:04,400 --> 00:19:06,680 moving forward. And you know, as as companies 314 00:19:06,680 --> 00:19:11,760 continue to really grab a hold of utilizing this tool as best 315 00:19:11,760 --> 00:19:16,960 they possibly can and is their task a little bit more time 316 00:19:16,960 --> 00:19:19,280 efficient as far as completing those tasks. 317 00:19:19,720 --> 00:19:23,960 My question is, you know, as miraculous and as awesome as 318 00:19:24,400 --> 00:19:27,800 something like Gin AI is, how do you suggest or what do you 319 00:19:27,800 --> 00:19:33,280 advise to, to get started in working in artificial 320 00:19:33,280 --> 00:19:35,400 intelligence? Yeah, that's right. 321 00:19:35,480 --> 00:19:39,520 And so we think that there's four key steps to getting 322 00:19:39,560 --> 00:19:43,160 started, OK. So the first is identifying 323 00:19:43,160 --> 00:19:46,680 where the money and the value is in the business to unlock. 324 00:19:47,120 --> 00:19:51,080 You know, on this first step, we actually strongly encourage that 325 00:19:51,080 --> 00:19:56,320 pricing teams look beyond just the specific pricing use cases 326 00:19:56,640 --> 00:20:01,440 and actually think more broadly about the business outcomes to 327 00:20:01,480 --> 00:20:04,400 unlock that would be of most value to the organization. 328 00:20:05,000 --> 00:20:09,520 So think of this not as like. Hey, I want to set my sight on 329 00:20:09,520 --> 00:20:12,840 just improving price recommendations, but a more 330 00:20:12,840 --> 00:20:16,760 aspirational goal that is looks more like actually want to 331 00:20:16,760 --> 00:20:20,320 increase win rates by X percent for the organization. 332 00:20:21,120 --> 00:20:24,320 We want to increase renewal rates by X percent. 333 00:20:24,600 --> 00:20:29,160 We want to reduce bid response time by this amount of time and 334 00:20:29,240 --> 00:20:33,640 pricing no doubt will be a component of that solve, but 335 00:20:33,640 --> 00:20:36,800 there will also be other capabilities required. 336 00:20:37,160 --> 00:20:40,120 And we think it's important to set your sights a little bit 337 00:20:40,120 --> 00:20:45,600 broader to create the win and the organizational energy behind 338 00:20:45,840 --> 00:20:48,320 the effort. Because I mean, just frankly, if 339 00:20:48,320 --> 00:20:52,400 you touch US executive, but I want to improve my how I set 340 00:20:52,400 --> 00:20:55,120 prices versus hey, I want to increase our win rates, but it's 341 00:20:55,120 --> 00:20:58,800 just a different level of engagement that you get from 342 00:20:58,800 --> 00:21:02,280 those conversations. So we think the first thing is 343 00:21:02,800 --> 00:21:08,000 know where the value is. The second step is you got to 344 00:21:08,000 --> 00:21:12,680 figure out where you are in terms of your org readiness to 345 00:21:12,680 --> 00:21:15,320 be able to utilize some of these tools. 346 00:21:15,800 --> 00:21:20,680 So make no mistake, as great as AI and Gen. 347 00:21:20,680 --> 00:21:23,680 AI and all these new technologies are, they are 348 00:21:23,680 --> 00:21:27,760 tools. Tools enable a strategy. 349 00:21:27,880 --> 00:21:32,760 It doesn't make a strategy. So if you don't know how you 350 00:21:32,760 --> 00:21:37,480 want a price in the future as a business, a tool's not going to 351 00:21:37,480 --> 00:21:41,000 fix that. You know, you will end up 352 00:21:41,400 --> 00:21:44,680 codifying your same old pricing practices if you don't think 353 00:21:44,680 --> 00:21:47,320 through and kind of define what that future state should look 354 00:21:47,320 --> 00:21:50,040 like. And then you'll be left 355 00:21:50,040 --> 00:21:52,600 wondering, OK, well, why didn't that tool work? 356 00:21:52,600 --> 00:21:55,000 Well, it's because you codified your all your old practices that 357 00:21:55,000 --> 00:22:00,400 actually wasn't already working with this new technology. 