1 00:00:00,120 --> 00:00:02,840 Speaker 1: Hey everyone, it's Robert and Joe here. Today we've got 2 00:00:02,840 --> 00:00:04,720 Speaker 1: something a little bit different to share with you. It 3 00:00:04,840 --> 00:00:08,000 Speaker 1: is a new season of the Smart Talks with IBM 4 00:00:08,119 --> 00:00:09,119 Speaker 1: podcast series. 5 00:00:09,600 --> 00:00:11,680 Speaker 2: Today we are witnessed to one of those rare moments 6 00:00:11,680 --> 00:00:14,360 Speaker 2: in history, the rise of an innovative technology with the 7 00:00:14,360 --> 00:00:18,680 Speaker 2: potential to radically transform business and society forever. The technology, 8 00:00:18,760 --> 00:00:22,200 Speaker 2: of course, is artificial intelligence, and it's the central focus 9 00:00:22,239 --> 00:00:24,800 Speaker 2: for this new season of Smart Talks with IBM. 10 00:00:25,320 --> 00:00:28,400 Speaker 1: Join hosts from your favorite Pushkin podcasts as they talk 11 00:00:28,480 --> 00:00:31,640 Speaker 1: with industry experts and leaders to explore how businesses can 12 00:00:31,680 --> 00:00:35,360 Speaker 1: integrate AI into their workflows and help drive real change 13 00:00:35,400 --> 00:00:38,200 Speaker 1: in this new era of AI. And of course, host 14 00:00:38,280 --> 00:00:40,440 Speaker 1: Malcolm Gladwell will be there to guide you through the 15 00:00:40,479 --> 00:00:42,640 Speaker 1: season and throw in his two cents as well. 16 00:00:43,120 --> 00:00:46,120 Speaker 2: Look out for new episodes of Smart Talks with IBM 17 00:00:46,400 --> 00:00:49,519 Speaker 2: every other week on the iHeartRadio app, Apple Podcasts, or 18 00:00:49,560 --> 00:00:53,360 Speaker 2: wherever you get your podcasts, and learn more at IBM 19 00:00:53,479 --> 00:00:55,480 Speaker 2: dot com slash smart talks. 20 00:00:57,200 --> 00:01:00,840 Speaker 3: Hello, Hello, Welcome to Smart Talks with IBM, a podcast 21 00:01:00,840 --> 00:01:06,760 Speaker 3: from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Glapo. This 22 00:01:06,880 --> 00:01:11,160 Speaker 3: season we're continuing our conversations with new creators visionaries who 23 00:01:11,200 --> 00:01:15,760 Speaker 3: are creatively applying technology in business to drive change, but 24 00:01:16,080 --> 00:01:20,360 Speaker 3: with a focus on the transformative power of artificial intelligence 25 00:01:20,880 --> 00:01:23,679 Speaker 3: and what it means to leverage AI as a game 26 00:01:23,800 --> 00:01:28,959 Speaker 3: changing multiplier for your business. Today's episode highlights the power 27 00:01:29,040 --> 00:01:32,720 Speaker 3: of collaboration. IBM has long been a supporter of the 28 00:01:32,760 --> 00:01:37,520 Speaker 3: better Together mindset and embraces partnerships. They have been working 29 00:01:37,560 --> 00:01:41,280 Speaker 3: together with Salesforce for more than two decades, but have 30 00:01:41,400 --> 00:01:47,319 Speaker 3: recently launched a new collaborative effort surrounding generative AI. Pushkin's 31 00:01:47,360 --> 00:01:50,600 Speaker 3: very own Jacob Goldstein sat down with Matt Candy and 32 00:01:50,680 --> 00:01:55,120 Speaker 3: Susan Emerson. Matt is the global managing partner of Generative 33 00:01:55,200 --> 00:01:59,320 Speaker 3: AI at IBM Consulting, helping clients and partners around the 34 00:01:59,360 --> 00:02:03,920 Speaker 3: world in raised this new era of technology, and Susan 35 00:02:04,400 --> 00:02:08,160 Speaker 3: is a senior vice president for Salesforce dedicated to AI, 36 00:02:08,760 --> 00:02:13,720 Speaker 3: analytics and data. They discussed the historic collaboration between the 37 00:02:13,760 --> 00:02:18,560 Speaker 3: two tech giants, explored the opportunity AI presents for customer service, 38 00:02:19,040 --> 00:02:23,440 Speaker 3: and walk through how businesses can use generative AI to 39 00:02:23,639 --> 00:02:28,520 Speaker 3: interface with clients. Okay, let's get to the conversation. 40 00:02:30,600 --> 00:02:33,799 Speaker 4: Thank you guys for coming this morning. So I'm interested 41 00:02:34,120 --> 00:02:37,639 Speaker 4: in how you both came to generative AI, or maybe 42 00:02:37,639 --> 00:02:39,000 Speaker 4: it sort of came to you in the way it 43 00:02:39,040 --> 00:02:41,040 Speaker 4: sort of came to all of us, But how did 44 00:02:41,080 --> 00:02:43,320 Speaker 4: you arrive at working on generative AI. 45 00:02:43,639 --> 00:02:46,440 Speaker 5: As part of my remitted Salesforce. Over the years, I've 46 00:02:46,480 --> 00:02:50,160 Speaker 5: brought a lot of analytics and data and machine learning 47 00:02:50,200 --> 00:02:54,880 Speaker 5: products to life under the Einstein brand at Salesforce. So 48 00:02:55,400 --> 00:03:00,360 Speaker 5: as we pivoted Salesforce into taking advantage of the enertive 49 00:03:00,400 --> 00:03:03,480 Speaker 5: AI moment, it was natural that I became part of 50 00:03:03,520 --> 00:03:10,440 Speaker 5: the advanced team leveraging generative AI and it's become interesting. 51 00:03:10,520 --> 00:03:14,240 Speaker 5: But what I see as I speak with customers the 52 00:03:14,320 --> 00:03:16,760 Speaker 5: moment that everyone is facing in terms of how they 53 00:03:16,919 --> 00:03:22,839 Speaker 5: incorporate genitive AI into their businesses, their workforces, and their 54 00:03:22,880 --> 00:03:26,160 Speaker 5: technical stacks. It's actually opening up a lot of doors 55 00:03:26,240 --> 00:03:31,239 Speaker 5: to other utility of analytics, data and AI. So it's 56 00:03:31,240 --> 00:03:35,320 Speaker 5: been this big pull through in terms of incorporating not 57 00:03:35,400 --> 00:03:39,520 Speaker 5: just generative AI, but a larger conversation around how we 58 00:03:39,960 --> 00:03:43,640 Speaker 5: become all better using data in our day jobs. 59 00:03:45,200 --> 00:03:47,839 Speaker 4: So that's a great frame for sort of what's going 60 00:03:47,880 --> 00:03:51,480 Speaker 4: on at Salesforce with generative AI. Matt tell us a 61 00:03:51,520 --> 00:03:54,800 Speaker 4: little bit about how that fits with the way IBM 62 00:03:54,920 --> 00:03:55,800 Speaker 4: is approaching the space. 63 00:03:56,560 --> 00:03:59,800 Speaker 6: Yeah, so I guess through three sides to that question. 64 00:04:00,400 --> 00:04:03,280 Speaker 6: And so there's the technology side of it. So IBM 65 00:04:03,320 --> 00:04:06,640 Speaker 6: has a technology organization, and so you know, we are 66 00:04:06,680 --> 00:04:10,360 Speaker 6: building and have been over many years, decades In fact, 67 00:04:10,400 --> 00:04:13,800 Speaker 6: IBM has been working in this space a generative AI 68 00:04:13,960 --> 00:04:21,480 Speaker 6: stack that allows organizations to adopt generative AI technology aimed 69 00:04:21,520 --> 00:04:26,080 Speaker 6: at enterprise and business use within their organizations. So then 70 00:04:26,120 --> 00:04:28,719 Speaker 6: within the consulting business, you know, we have one hundred 71 00:04:28,760 --> 00:04:32,279 Speaker 6: and sixty thousand people who work every day with clients 72 00:04:32,320 --> 00:04:38,000 Speaker 6: across every industry, regulated industries, government organizations, and so this, 73 00:04:38,240 --> 00:04:41,720 Speaker 6: you know, is a really important technology that those companies 74 00:04:41,720 --> 00:04:43,960 Speaker 6: are going to be using to drive the next level 75 00:04:43,960 --> 00:04:48,279 Speaker 6: of transformation in their enterprises processes and the types of 76 00:04:48,279 --> 00:04:51,359 Speaker 6: experiences they build for their customers. And so you know, 77 00:04:51,440 --> 00:04:56,720 Speaker 6: we work extensively with partners technology such as Salesforce, AWS, Microsoft, 78 00:04:57,279 --> 00:05:00,680 Speaker 6: as well as our own technology. And then find I 79 00:05:00,680 --> 00:05:03,520 Speaker 6: guess the third angle is the work that we've got 80 00:05:03,520 --> 00:05:06,440 Speaker 6: to do to reinvent the business of consulting. And so 81 00:05:06,600 --> 00:05:09,760 Speaker 6: if I think about you know, consulting in systems integration. 