358 00:22:00,400 --> 00:22:03,560 You definitely will need new data sets as we can have talked 359 00:22:03,560 --> 00:22:05,600 about. You'll need new tools with Gen. 360 00:22:05,600 --> 00:22:08,760 AI and other things like that. You'll need new architecture for 361 00:22:08,760 --> 00:22:11,520 how that data interacts with all of your systems. 362 00:22:11,880 --> 00:22:15,960 You'll need actually probably different talent to enable this 363 00:22:15,960 --> 00:22:18,600 work. And you'll actually need 364 00:22:18,600 --> 00:22:22,080 commitment from your leadership to drive this change through and 365 00:22:22,080 --> 00:22:24,880 to get actually the resourcing that you need to do this right. 366 00:22:25,360 --> 00:22:29,360 So it's really important that you know, you identify the 367 00:22:29,360 --> 00:22:32,120 value, which is the first step, but the second step is you need 368 00:22:32,120 --> 00:22:35,680 to have a clear vision of what your starting point is so that 369 00:22:35,680 --> 00:22:39,200 you know where you can go. You know, what's really more 370 00:22:39,200 --> 00:22:42,560 immediate next step versus long term aspirational in your road 371 00:22:42,560 --> 00:22:46,440 map. So once you know where the value 372 00:22:46,440 --> 00:22:52,200 is and what your organizational capabilities are, then you can 373 00:22:52,200 --> 00:22:56,040 start to prioritize well, what things can I actually tackle 374 00:22:56,080 --> 00:22:58,960 near term and what things are probably kind of a little bit 375 00:22:58,960 --> 00:23:01,480 longer in the road map. So we want to get to those 376 00:23:01,480 --> 00:23:04,080 things, but there's more foundational things we need to 377 00:23:04,080 --> 00:23:08,040 do 1st. And there are so many use cases 378 00:23:08,480 --> 00:23:11,600 you can choose from. And so prioritization is really 379 00:23:11,600 --> 00:23:15,520 paramount. We see organizations who are 380 00:23:15,520 --> 00:23:20,120 early pioneers of this work falling into a couple of traps. 381 00:23:20,240 --> 00:23:24,360 And so when you think about prioritization, one of the 382 00:23:24,360 --> 00:23:28,560 things we see as a trap is doing what is easy versus what's 383 00:23:28,560 --> 00:23:31,160 valuable. So we've seen clients who've 384 00:23:31,160 --> 00:23:35,400 started to do this work on their own, and they'll enable 385 00:23:35,400 --> 00:23:37,680 something that's like, oh, well, you know, this would be easy to 386 00:23:37,680 --> 00:23:43,200 do, but there's actually not a very clear ROI on actually doing 387 00:23:43,200 --> 00:23:45,520 that work. And the success metrics may not 388 00:23:45,520 --> 00:23:49,600 be very clear either. And so then, you know, it's very 389 00:23:49,600 --> 00:23:51,600 hard to see like, well, did I get value out of this? 390 00:23:51,600 --> 00:23:54,320 Should I keep doing these things, you know, and so that's 391 00:23:54,320 --> 00:23:57,800 one trap. The other one is 1 I kind of 392 00:23:57,800 --> 00:23:59,880 touched upon earlier. It's like just starting with the 393 00:23:59,880 --> 00:24:03,560 use case that's so small that it's hard to really have 394 00:24:03,560 --> 00:24:06,920 meaningful impact. And so that's why we 395 00:24:06,920 --> 00:24:12,280 recommending not just looking at just a pricing use case, but 396 00:24:12,280 --> 00:24:16,400 maybe a constellation of use cases that delivers an overall 397 00:24:16,400 --> 00:24:20,280 business outcome. And ideally you'd actually have 398 00:24:20,280 --> 00:24:22,880 it tied to a common set of users from a change management 399 00:24:22,880 --> 00:24:24,280 perspective. So you're kind of making the 400 00:24:24,280 --> 00:24:29,320 change holistically and enabling like a constellation of use 401 00:24:29,320 --> 00:24:34,160 cases makes it easier to create and measure step change success. 402 00:24:34,160 --> 00:24:37,640 So now we're not no longer talking about, oh, I, you know, 403 00:24:37,640 --> 00:24:40,360 improved prices for this many transactions. 