82 00:05:10,200 --> 00:05:13,719 Speaker 6: You know, ultimately we are knowledge workers, right, and so 83 00:05:14,000 --> 00:05:16,919 Speaker 6: from an industry perspective, I think you know, our industry is, 84 00:05:17,160 --> 00:05:19,839 Speaker 6: same as many others, is going to is going to 85 00:05:19,880 --> 00:05:23,880 Speaker 6: go undergo a level of disruption caused by this technology. 86 00:05:24,160 --> 00:05:26,800 Speaker 6: But therefore that will also create a huge opportunity for 87 00:05:26,880 --> 00:05:27,960 Speaker 6: us as well. 88 00:05:28,000 --> 00:05:30,160 Speaker 7: So those three aspects, Jacob. 89 00:05:30,400 --> 00:05:32,680 Speaker 4: Great, So, so that's the point of view sort of 90 00:05:32,720 --> 00:05:37,080 Speaker 4: from your companies in your work. I'm curious to talk 91 00:05:37,120 --> 00:05:39,720 Speaker 4: for a moment about AI from the point of view 92 00:05:39,800 --> 00:05:44,480 Speaker 4: of consumers and employees kind of out in the world today. 93 00:05:44,520 --> 00:05:47,440 Speaker 4: So just to start with consumers, when I'm just out 94 00:05:48,040 --> 00:05:50,360 Speaker 4: as a person as a consumer in the world, how 95 00:05:50,400 --> 00:05:52,200 Speaker 4: am I experiencing AI today? 96 00:05:53,360 --> 00:05:54,719 Speaker 7: I'll give you a great little use case. 97 00:05:54,760 --> 00:05:58,839 Speaker 6: Actually, I was on holiday three weeks ago in Tenerif 98 00:05:58,880 --> 00:06:01,800 Speaker 6: in Spain, and I was trying to find somewhere to 99 00:06:01,839 --> 00:06:04,240 Speaker 6: park the car with the family for dinner that evening, 100 00:06:04,880 --> 00:06:10,080 Speaker 6: and I found this area next to this kind of 101 00:06:10,160 --> 00:06:13,240 Speaker 6: shopping center and there was this sign there and I 102 00:06:13,279 --> 00:06:15,640 Speaker 6: couldn't quite work out if it was saying I could 103 00:06:15,640 --> 00:06:18,640 Speaker 6: park there or not, And so I took a photo 104 00:06:18,720 --> 00:06:21,599 Speaker 6: of the sign and I uploaded it to an AI 105 00:06:21,960 --> 00:06:24,159 Speaker 6: tool and I said what does this mean? And it 106 00:06:24,200 --> 00:06:26,359 Speaker 6: basically explained to me what the sign was saying and 107 00:06:26,400 --> 00:06:28,520 Speaker 6: basically told me that I shouldn't be parking there, and 108 00:06:28,560 --> 00:06:31,800 Speaker 6: so I drove on and I found some somewhere else 109 00:06:31,839 --> 00:06:36,360 Speaker 6: to park. But you know, that allowed me, in under 110 00:06:36,440 --> 00:06:40,360 Speaker 6: sixty seconds to probably avoid one hundred euro fine by 111 00:06:40,400 --> 00:06:43,880 Speaker 6: parking the car there. So just a simple example, but 112 00:06:43,960 --> 00:06:47,280 Speaker 6: I think the ability that these tools have to take 113 00:06:47,360 --> 00:06:50,240 Speaker 6: friction out of our daily lives, you know, and to 114 00:06:50,279 --> 00:06:53,320 Speaker 6: be able to make just things that we do in 115 00:06:53,320 --> 00:06:55,680 Speaker 6: our everyday life simple and more frictionless. 116 00:06:56,440 --> 00:06:56,600 Speaker 2: You know. 117 00:06:56,680 --> 00:06:59,880 Speaker 6: That's how I look at how mat the consumer's going 118 00:06:59,920 --> 00:07:02,000 Speaker 6: to benefit from some of this type of technology. 119 00:07:02,720 --> 00:07:06,279 Speaker 5: And from my perspective, it's also a travel story. I 120 00:07:06,320 --> 00:07:09,400 Speaker 5: spend a lot of time on the road for work, 121 00:07:09,760 --> 00:07:13,720 Speaker 5: but recently had to send my sister and her family 122 00:07:13,880 --> 00:07:16,840 Speaker 5: to a destination they had never been to for a wedding, 123 00:07:17,520 --> 00:07:21,120 Speaker 5: and it was really quick and easy to use some 124 00:07:21,480 --> 00:07:24,560 Speaker 5: generitive tools to come up with a whole plan for them. 125 00:07:24,560 --> 00:07:27,160 Speaker 5: Because they love to hike and to be outdoors and 126 00:07:27,200 --> 00:07:30,840 Speaker 5: to hike in areas that aren't overly crowded with people, 127 00:07:31,400 --> 00:07:34,760 Speaker 5: and so Jenai very quickly gave me an itinerary of 128 00:07:34,760 --> 00:07:38,200 Speaker 5: a bunch of terrific hikes for them for a destination. 129 00:07:38,560 --> 00:07:40,120 Speaker 7: So things like that great. 130 00:07:40,560 --> 00:07:44,480 Speaker 4: And then what about the effect of AI and of 131 00:07:44,520 --> 00:07:47,600 Speaker 4: automation more generally on employees on the. 132 00:07:47,520 --> 00:07:51,520 Speaker 5: Workforce, Well, there's so many dimensions to take that from 133 00:07:51,800 --> 00:07:55,680 Speaker 5: generitive AI really can up level a workforce in all 134 00:07:55,720 --> 00:07:59,280 Speaker 5: sorts of ways by providing these consistent ways to engage 135 00:07:59,360 --> 00:08:03,400 Speaker 5: with technology, with these natural language experiences. So I think 136 00:08:03,400 --> 00:08:07,360 Speaker 5: it changes everything from it finds us content, it generates 137 00:08:07,440 --> 00:08:10,000 Speaker 5: us content, it makes it easier to work with our 138 00:08:10,040 --> 00:08:16,000 Speaker 5: systems of engagement and operation, and for many organizations it 139 00:08:16,040 --> 00:08:19,160 Speaker 5: can be a lifting factor in terms of bringing a 140 00:08:19,200 --> 00:08:23,760 Speaker 5: more consistent workforce experience because these tools can just be 141 00:08:23,800 --> 00:08:26,679 Speaker 5: ever present in our systems of work. 142 00:08:27,560 --> 00:08:29,640 Speaker 6: I mean, I'll give you a little example here in IBM, 143 00:08:29,800 --> 00:08:33,120 Speaker 6: we have something called our skjar and so that's our 144 00:08:33,200 --> 00:08:38,200 Speaker 6: conversational AI interface that we use to interact with HR 145 00:08:38,400 --> 00:08:43,080 Speaker 6: services and ninety four percent of every employee interaction now 146 00:08:43,520 --> 00:08:47,760 Speaker 6: happens without human intervention through that interface. But you would 147 00:08:47,760 --> 00:08:50,520 Speaker 6: never know that. And so if I think about, you know, 148 00:08:51,200 --> 00:08:54,520 Speaker 6: our HR processes. You know, we have this amazing conversational 149 00:08:54,559 --> 00:08:57,880 Speaker 6: based AI that we use for all of our HR 150 00:08:58,040 --> 00:09:02,280 Speaker 6: interactions and we surface that through SLACK, and so SLACK 151 00:09:02,320 --> 00:09:05,360 Speaker 6: becomes the front door for how we access a lot 152 00:09:05,400 --> 00:09:09,360 Speaker 6: of these different enterprise processes and capabilities and how we 153 00:09:09,440 --> 00:09:11,839 Speaker 6: surface AI. In fact, I'm taking a flight shortly back 154 00:09:11,840 --> 00:09:14,679 Speaker 6: to the UK and our our skhar boss is reminding 155 00:09:14,720 --> 00:09:16,439 Speaker 6: me that it's raining in the UK and I should 156 00:09:16,440 --> 00:09:17,559 Speaker 6: take an umbrella. 157 00:09:18,360 --> 00:09:20,840 Speaker 5: Isn't it always like raining in England? 158 00:09:23,120 --> 00:09:25,240 Speaker 6: Yeah, I don't think there's any AI needed for that. 159 00:09:25,280 --> 00:09:28,600 Speaker 6: I think that's just a hard coded If England, then 160 00:09:28,640 --> 00:09:29,360 Speaker 6: take umbrella. 161 00:09:29,440 --> 00:09:30,520 Speaker 7: That's right, that's just a rule. 162 00:09:30,679 --> 00:09:33,760 Speaker 6: That's just a rule, right, and you're able to converse 163 00:09:33,800 --> 00:09:35,840 Speaker 6: and yeah, I need to book holiday, I need to 164 00:09:35,880 --> 00:09:38,880 Speaker 6: move somebody between managers. I need to figure out the 165 00:09:38,920 --> 00:09:43,120 Speaker 6: policy on this. And the AI basically navigates across the 166 00:09:43,160 --> 00:09:46,040 Speaker 6: different systems to be able to help get that information, 167 00:09:46,240 --> 00:09:48,800 Speaker 6: to summarize it back to be able to carry out 168 00:09:48,800 --> 00:09:51,480 Speaker 6: the transactions that I need carried out, and it just 169 00:09:51,760 --> 00:09:54,720 Speaker 6: removes all of that complexity and makes it easier to 170 00:09:54,720 --> 00:09:55,319 Speaker 6: get things. 171 00:09:55,160 --> 00:10:04,520 Speaker 4: Done when you are working with companies to implement generative AI. Now, 172 00:10:05,240 --> 00:10:08,600 Speaker 4: what do you find tends to be their primary focus? 173 00:10:09,280 --> 00:10:11,560 Speaker 5: I mean I speak with a lot of customers each week, 174 00:10:11,640 --> 00:10:15,880 Speaker 5: and for the last several months, most organizations have just 175 00:10:15,960 --> 00:10:20,160 Speaker 5: been reorienting themselves in terms of where are we in 176 00:10:20,200 --> 00:10:23,760 Speaker 5: this moment, what is this technology capable of? What are 177 00:10:23,800 --> 00:10:27,400 Speaker 5: the risks and governance and frameworks that I need to 178 00:10:27,760 --> 00:10:31,920 Speaker 5: establish in order to engage and talk to everyone. Talk 179 00:10:31,960 --> 00:10:34,960 Speaker 5: to my vendors, talk to my cloud providers, talk to 180 00:10:35,000 --> 00:10:39,840 Speaker 5: my consultants, talk to academics, and generally get your sea 181 00:10:39,920 --> 00:10:44,320 Speaker 5: legs under them. And the sort of the unstructured hand 182 00:10:44,360 --> 00:10:48,600 Speaker 5: on keyboards fiddling with technology seems to be moving towards 183 00:10:48,760 --> 00:10:51,280 Speaker 5: let's get some points on the board, let's turn this 184 00:10:51,320 --> 00:10:54,040 Speaker 5: stuff on and go. So that's what I've been seeing 185 00:10:54,120 --> 00:10:58,360 Speaker 5: in terms of the work within the salesforce ecosystem. Matt, 186 00:10:58,360 --> 00:11:02,079 Speaker 5: you've got a larger apperture as well. What are you seeing? 187 00:11:02,679 --> 00:11:05,280 Speaker 7: Yeah, so I definitely agree. 188 00:11:05,320 --> 00:11:10,400 Speaker 6: I think, you know, there's been lots of getting sea legs, experimentation, 189 00:11:10,760 --> 00:11:12,840 Speaker 6: just trying to build knowledge, being able to try and 190 00:11:13,840 --> 00:11:18,240 Speaker 6: build almost you know, internal organizational point of view and 191 00:11:18,280 --> 00:11:21,160 Speaker 6: reference framework. I've seen lots of what I would have 192 00:11:21,200 --> 00:11:22,920 Speaker 6: referred to as random acts of AI. 193 00:11:24,920 --> 00:11:27,280 Speaker 7: In terms of in terms of experimentation. 194 00:11:27,320 --> 00:11:29,439 Speaker 6: But I think I think people now looking into twenty 195 00:11:29,480 --> 00:11:32,600 Speaker 6: twenty four and this is all about now adoption and scaling, 196 00:11:33,200 --> 00:11:37,040 Speaker 6: what's become really clear is organizations have started to realize 197 00:11:37,080 --> 00:11:39,200 Speaker 6: this is going to be a very multi model world 198 00:11:39,280 --> 00:11:40,760 Speaker 6: that they're going to live in. There is no one 199 00:11:40,800 --> 00:11:44,560 Speaker 6: AI that is the answer for their organization, and so 200 00:11:44,840 --> 00:11:48,240 Speaker 6: they're going to have lots of different generative AI models 201 00:11:48,960 --> 00:11:51,520 Speaker 6: and technologies that they're going to sit in the organization 202 00:11:51,840 --> 00:11:56,760 Speaker 6: servicing different use cases, different domain areas, different products and services, 203 00:11:58,040 --> 00:12:00,959 Speaker 6: and so therefore having to figure out how they're going 204 00:12:01,000 --> 00:12:04,079 Speaker 6: to navigate and manage this kind of open world that 205 00:12:04,080 --> 00:12:06,360 Speaker 6: they're going to be sitting in and the decisions that 206 00:12:06,360 --> 00:12:08,800 Speaker 6: they're going to have to make around that. I think 207 00:12:08,840 --> 00:12:12,079 Speaker 6: the second thing that I've seen that people are now 208 00:12:12,120 --> 00:12:14,559 Speaker 6: becoming very clear that this needs to be what I 209 00:12:14,600 --> 00:12:17,199 Speaker 6: would refer to as use case lead and outcome focus, 210 00:12:18,600 --> 00:12:21,960 Speaker 6: and so really needing to start with thinking about the 211 00:12:22,040 --> 00:12:25,000 Speaker 6: business outcome and the problem that you know, we're trying 212 00:12:25,080 --> 00:12:29,080 Speaker 6: to solve, and therefore, how do I use generative AI 213 00:12:29,760 --> 00:12:33,160 Speaker 6: as part of the mechanism to solve that problem. And 214 00:12:33,200 --> 00:12:35,840 Speaker 6: I think, you know, what Susan and the Salesforce team 215 00:12:35,880 --> 00:12:37,720 Speaker 6: do is an amazing example of that. You know, they've 216 00:12:37,720 --> 00:12:42,120 Speaker 6: got this incredible platform and engine that allows companies to 217 00:12:42,160 --> 00:12:45,199 Speaker 6: transform their sales and service processes and to be able 218 00:12:45,240 --> 00:12:47,280 Speaker 6: to put data in the hands of users, to be 219 00:12:47,280 --> 00:12:50,320 Speaker 6: able to make better decisions, et cetera. And so now 220 00:12:50,360 --> 00:12:53,200 Speaker 6: by weaving generative AI into that platform, we're going to 221 00:12:53,200 --> 00:12:56,199 Speaker 6: be able to make those processes workflows even more efficient. Right, 222 00:12:56,280 --> 00:12:59,240 Speaker 6: So it's generative AI plus all of these other amazing 223 00:12:59,240 --> 00:13:02,079 Speaker 6: things that are there. It will be led through business outcome, 224 00:13:02,520 --> 00:13:04,960 Speaker 6: and it will be led through use case and the 225 00:13:05,040 --> 00:13:08,480 Speaker 6: business problem or workflow that we're trying to improve. And 226 00:13:08,520 --> 00:13:10,320 Speaker 6: then I think the third thing is shifting from this 227 00:13:10,440 --> 00:13:13,520 Speaker 6: experimentation to scale. You know, I think everybody's really early 228 00:13:13,559 --> 00:13:17,960 Speaker 6: in this journey, but what's become clear is that you know, 229 00:13:18,360 --> 00:13:22,320 Speaker 6: everybody now need realizes and is starting to lay down 230 00:13:22,400 --> 00:13:27,280 Speaker 6: these ground rules, the guardrails, the frameworks to allow them 231 00:13:27,760 --> 00:13:31,719 Speaker 6: to scale this across the organization. So, you know, I 232 00:13:32,080 --> 00:13:34,760 Speaker 6: think we're in for an exciting, exciting time in twenty 233 00:13:34,840 --> 00:13:35,320 Speaker 6: twenty four. 234 00:13:36,040 --> 00:13:38,760 Speaker 4: So now that we're getting to this moment, what are 235 00:13:38,800 --> 00:13:42,079 Speaker 4: the key things companies have to figure out about scaling 236 00:13:42,160 --> 00:13:42,840 Speaker 4: generative AI. 237 00:13:45,240 --> 00:13:48,520 Speaker 5: I would put that in kind of two categories, and 238 00:13:48,559 --> 00:13:52,199 Speaker 5: following on what Matt was saying in terms of use, 239 00:13:52,240 --> 00:13:55,720 Speaker 5: case defined and outcome, LAD one hundred percent on that 240 00:13:55,760 --> 00:13:58,480 Speaker 5: in terms of starting with a hypothesis of value while 241 00:13:58,520 --> 00:14:02,160 Speaker 5: at the same time people are getting you know, closer 242 00:14:02,160 --> 00:14:04,600 Speaker 5: to the technology to know what their bounds are. But 243 00:14:04,640 --> 00:14:07,720 Speaker 5: the biggest, you know, set of conversations is in the 244 00:14:07,880 --> 00:14:13,040 Speaker 5: enterprise area in terms of embarking and using with generative AI, 245 00:14:13,520 --> 00:14:16,280 Speaker 5: how to do it in ways that is safe for 246 00:14:17,280 --> 00:14:21,000 Speaker 5: use of data that is safe around not just the 247 00:14:21,120 --> 00:14:26,120 Speaker 5: larger topic of generative AI and hallucinations, which which are 248 00:14:26,160 --> 00:14:27,520 Speaker 5: fun to talk about in the media. 