404 00:24:40,680 --> 00:24:44,800 It's more like actually this war helped us change our win rate 405 00:24:44,800 --> 00:24:50,400 from 10% to 11%, which is more of a step change success, which 406 00:24:50,400 --> 00:24:54,240 is actually incredibly important when you're building early 407 00:24:54,280 --> 00:24:57,520 momentum for this kind of work in an organization. 408 00:24:58,440 --> 00:25:01,280 And so if you know where the value is, you know where your 409 00:25:01,280 --> 00:25:03,880 starting point is and you've started to prioritize and you 410 00:25:03,880 --> 00:25:06,440 have a sense for like, OK, this is these are the things, this is 411 00:25:06,440 --> 00:25:07,720 what the road map's going to look like. 412 00:25:08,240 --> 00:25:11,640 The last thing you need to do is actually prepare your 413 00:25:11,640 --> 00:25:15,440 organization for change. And so that means several 414 00:25:15,440 --> 00:25:18,680 different things. One, you need to have clear 415 00:25:18,680 --> 00:25:21,760 rules and responsibilities for the team that's going to support 416 00:25:21,760 --> 00:25:23,920 the program. And that means they're clear on 417 00:25:23,920 --> 00:25:25,640 they're just who like who's going to make The Who has 418 00:25:25,640 --> 00:25:28,400 decision rights, who's accountable for execution. 419 00:25:29,480 --> 00:25:31,840 The second is, you know, you're going to have to think about 420 00:25:31,840 --> 00:25:35,200 your org structure that supports a change to make sure that it 421 00:25:35,200 --> 00:25:37,440 creates like sustained, long lasting changes. 422 00:25:37,440 --> 00:25:40,320 And so it's like, you know, how centralized should some of these 423 00:25:40,320 --> 00:25:43,320 capabilities be? Should they be be be you LED, 424 00:25:43,400 --> 00:25:46,560 you know, like some of those decisions have to be have to be 425 00:25:46,560 --> 00:25:50,360 thought through. You need a good change pro 426 00:25:50,400 --> 00:25:53,840 management program and culture in place that identifies the 427 00:25:53,840 --> 00:25:59,040 right sponsors, change agents, activities and communications 428 00:25:59,240 --> 00:26:03,040 that really foster a data culture and improves Gen. 429 00:26:03,040 --> 00:26:06,400 AI and AI literacy across the organization. 430 00:26:06,680 --> 00:26:09,600 Like people have to understand what are these technologies? 431 00:26:09,600 --> 00:26:11,640 Why are we using them? I mean, because I think at the 432 00:26:11,840 --> 00:26:15,160 core of it, you know, people are worried about change. 433 00:26:15,160 --> 00:26:16,680 And especially when you talk about Gen. 434 00:26:16,680 --> 00:26:19,360 AI and AI, people are worried about, does this mean you're 435 00:26:19,360 --> 00:26:20,400 replacing me? You know? 436 00:26:20,400 --> 00:26:24,520 And so having that dialogue around, you know, what we're 437 00:26:24,520 --> 00:26:28,240 trying to do in terms of improving outcomes, how these 438 00:26:28,240 --> 00:26:31,760 technologies can be used, and each person's role in that 439 00:26:31,760 --> 00:26:33,520 journey is going to be very important. 440 00:26:34,800 --> 00:26:39,480 I mentioned earlier that having the right talent is going to be 441 00:26:39,480 --> 00:26:42,000 really important. You're going to need talent that 442 00:26:42,000 --> 00:26:46,480 understands, you know, AI skills and you're going to have to 443 00:26:46,480 --> 00:26:49,560 probably hire for some of these these talent gaps because most 444 00:26:49,560 --> 00:26:53,760 people don't have necessarily, you know, these kinds of skill 445 00:26:53,760 --> 00:26:55,640 sets inside of their organization today. 446 00:26:56,120 --> 00:26:59,960 And beyond just hiring, you actually need to set up programs 447 00:27:00,360 --> 00:27:03,040 to retain and continuously develop this talent. 