249 00:14:27,680 --> 00:14:30,440 Speaker 4: But it's a fun word, right If it was called 250 00:14:30,480 --> 00:14:33,640 Speaker 4: something other than hallucinations, people wouldn't talk about it as much. 251 00:14:33,720 --> 00:14:34,200 Speaker 2: It was just. 252 00:14:34,280 --> 00:14:38,440 Speaker 5: Mistakes, Yeah, that's right, just things that aren't factually true. 253 00:14:38,640 --> 00:14:40,840 Speaker 5: We've been doing a lot of work at Salesforce around 254 00:14:40,920 --> 00:14:44,800 Speaker 5: using you know, dynamic and structured grounding the data so 255 00:14:44,840 --> 00:14:49,120 Speaker 5: we can give very strong and non naive prompt instructions 256 00:14:49,120 --> 00:14:51,920 Speaker 5: to lllms to get return on that. So so just 257 00:14:51,920 --> 00:14:55,560 Speaker 5: to summarize top of mind for organizations using you know, 258 00:14:55,840 --> 00:14:59,040 Speaker 5: large language models, is using their data in ways that 259 00:14:59,120 --> 00:15:04,440 Speaker 5: are safe, trusted, not exposed, and reducing the opportunity for 260 00:15:04,480 --> 00:15:07,520 Speaker 5: hallucinations and maximizing relevant content. 261 00:15:08,080 --> 00:15:08,360 Speaker 2: Great. 262 00:15:08,440 --> 00:15:10,920 Speaker 4: So, Matt Susan was talking about, you know, both what 263 00:15:11,080 --> 00:15:15,000 Speaker 4: organizations are concerned with as a scale generative AI and 264 00:15:15,040 --> 00:15:18,560 Speaker 4: how Salesforce is working to sort of address those concerns. 265 00:15:19,120 --> 00:15:21,840 Speaker 4: What are you seeing at IBM here? 266 00:15:21,920 --> 00:15:26,960 Speaker 6: So, I think certainly from a scaling of generative AI perspective, 267 00:15:27,000 --> 00:15:30,760 Speaker 6: you know, this topic of governance, you know, and how 268 00:15:30,840 --> 00:15:32,800 Speaker 6: organizations are going to have to govern all of these 269 00:15:32,800 --> 00:15:38,360 Speaker 6: models that sit withinside, how they manage kind of bias fairness, 270 00:15:38,560 --> 00:15:41,320 Speaker 6: model drift, you know, if you think about the data 271 00:15:41,360 --> 00:15:44,880 Speaker 6: that's gone into a model and the output it gives 272 00:15:44,920 --> 00:15:47,800 Speaker 6: to start with, not because the model changes, but because 273 00:15:47,840 --> 00:15:50,120 Speaker 6: the context of the world moves on. And so being 274 00:15:50,160 --> 00:15:52,200 Speaker 6: able to kind of manage this model drift is going 275 00:15:52,280 --> 00:15:55,200 Speaker 6: to be a really important thing. I think data really matters, 276 00:15:55,640 --> 00:16:00,480 Speaker 6: and so quality access security around data within the enterprise 277 00:16:00,600 --> 00:16:03,240 Speaker 6: is going to be critical to scaling generative AI. And 278 00:16:03,280 --> 00:16:05,320 Speaker 6: the other one I think that's going to be really important, 279 00:16:05,320 --> 00:16:07,880 Speaker 6: and I think many organizations haven't even got there yet. 280 00:16:07,880 --> 00:16:11,240 Speaker 6: In their thinking is around the ESG implications. So carbon 281 00:16:12,080 --> 00:16:14,480 Speaker 6: you know, the use of this technology does not come 282 00:16:14,560 --> 00:16:16,040 Speaker 6: without a cost of carbon. 283 00:16:16,760 --> 00:16:19,040 Speaker 4: Carbon meaning it's very energy intensive. 284 00:16:19,400 --> 00:16:23,000 Speaker 6: Correct, Yeah, the training of the models and so thinking 285 00:16:23,000 --> 00:16:27,080 Speaker 6: about carbon disclosures and thinking about where I'm infusing it 286 00:16:27,120 --> 00:16:29,440 Speaker 6: into my business and how much I'm using it and 287 00:16:29,520 --> 00:16:32,440 Speaker 6: what the carbon cost of that is. As I think 288 00:16:32,480 --> 00:16:37,840 Speaker 6: about the you know, you know, my own organizational responsibilities 289 00:16:37,840 --> 00:16:39,880 Speaker 6: to reduce carbon I think, you know, there's all of 290 00:16:39,920 --> 00:16:42,080 Speaker 6: these things that I think are going to become important 291 00:16:42,160 --> 00:16:45,200 Speaker 6: factors as people are thinking about the scaling implications of 292 00:16:45,240 --> 00:16:46,040 Speaker 6: this technology. 293 00:16:47,000 --> 00:16:50,480 Speaker 3: AI is already making new experiences possible, but we must 294 00:16:50,520 --> 00:16:53,840 Speaker 3: be mindful in how we integrate this new technology as 295 00:16:53,840 --> 00:16:58,480 Speaker 3: we continue scaling generative AI. Matt touched on some crucial 296 00:16:58,520 --> 00:17:02,960 Speaker 3: aspects from an IBM perspective. Governance, bias, fairness, and security 297 00:17:03,240 --> 00:17:07,080 Speaker 3: are all key considerations when organizations aim to expand their 298 00:17:07,160 --> 00:17:12,280 Speaker 3: use of generative AI. The environmental aspect is especially important, 299 00:17:12,840 --> 00:17:15,919 Speaker 3: and it's refreshing to hear leading thinkers like Matt and 300 00:17:16,000 --> 00:17:21,120 Speaker 3: Susan highlight these issues As this technology continues to evolve. 301 00:17:21,640 --> 00:17:26,520 Speaker 3: These factors are becoming increasingly important for organizations to address 302 00:17:27,400 --> 00:17:31,560 Speaker 3: the historic collaboration between IBM and Salesforce is helping to 303 00:17:31,680 --> 00:17:35,040 Speaker 3: remedy issues companies face when scaling AI. 304 00:17:36,200 --> 00:17:41,119 Speaker 4: So IBM and Salesforce recently announced a new collaborative project 305 00:17:41,280 --> 00:17:44,240 Speaker 4: around generative AI. Tell me more about that. 306 00:17:45,400 --> 00:17:50,119 Speaker 6: We've been partners for over two decades now IBM and Salesforce, 307 00:17:50,160 --> 00:17:54,040 Speaker 6: and so within our consulting business, we work with Salesforce 308 00:17:54,200 --> 00:17:57,400 Speaker 6: technology to help our clients implement that technology to transform 309 00:17:57,440 --> 00:18:01,480 Speaker 6: their businesses. We've got a huge practice, over twelve thousand 310 00:18:01,880 --> 00:18:05,840 Speaker 6: people with certifications around Salesforce platforms, and so you know, 311 00:18:05,880 --> 00:18:07,920 Speaker 6: as Susan and her team and the broader team in 312 00:18:07,960 --> 00:18:12,040 Speaker 6: Salesforce are infusing more capability into the platform around generative AI, 313 00:18:12,600 --> 00:18:15,400 Speaker 6: then our mission is really simple. It's to help clients 314 00:18:15,960 --> 00:18:20,040 Speaker 6: who are using the Salesforce platform adopt those capabilities to help. 315 00:18:19,920 --> 00:18:22,119 Speaker 7: Them get more benefit within their organization. 316 00:18:22,680 --> 00:18:25,879 Speaker 6: You know, we're also a significant user of Salesforce technology 317 00:18:25,880 --> 00:18:29,399 Speaker 6: within IBM. We're one of Salesforce's largest customers globally, and 318 00:18:29,480 --> 00:18:32,280 Speaker 6: so you know, as we continue to transform our own 319 00:18:32,359 --> 00:18:36,440 Speaker 6: sales and service processes within IBM, then you know our 320 00:18:36,560 --> 00:18:39,960 Speaker 6: use of the generative AI capabilities that they're infusing into sales, 321 00:18:40,000 --> 00:18:43,359 Speaker 6: cloud service, cloud slack, et cetera will be something that 322 00:18:43,359 --> 00:18:47,520 Speaker 6: will become really important to us driving productivity within the company. 323 00:18:47,920 --> 00:18:49,439 Speaker 6: And then the other thing that I would say is, 324 00:18:49,680 --> 00:18:51,240 Speaker 6: you know, as I think about the work that we 325 00:18:51,320 --> 00:18:54,240 Speaker 6: do with clients, you know, as they're implementing and on 326 00:18:54,280 --> 00:18:57,240 Speaker 6: their generative AI journeys, you know they're going to utilize 327 00:18:57,240 --> 00:19:00,760 Speaker 6: and leverage the salesforce capabilities within the platform and their 328 00:19:00,800 --> 00:19:05,040 Speaker 6: generative AI technologies. But then you start thinking about processes 329 00:19:05,080 --> 00:19:07,959 Speaker 6: and workflows that run beyond the walls of CRM right 330 00:19:08,000 --> 00:19:11,359 Speaker 6: that run into supply chain and into the finance area 331 00:19:11,359 --> 00:19:14,360 Speaker 6: of the organization. And so there is work that we're 332 00:19:14,359 --> 00:19:17,200 Speaker 6: doing with clients where we're using ibms. What's the next 333 00:19:17,280 --> 00:19:21,080 Speaker 6: platform to be able to help get access to to 334 00:19:21,200 --> 00:19:24,399 Speaker 6: generate insights from data sources that sit in all of 335 00:19:24,440 --> 00:19:27,239 Speaker 6: these kind of back office areas of the enterprise and 336 00:19:27,280 --> 00:19:29,959 Speaker 6: to be able to get that data across the salesforce, 337 00:19:30,040 --> 00:19:34,160 Speaker 6: into these customer interaction points and into the employees who 338 00:19:34,160 --> 00:19:39,080 Speaker 6: are servicing those customers using salesforces AI and generative AI technologies. 339 00:19:39,080 --> 00:19:42,120 Speaker 6: So there's a kind of one plus one equals three 340 00:19:42,320 --> 00:19:45,560 Speaker 6: kind of you know, better together, you know, and being 341 00:19:45,560 --> 00:19:48,320 Speaker 6: able to bring our technologies together in service of these 342 00:19:48,320 --> 00:19:52,280 Speaker 6: clients problems as you think about these processes that run 343 00:19:52,320 --> 00:19:56,520 Speaker 6: across their enterprise. So yeah, so huge hut unity and 344 00:19:56,520 --> 00:19:58,880 Speaker 6: what we're doing together in the market to help clients. 345 00:19:59,480 --> 00:20:02,720 Speaker 5: Yeah, building on that, it is a huge moment for 346 00:20:03,880 --> 00:20:07,240 Speaker 5: organizations and for technology companies like Salesforce, and we couldn't 347 00:20:07,280 --> 00:20:10,360 Speaker 5: be happier to have partnerships like we have with IBM. 348 00:20:10,880 --> 00:20:16,040 Speaker 5: Like the range of thought leadership that is appropriate at 349 00:20:16,040 --> 00:20:19,520 Speaker 5: the moment is everything from what is that hypothesis of 350 00:20:19,600 --> 00:20:22,359 Speaker 5: value and what are those use cases? And what is 351 00:20:22,400 --> 00:20:25,000 Speaker 5: the order of operation in terms of approaching it just 352 00:20:25,040 --> 00:20:28,760 Speaker 5: in terms of focus, but then things that would help 353 00:20:28,880 --> 00:20:33,280 Speaker 5: organizations assess their AI readiness and then their approach Like 354 00:20:33,320 --> 00:20:37,040 Speaker 5: you know, we talked earlier about frameworks and guardrails. You know, 355 00:20:37,080 --> 00:20:40,000 Speaker 5: what are use cases that we're comfortable with given the 356 00:20:40,040 --> 00:20:43,440 Speaker 5: state of the technology that face employees or face customers. 357 00:20:43,480 --> 00:20:46,760 Speaker 5: So creating these much larger roadmaps in terms of how 358 00:20:46,800 --> 00:20:51,080 Speaker 5: to approach this over a series of initiatives, the way 359 00:20:51,280 --> 00:20:55,200 Speaker 5: it can fundamentally change the way we engage with technology 360 00:20:55,800 --> 00:20:59,479 Speaker 5: and what that means for the you know, training and 361 00:20:59,560 --> 00:21:04,399 Speaker 5: change management and use cases that fundamentally shift how you 362 00:21:05,320 --> 00:21:09,080 Speaker 5: engage with systems like Salesforce. There's just a massive opportunity 363 00:21:09,200 --> 00:21:09,919 Speaker 5: for us together. 364 00:21:10,720 --> 00:21:14,560 Speaker 4: So you're talking in sort of general terms, I'm interested in, 365 00:21:14,760 --> 00:21:19,120 Speaker 4: you know, thinking in particular about the way generitive AI 366 00:21:19,240 --> 00:21:23,159 Speaker 4: can essentially lead to better business outcomes, right Like, what 367 00:21:23,200 --> 00:21:25,760 Speaker 4: does that look like? How do you measure it? You know, 368 00:21:25,880 --> 00:21:28,360 Speaker 4: there's a certain bottom line question there, right like, how 369 00:21:28,359 --> 00:21:30,879 Speaker 4: does AI make businesses work better? And in what ways? 370 00:21:31,520 --> 00:21:35,800 Speaker 5: You know, as consumers of products and services, we all 371 00:21:35,840 --> 00:21:38,440 Speaker 5: love and respect great service, you know, in terms of 372 00:21:38,480 --> 00:21:41,800 Speaker 5: getting timely, quick answers, resolving issues quickly, all those those 373 00:21:41,840 --> 00:21:46,800 Speaker 5: types of things. And from the perspective of using generitive 374 00:21:46,880 --> 00:21:51,040 Speaker 5: and predictive capabilities for agents who are interacting with customers, 375 00:21:51,400 --> 00:21:54,640 Speaker 5: there is just a whole ton of opportunity to take 376 00:21:54,680 --> 00:21:57,120 Speaker 5: friction out of the process in terms of finding answers, 377 00:21:57,200 --> 00:22:00,919 Speaker 5: resolving issues, in terms of using these generatives capabilities that 378 00:22:00,960 --> 00:22:04,080 Speaker 5: will bring you know, answers and content to the fingertips 379 00:22:04,119 --> 00:22:09,399 Speaker 5: more easily to the human agents that are working with customers. Now, 380 00:22:09,480 --> 00:22:13,080 Speaker 5: taking that to the next step, for organizations when they're 381 00:22:13,119 --> 00:22:16,960 Speaker 5: ready to move into more customer facing automation. That's yet 382 00:22:17,000 --> 00:22:19,800 Speaker 5: another channel. As a consumer, we'll all enjoy with the 383 00:22:19,840 --> 00:22:22,000 Speaker 5: brands and the products and the services that we want 384 00:22:22,040 --> 00:22:25,600 Speaker 5: in terms of fast answers and resolutions to customers, and 385 00:22:25,640 --> 00:22:30,400 Speaker 5: as we all know, great customer experience yields return business. 386 00:22:30,880 --> 00:22:34,119 Speaker 5: Now on the sales side, you know, maybe a different example, 387 00:22:34,720 --> 00:22:37,639 Speaker 5: and these are areas where I think the capability of 388 00:22:37,840 --> 00:22:41,320 Speaker 5: predictive and generative go very well together in terms of 389 00:22:41,359 --> 00:22:45,160 Speaker 5: focusing on business outcomes. And a classic example would be, 390 00:22:45,680 --> 00:22:50,040 Speaker 5: you know, predictions that help us understand customer health. You know, 391 00:22:50,160 --> 00:22:54,120 Speaker 5: is this customer engaged, is this customer at risk? Predictions 392 00:22:54,119 --> 00:22:58,240 Speaker 5: that help us understand next best product or next best conversation. 