448 00:27:03,040 --> 00:27:06,920 So they want to stick around. And lastly, you'll need a 449 00:27:06,920 --> 00:27:10,880 governance structure that actually sets up policies on how 450 00:27:10,880 --> 00:27:13,760 to govern the data. Yeah, how to govern the 451 00:27:13,760 --> 00:27:19,200 investments who track results of pilots deployed and, you know, 452 00:27:19,240 --> 00:27:22,240 really work on fully embedding like responsible AI in the 453 00:27:22,240 --> 00:27:25,200 target governance framework. So there's quite a few things 454 00:27:25,200 --> 00:27:29,680 you need to do to really prepare your organization for change and 455 00:27:29,680 --> 00:27:32,240 also bring them along in the change journey. 456 00:27:33,240 --> 00:27:34,560 That's good. That's really good. 457 00:27:34,920 --> 00:27:37,720 I'm also glad you said originally that this is a tool 458 00:27:38,160 --> 00:27:42,440 and this is not something that the companies need to fully rely 459 00:27:42,440 --> 00:27:45,240 on. We still have to put in the work 460 00:27:45,240 --> 00:27:49,040 in the effort to strategize and they come up with a plan since 461 00:27:49,040 --> 00:27:51,600 around our pricing goals and everything in that nature. 462 00:27:51,600 --> 00:27:55,400 And so it's I'm also grateful that you mentioned that. 463 00:27:55,400 --> 00:27:58,600 And I even think about this, you know, a lot of companies don't 464 00:27:58,600 --> 00:28:02,920 have an individual or personnel in their organization that is 465 00:28:02,920 --> 00:28:05,200 familiar with AI or a Gen. AI. 466 00:28:05,360 --> 00:28:09,840 And and even if they do, what are they doing to continue that 467 00:28:10,560 --> 00:28:13,640 individual or those group of people to retain those, those 468 00:28:13,640 --> 00:28:16,480 those people? And so that's that's mind 469 00:28:16,480 --> 00:28:18,680 boggling. But I think it's time continues 470 00:28:18,680 --> 00:28:22,680 to progress programs, things of the things of that nature will 471 00:28:22,680 --> 00:28:27,320 continue to kind of come to surface and give companies more 472 00:28:27,320 --> 00:28:30,800 reason to invest in those to be able to retain such individuals. 473 00:28:31,080 --> 00:28:32,520 That's a good point. They mentioned mentioned as 474 00:28:32,520 --> 00:28:34,360 well. But where do you see this 475 00:28:34,360 --> 00:28:36,400 headed? You know, because we've already 476 00:28:36,400 --> 00:28:40,880 come such a long way, but it feels like, it feels like it's 477 00:28:40,880 --> 00:28:42,040 at the starting point, to be honest. 478 00:28:42,040 --> 00:28:46,440 Yeah, it's interesting because you know, AIML has been around 479 00:28:47,040 --> 00:28:49,840 and so, you know, I think my talk was like, is it high? 480 00:28:50,080 --> 00:28:53,120 You know, is it, you know, high stakes game changer or not? 481 00:28:53,160 --> 00:28:56,760 And, and the reality is, is that there are many use, you have 482 00:28:56,760 --> 00:29:02,760 many examples of traditional AI being very successful and 483 00:29:02,760 --> 00:29:06,440 definitely tried and true. Some organizations do it better 484 00:29:06,440 --> 00:29:08,440 than others. And as I mentioned earlier in in 485 00:29:08,440 --> 00:29:10,880 the talk, you know, Jenny, I, I think is actually only going to 486 00:29:10,880 --> 00:29:13,680 continue to help actually improve the outcomes. 487 00:29:14,280 --> 00:29:17,920 If I'm being perfectly honest, I think that Jenny, I right now 488 00:29:17,920 --> 00:29:20,840 people are experimenting with things, but it's probably a 489 00:29:20,840 --> 00:29:24,320 little bit still more hyped, you know, than in reality. 490 00:29:24,720 --> 00:29:28,840 But this space is moving so quickly. 491 00:29:28,880 --> 00:29:32,160 It's one of those things where it's like, oh, I can ignore it, 492 00:29:32,240 --> 00:29:34,560 you know, for the next like whatever couple of years. 493 00:29:34,560 --> 00:29:39,160 Because the reality is, is that the capabilities with Gen. 494 00:29:39,160 --> 00:29:44,560 AI has propelled the topic of AI in general to the forefront of 495 00:29:44,560 --> 00:29:48,120 the business world. I mean, in my work with clients, 496 00:29:48,200 --> 00:29:51,880 I have so many, you know, we hear so many board members, PE 497 00:29:51,880 --> 00:29:55,760 owners, private equity owners that now want to know from the 498 00:29:55,760 --> 00:29:59,000 management teams like, hey, how are you planning on leveraging 499 00:29:59,000 --> 00:30:02,400 these capabilities just to create a sustained advantage in 500 00:30:02,400 --> 00:30:05,720 the marketplace? And as I shared, like, you know, 501 00:30:05,840 --> 00:30:07,960 using both the traditional and the Gen. 502 00:30:07,960 --> 00:30:11,480 AI capabilities, that's only going to continue to grow. 503 00:30:11,480 --> 00:30:13,040 I don't care what sector you're in. 504 00:30:13,080 --> 00:30:15,400 You know, even if it's slow, it's going to continue to grow. 505 00:30:15,400 --> 00:30:17,640 And in some places it's actually going to move pretty rapidly. 506 00:30:18,280 --> 00:30:23,960 And we know that market leaders already experimenting with new 507 00:30:23,960 --> 00:30:27,320 ways to unlock value for their organizations through these new 508 00:30:27,320 --> 00:30:31,480 capabilities. So my advice is like, don't get 509 00:30:31,760 --> 00:30:35,000 caught flat footed. You know, want to start 510 00:30:35,000 --> 00:30:38,400 experimenting, investing and thinking about these 511 00:30:38,400 --> 00:30:42,040 capabilities because it's going to take time for you to probably 512 00:30:42,040 --> 00:30:44,840 build all the things that you need to internally and get 513 00:30:44,840 --> 00:30:46,760 things, you know, moving in the right direction. 514 00:30:47,160 --> 00:30:50,040 And so I think, you know, now is the time, if you haven't 515 00:30:50,040 --> 00:30:53,000 already, to be seriously thinking about how these 516 00:30:53,160 --> 00:30:56,880 technologies can be used to really up your game in your 517 00:30:56,880 --> 00:30:58,680 business. That's good. 518 00:30:58,960 --> 00:31:01,840 That's good. OK, awesome Gen. 519 00:31:01,920 --> 00:31:05,480 AI pricing hype or high stakes game changer. 520 00:31:05,840 --> 00:31:08,360 Miss Stephanie, thank you so much for your time today. 521 00:31:08,680 --> 00:31:12,240 We are super excited to have you as one of our keynote speakers 522 00:31:12,240 --> 00:31:13,880 for this upcoming fall conference. 523 00:31:14,200 --> 00:31:17,680 I mean, you carry such a a tremendous amount of insight in 524 00:31:17,680 --> 00:31:20,960 this particular topic and you have a lot, a lot of experience 525 00:31:20,960 --> 00:31:22,960 behind you. And so we're super grateful and 526 00:31:22,960 --> 00:31:25,040 excited to have you. One more question for the 527 00:31:25,040 --> 00:31:28,360 listeners. Where can they go to learn more 528 00:31:28,360 --> 00:31:30,800 about you, the company you work for? 529 00:31:30,800 --> 00:31:33,560 Any resources you might want to kind of promote? 530 00:31:33,560 --> 00:31:35,720 Where can they go to learn more about those things? 531 00:31:36,240 --> 00:31:38,520 Yeah. So obviously people can find me 532 00:31:38,520 --> 00:31:44,000 on LinkedIn, on bain.com. We we have information about our 533 00:31:44,000 --> 00:31:47,600 pricing practice and all of the good work we we do there. 534 00:31:47,600 --> 00:31:50,800 And so those are ways in which you're more than welcome to 535 00:31:50,800 --> 00:31:53,400 reach out and we can continue the conversation. 536 00:31:54,400 --> 00:31:56,000 All right. Well, thank you again for your 537 00:31:56,000 --> 00:31:58,880 time today and listeners. And so next time we'll see you 538 00:31:58,880 --> 00:32:00,360 guys later. Have a good one. 539 00:32:00,400 --> 00:32:00,640 Bye bye.