393 00:22:58,760 --> 00:23:04,400 Speaker 5: These all help focus sales team's time on a customer 394 00:23:04,480 --> 00:23:08,080 Speaker 5: or a territory, and so that deep focus puts all 395 00:23:08,080 --> 00:23:10,359 Speaker 5: the wood behind an arrow, so to speak, in terms 396 00:23:10,440 --> 00:23:14,919 Speaker 5: of where we should be engaging. And those types of 397 00:23:15,400 --> 00:23:20,119 Speaker 5: driven sales organizations that have these capabilities just lead to 398 00:23:20,160 --> 00:23:25,000 Speaker 5: better performance and outcomes and customer experience too. Now, let's 399 00:23:25,040 --> 00:23:29,480 Speaker 5: also layer in generitive capabilities, where we're using the generative 400 00:23:29,520 --> 00:23:33,560 Speaker 5: capabilities to assist and augment a sales team where we're 401 00:23:33,640 --> 00:23:37,680 Speaker 5: using the power de generitive for everything like generating personalized 402 00:23:37,760 --> 00:23:42,600 Speaker 5: and relevant customer interaction content, for example, leveraging our customer 403 00:23:42,720 --> 00:23:47,639 Speaker 5: data like engagement history, product purchases, service history to create 404 00:23:47,640 --> 00:23:51,080 Speaker 5: an email or a campaign. And this scale of automation 405 00:23:51,240 --> 00:23:54,240 Speaker 5: has just never been possible before. And you know, maybe 406 00:23:54,280 --> 00:23:56,719 Speaker 5: even taking this one step further with genitive where we 407 00:23:56,760 --> 00:23:59,400 Speaker 5: take all the administrative friction out of the day job 408 00:24:00,119 --> 00:24:03,119 Speaker 5: doing things for sales teams like summarizing their calls or 409 00:24:03,160 --> 00:24:06,240 Speaker 5: creating a meeting plan for them, and you know, very 410 00:24:06,280 --> 00:24:10,200 Speaker 5: broadly speaking, using generative AI to change the interaction mode 411 00:24:10,280 --> 00:24:14,960 Speaker 5: with systems like Salesforce from clicks and training where people 412 00:24:15,000 --> 00:24:18,160 Speaker 5: have to focus on the process to more conversational user 413 00:24:18,240 --> 00:24:22,480 Speaker 5: experiences which are much more engaging and easier to use. 414 00:24:22,920 --> 00:24:26,880 Speaker 5: So all of this together is just incredible and transformational 415 00:24:27,240 --> 00:24:30,280 Speaker 5: and makes all businesses and people work better. 416 00:24:30,880 --> 00:24:33,360 Speaker 4: So I just want to spend one more moment on 417 00:24:33,440 --> 00:24:38,879 Speaker 4: the partnership between IBM and Salesforce and generitive AI. And 418 00:24:38,920 --> 00:24:43,080 Speaker 4: there's this phrase that's interesting to me. It's ecosystem partnership 419 00:24:43,359 --> 00:24:46,200 Speaker 4: that I think is relevant here. So what is an 420 00:24:46,200 --> 00:24:51,760 Speaker 4: ecosystem partnership and why is it helpful in creating scalable 421 00:24:51,840 --> 00:24:52,879 Speaker 4: AI solutions. 422 00:24:53,800 --> 00:24:57,879 Speaker 6: This idea of being open, I think is probably one 423 00:24:57,920 --> 00:25:02,119 Speaker 6: of the most important premises for US as technology companies, 424 00:25:02,200 --> 00:25:06,320 Speaker 6: for us as consultancies and system integrators, and for our clients. 425 00:25:06,359 --> 00:25:10,080 Speaker 6: To think about the sources of value that can be 426 00:25:10,119 --> 00:25:14,000 Speaker 6: created through taking an open approach is hugely important. So 427 00:25:14,320 --> 00:25:18,040 Speaker 6: if I think about for US, ecosystem means making sure 428 00:25:18,080 --> 00:25:22,320 Speaker 6: that we have all of the different partnerships that we 429 00:25:22,400 --> 00:25:26,880 Speaker 6: need with technology providers, with service providers that we can 430 00:25:27,920 --> 00:25:32,080 Speaker 6: bring to our clients the right set of capabilities to 431 00:25:32,119 --> 00:25:34,560 Speaker 6: solve the problem that they've got, and not thinking that 432 00:25:35,000 --> 00:25:38,000 Speaker 6: just you know, what we have in house, or what 433 00:25:38,080 --> 00:25:40,399 Speaker 6: we have with just one other partner that we work with, 434 00:25:40,520 --> 00:25:42,639 Speaker 6: you know, is the right thing. And so you know, 435 00:25:42,680 --> 00:25:45,800 Speaker 6: I think every problem that our clients have is solved 436 00:25:45,880 --> 00:25:50,160 Speaker 6: through a range of technologies that come together in service 437 00:25:50,240 --> 00:25:52,200 Speaker 6: of creating that business outcome. 438 00:25:52,680 --> 00:25:58,080 Speaker 4: I want to touch briefly on ethics and governance. Something 439 00:25:58,160 --> 00:26:03,119 Speaker 4: like eighty percent of CEOs see explainability, ethics, bias, trust 440 00:26:03,359 --> 00:26:07,760 Speaker 4: as major concerns on the road to AI adoption, and 441 00:26:07,800 --> 00:26:12,720 Speaker 4: so I'm curious how business leaders navigate these things, and 442 00:26:12,760 --> 00:26:17,320 Speaker 4: in particular, how Salesforce and IBM are building these concerns 443 00:26:17,359 --> 00:26:19,719 Speaker 4: into how they work with customers. 444 00:26:20,400 --> 00:26:25,440 Speaker 5: You know, we've been incorporating predictive machine learning into our 445 00:26:25,480 --> 00:26:29,199 Speaker 5: products since mid last decade, and at that time we 446 00:26:29,280 --> 00:26:33,159 Speaker 5: started with all of our ethics and governance work at 447 00:26:33,200 --> 00:26:36,399 Speaker 5: that time in terms of frameworks for engaging with AI 448 00:26:36,880 --> 00:26:40,280 Speaker 5: and ethical and safe ways, and have a lot of 449 00:26:40,280 --> 00:26:43,800 Speaker 5: guidance for customers in terms of those programs. The machine 450 00:26:43,880 --> 00:26:47,200 Speaker 5: learning focus that we've had at Salesforce has always been 451 00:26:47,280 --> 00:26:51,560 Speaker 5: deeply focused on explainability. So if we're making you know, 452 00:26:51,640 --> 00:26:56,080 Speaker 5: predictive recommendations to explain how we got to that, you know, 453 00:26:56,119 --> 00:26:59,560 Speaker 5: whether that's something that a user sees as they're engaging 454 00:26:59,560 --> 00:27:02,920 Speaker 5: with it, they have full trust in terms of interacting 455 00:27:02,960 --> 00:27:06,560 Speaker 5: with it, but also for the practitioners who are building it. 456 00:27:07,000 --> 00:27:10,359 Speaker 5: So we have this like long standing vibe and capability 457 00:27:10,520 --> 00:27:13,840 Speaker 5: with our predictive side of the house and on the 458 00:27:13,880 --> 00:27:16,280 Speaker 5: generative side of the house. You know, the state of 459 00:27:16,320 --> 00:27:20,080 Speaker 5: the marketplace right now is llms for most people are 460 00:27:20,560 --> 00:27:25,040 Speaker 5: largely black boxes in terms of not fully interpretable in 461 00:27:25,119 --> 00:27:27,920 Speaker 5: terms of how they come up with their content. Now 462 00:27:27,960 --> 00:27:30,120 Speaker 5: that said, there is a lot that you can do 463 00:27:30,760 --> 00:27:34,880 Speaker 5: in terms of audit, in terms of you know, transparency, 464 00:27:34,960 --> 00:27:37,800 Speaker 5: in terms of what are the prompts that are being 465 00:27:37,920 --> 00:27:42,680 Speaker 5: submitted to these llms, what do these llms provide back 466 00:27:42,720 --> 00:27:45,359 Speaker 5: in terms of return, and then what did the human 467 00:27:45,480 --> 00:27:48,119 Speaker 5: do to change it, use it, or adjust it. So 468 00:27:48,160 --> 00:27:51,719 Speaker 5: we've been updating all of our ethics and governance frameworks 469 00:27:51,760 --> 00:27:54,440 Speaker 5: now I guess I would call it with safety components 470 00:27:54,480 --> 00:27:56,960 Speaker 5: as well, in terms of how to work with data 471 00:27:57,840 --> 00:28:01,200 Speaker 5: in safe ways and with these turned parents governance models. 472 00:28:01,520 --> 00:28:03,719 Speaker 6: Yeah, So I mean this is an area that IBM 473 00:28:03,760 --> 00:28:06,120 Speaker 6: has been kind of working on for many years. And 474 00:28:06,200 --> 00:28:08,639 Speaker 6: so you know, our AI Ethics Board that we have 475 00:28:08,800 --> 00:28:13,439 Speaker 6: internally kind of governs and provides frameworks and guidance for 476 00:28:13,520 --> 00:28:15,919 Speaker 6: everything that we do in the company. There's a lot 477 00:28:15,960 --> 00:28:18,399 Speaker 6: of work that we do to help our clients and 478 00:28:18,560 --> 00:28:22,919 Speaker 6: organizations establish their strategies for AI governance as well as 479 00:28:22,960 --> 00:28:27,600 Speaker 6: their own internal policies, models, approaches, ethics boards, et cetera. 480 00:28:28,320 --> 00:28:30,920 Speaker 6: And so you know, helping them put in place these 481 00:28:31,400 --> 00:28:38,160 Speaker 6: ground rules and guardrails, organizational process changes, et cetera. I 482 00:28:38,160 --> 00:28:40,680 Speaker 6: think is a really important part of this scaling discussion. 483 00:28:40,720 --> 00:28:43,000 Speaker 6: That we were having earlier, as people are going to 484 00:28:43,000 --> 00:28:45,600 Speaker 6: be kind of rolling out more of this technology internally, 485 00:28:46,520 --> 00:28:50,360 Speaker 6: and then I think there's a lot that organizations are 486 00:28:50,360 --> 00:28:52,000 Speaker 6: going to have to do to think about, especially in 487 00:28:52,000 --> 00:28:55,000 Speaker 6: the generative world, around all of the different types of 488 00:28:55,040 --> 00:28:58,959 Speaker 6: models that they're using, models that they're training and tuning 489 00:28:59,000 --> 00:29:01,320 Speaker 6: and building, and how they manage all of those for 490 00:29:01,400 --> 00:29:07,680 Speaker 6: explainability and bias drift, and actually regulatory requirements, Like if 491 00:29:07,760 --> 00:29:11,520 Speaker 6: you think about what's happening around the world, there's different countries, 492 00:29:12,120 --> 00:29:15,400 Speaker 6: the EUAI Act, you know, there's lots of different regulatory 493 00:29:15,440 --> 00:29:17,280 Speaker 6: requirements that are going to be coming in, and so 494 00:29:17,360 --> 00:29:23,719 Speaker 6: for multinational companies operating across multiple countries, how they're going 495 00:29:23,760 --> 00:29:27,160 Speaker 6: to have to make sure that they're complying with all 496 00:29:27,200 --> 00:29:31,240 Speaker 6: of not only their own internal policies, but the requirements 497 00:29:31,280 --> 00:29:37,160 Speaker 6: of the country as well as potentially industry regulatory requirements 498 00:29:37,200 --> 00:29:38,360 Speaker 6: as well. 499 00:29:38,400 --> 00:29:39,720 Speaker 7: And so there's a lot that. 500 00:29:39,680 --> 00:29:42,560 Speaker 6: We are doing and going to be doing in helping 501 00:29:42,600 --> 00:29:46,440 Speaker 6: them manage complexity. But IBM has a very firm view 502 00:29:46,480 --> 00:29:49,560 Speaker 6: that we believe that this is all about regulating AI risk, 503 00:29:50,080 --> 00:29:53,800 Speaker 6: not AI algorithms, and so focusing on precision regulation. 504 00:29:54,160 --> 00:29:58,680 Speaker 7: So you know, use the bodies and regulatory bodies that. 505 00:29:58,640 --> 00:30:02,760 Speaker 6: Are out there to provide the control as opposed to 506 00:30:02,760 --> 00:30:04,240 Speaker 6: trying to regulate the technology. 507 00:30:05,040 --> 00:30:09,000 Speaker 4: So genitive AI is changing kind of absurdly quickly. Right, 508 00:30:09,040 --> 00:30:10,440 Speaker 4: a year and a half ago, we wouldn't have been 509 00:30:10,480 --> 00:30:14,040 Speaker 4: having this conversation. We're here today. Everything's happening now. I'm 510 00:30:14,040 --> 00:30:17,080 Speaker 4: curious what you both think about about the near term 511 00:30:17,120 --> 00:30:20,080 Speaker 4: future of genitive AI. Right, if we came back in 512 00:30:20,200 --> 00:30:21,920 Speaker 4: a year, or let's say two years from now, if 513 00:30:21,920 --> 00:30:24,520 Speaker 4: we came back two years from now to talk about 514 00:30:24,560 --> 00:30:26,760 Speaker 4: the work you're doing in genitive AI, what would we. 515 00:30:26,800 --> 00:30:27,400 Speaker 7: Be talking about. 516 00:30:29,120 --> 00:30:33,160 Speaker 5: I use this example sometimes I have three kids, and 517 00:30:33,840 --> 00:30:37,640 Speaker 5: I don't think any of them have ever gone into 518 00:30:37,680 --> 00:30:40,560 Speaker 5: a bank to deposit a check. Right, They pull out 519 00:30:40,600 --> 00:30:43,520 Speaker 5: their mobile phone and they scan the check with the 520 00:30:43,560 --> 00:30:44,680 Speaker 5: camera and they're done. 521 00:30:44,840 --> 00:30:46,880 Speaker 4: I'm surprised that they even know what a check is, 522 00:30:46,960 --> 00:30:47,960 Speaker 4: for the record. 523 00:30:47,680 --> 00:30:51,680 Speaker 5: But yes, well yeah, sometimes their parents give them one, 524 00:30:51,920 --> 00:30:55,880 Speaker 5: like they get direct deposit. But anyway, like this experience 525 00:30:56,000 --> 00:30:58,840 Speaker 5: of like, what do you mean a go into a 526 00:30:58,880 --> 00:31:00,840 Speaker 5: branch in cash at check? Just do this with my 527 00:31:00,920 --> 00:31:03,760 Speaker 5: mobile phone? And I think a little bit of it 528 00:31:03,840 --> 00:31:05,920 Speaker 5: that way in terms of the systems that we use 529 00:31:05,960 --> 00:31:10,640 Speaker 5: at work. I can imagine explaining to my kids, like, oh, yeah, 530 00:31:10,880 --> 00:31:13,680 Speaker 5: at Salesforce, you know, back when someone had their first 531 00:31:13,720 --> 00:31:16,840 Speaker 5: day on the job, you know, as a service agent 532 00:31:16,920 --> 00:31:19,520 Speaker 5: or as a salesperson, they would have tabs on the 533 00:31:19,560 --> 00:31:22,360 Speaker 5: screen and they would be trained where to click, and 534 00:31:22,360 --> 00:31:27,080 Speaker 5: they'd have documented processes in manuals, and that showed them 535 00:31:27,120 --> 00:31:29,720 Speaker 5: where to get from point A to point B. And 536 00:31:29,800 --> 00:31:33,280 Speaker 5: as the clock turns forward, they're just interacting with the 537 00:31:33,400 --> 00:31:38,320 Speaker 5: natural language prompt. But it just kind of fundamentally changes 538 00:31:38,760 --> 00:31:41,320 Speaker 5: the way we'll be able to interact with our systems 539 00:31:41,360 --> 00:31:42,120 Speaker 5: or record at work. 540 00:31:42,640 --> 00:31:46,000 Speaker 4: It'll be just much more conversational. Instead of clicking through something, 541 00:31:46,120 --> 00:31:48,480 Speaker 4: you'll just basically have a conversation. 542 00:31:48,280 --> 00:31:49,520 Speaker 5: Much more conversational. 543 00:31:49,720 --> 00:31:52,080 Speaker 6: Yeah, this is the biggest paradigm shift in how we 544 00:31:52,160 --> 00:31:54,760 Speaker 6: interact with technology I think since the invention of the 545 00:31:54,760 --> 00:31:58,440 Speaker 6: graphical user interface, and it's going to enable us to 546 00:31:58,560 --> 00:32:02,920 Speaker 6: almost put us all of that complexity within organizations around 547 00:32:02,960 --> 00:32:06,840 Speaker 6: system silos, process silos flows, because you're just going to 548 00:32:06,920 --> 00:32:11,040 Speaker 6: layer this just simple natural language interface over all of 549 00:32:11,040 --> 00:32:11,960 Speaker 6: that complexity. 550 00:32:12,880 --> 00:32:15,200 Speaker 7: Yeah, it's going to amplify. 551 00:32:15,320 --> 00:32:18,080 Speaker 6: I think the potential of every person on every team 552 00:32:18,120 --> 00:32:21,040 Speaker 6: in a way that we've never been able to see before. 553 00:32:21,120 --> 00:32:23,760 Speaker 6: And the other thing that I think as you project 554 00:32:23,760 --> 00:32:25,840 Speaker 6: forward in a couple of years, and Susan just picking 555 00:32:25,880 --> 00:32:27,840 Speaker 6: up on the point that you talked about about banking, 556 00:32:28,520 --> 00:32:28,720 Speaker 6: you know. 557 00:32:28,720 --> 00:32:30,720 Speaker 7: I think there's a wonderful little example. 558 00:32:30,960 --> 00:32:32,720 Speaker 6: Look, if you think back to the seventies and the 559 00:32:32,720 --> 00:32:35,920 Speaker 6: eighties when the ATM kind of cash machines were rolling out, 560 00:32:36,640 --> 00:32:40,560 Speaker 6: and at that time, it wasn't really a reaction that 561 00:32:40,680 --> 00:32:43,400 Speaker 6: was one of awe or appreciation for convenience, but people 562 00:32:43,440 --> 00:32:46,760 Speaker 6: were concerned that we were automating away the bank teller jobs. 563 00:32:47,520 --> 00:32:47,760 Speaker 2: Right. 564 00:32:47,920 --> 00:32:51,200 Speaker 6: But now, when you think about it, what actually happened 565 00:32:51,320 --> 00:32:55,960 Speaker 6: was this technology allowed the banks to scale their branch networks, 566 00:32:56,080 --> 00:32:59,360 Speaker 6: more branches never before, more bank tellers than ever before, 567 00:33:00,200 --> 00:33:03,600 Speaker 6: teler employment and salaries increased, even though we automated them 568 00:33:03,600 --> 00:33:06,360 Speaker 6: out of work, because when they weren't having to spend 569 00:33:06,400 --> 00:33:09,280 Speaker 6: their time counting cash out for people, they were able 570 00:33:09,280 --> 00:33:11,560 Speaker 6: to do more valuable things, right, and new types of 571 00:33:11,640 --> 00:33:15,000 Speaker 6: financial products and services and mortgages and so like. If 572 00:33:15,040 --> 00:33:17,480 Speaker 6: I think back to that in the seventies and eighties, 573 00:33:17,520 --> 00:33:19,720 Speaker 6: and then I project to where we are today, we're 574 00:33:19,720 --> 00:33:23,800 Speaker 6: just going to unleash this creativity and potential for employees 575 00:33:23,800 --> 00:33:26,600 Speaker 6: and enterprises by freeing up the time that they're spending 576 00:33:26,640 --> 00:33:28,880 Speaker 6: on things that you know, they can do far more 577 00:33:28,960 --> 00:33:31,080 Speaker 6: value added tasks. And so I think we're going to 578 00:33:31,120 --> 00:33:34,840 Speaker 6: be amazed. I think around what happens and what companies 579 00:33:34,880 --> 00:33:36,360 Speaker 6: and people are going to be able to do as 580 00:33:36,360 --> 00:33:38,640 Speaker 6: we give them the time and space to be able 581 00:33:38,640 --> 00:33:39,920 Speaker 6: to do that great. 582 00:33:40,560 --> 00:33:43,720 Speaker 4: So, just to close, I want to talk about how 583 00:33:43,760 --> 00:33:47,320 Speaker 4: both of you use creativity in your own work. Just 584 00:33:47,320 --> 00:33:49,520 Speaker 4: to start with you, Matt, I know that you love 585 00:33:49,560 --> 00:33:55,440 Speaker 4: to combine creativity and technology through design. Do you use 586 00:33:55,520 --> 00:33:57,720 Speaker 4: generative AI in your own creative process? 587 00:33:58,280 --> 00:33:58,640 Speaker 7: Yeah? 588 00:33:58,720 --> 00:34:02,600 Speaker 6: So, I I'm a firm believer that this combination of 589 00:34:02,640 --> 00:34:05,400 Speaker 6: experience in AI is going to be the thing that 590 00:34:05,440 --> 00:34:08,040 Speaker 6: makes a difference. Like these large language models, and this 591 00:34:08,320 --> 00:34:10,840 Speaker 6: technology has been around actually for a number of years, 592 00:34:11,400 --> 00:34:14,520 Speaker 6: and it's only at the point late twenty twenty two 593 00:34:14,640 --> 00:34:18,200 Speaker 6: where open AI wrapped a digital experience around this and 594 00:34:18,239 --> 00:34:20,440 Speaker 6: put it in the hands of people that suddenly the 595 00:34:20,480 --> 00:34:24,120 Speaker 6: transformative power of this technology was realized. And so I 596 00:34:24,120 --> 00:34:27,680 Speaker 6: think the way that we surface these capabilities and put 597 00:34:27,680 --> 00:34:30,520 Speaker 6: them in the hands of people to be able to 598 00:34:30,600 --> 00:34:33,600 Speaker 6: adopt it in a really frictionless way is the thing 599 00:34:33,640 --> 00:34:36,400 Speaker 6: that's going to be hugely important to the adoption and 600 00:34:36,440 --> 00:34:39,239 Speaker 6: scaling of this. So I think the most important thing 601 00:34:39,280 --> 00:34:42,440 Speaker 6: for companies to do is to make people, not technology 602 00:34:42,600 --> 00:34:44,000 Speaker 6: central to their strategy. 603 00:34:44,680 --> 00:34:48,000 Speaker 4: Just to go more broadly into your work, Susan, I mean, 604 00:34:48,200 --> 00:34:52,520 Speaker 4: I know that you have launched Salesforce's AI products into 605 00:34:52,560 --> 00:34:54,520 Speaker 4: the market, and that you know a lot of those 606 00:34:54,560 --> 00:34:58,680 Speaker 4: have been built obviously given Salesforce business around helping people 607 00:34:58,760 --> 00:35:03,279 Speaker 4: build stronger customer relationships, right, And so I'm curious what 608 00:35:03,360 --> 00:35:04,960 Speaker 4: creativity did you bring to that work. 609 00:35:05,840 --> 00:35:08,399 Speaker 5: Some of the products that I've worked with a Salesforce there, 610 00:35:08,520 --> 00:35:12,960 Speaker 5: they're deeply visually focused. And my personal perspective is is 611 00:35:13,000 --> 00:35:17,480 Speaker 5: that the world can be really noisy. We're just inundated 612 00:35:18,120 --> 00:35:21,160 Speaker 5: with all sorts of demands on our time through so 613 00:35:21,280 --> 00:35:24,520 Speaker 5: many channels. Right, Like the phone is firing off, you're 614 00:35:24,520 --> 00:35:28,640 Speaker 5: getting instant messages, you're getting slack messages, you're getting you know, dms, 615 00:35:28,920 --> 00:35:32,319 Speaker 5: you're getting emails, your phone is ringing. There's processes that 616 00:35:32,400 --> 00:35:35,680 Speaker 5: are bearing down on you. And if we can use 617 00:35:35,719 --> 00:35:40,560 Speaker 5: really good design to filter out and essentially weed the garden, 618 00:35:41,080 --> 00:35:43,320 Speaker 5: because you know, we have this this phrase at Salesforce 619 00:35:43,400 --> 00:35:47,440 Speaker 5: is everything. If everything's important, nothing's important. So using really 620 00:35:47,480 --> 00:35:52,279 Speaker 5: good design to create the user experience in Salesforce, that 621 00:35:52,840 --> 00:35:56,080 Speaker 5: just brings stuff to life in the most powerful way. 622 00:35:56,520 --> 00:35:58,839 Speaker 5: So I always think of it from that perspective, like, 623 00:35:58,920 --> 00:36:01,960 Speaker 5: if I'm going to put this on a screen and Salesforce, 624 00:36:02,560 --> 00:36:04,799 Speaker 5: what did I not put on? Is this the most 625 00:36:04,840 --> 00:36:07,480 Speaker 5: important thing? And is this the thing that's going to 626 00:36:07,480 --> 00:36:10,759 Speaker 5: align everyone to the larger initiative of the firm. So 627 00:36:11,239 --> 00:36:14,800 Speaker 5: it's that kind of design thinking that I use probably 628 00:36:14,840 --> 00:36:17,640 Speaker 5: every moment of the day, whether I'm building a demo 629 00:36:18,160 --> 00:36:20,560 Speaker 5: or talking to an executive as a company in terms 630 00:36:20,560 --> 00:36:22,279 Speaker 5: of as I see a vision for how they might 631 00:36:22,280 --> 00:36:25,480 Speaker 5: deploy our products to actual product development. 632 00:36:26,760 --> 00:36:28,920 Speaker 4: Just to kind of bring together these two themes we've 633 00:36:28,960 --> 00:36:30,920 Speaker 4: been talking about, on the one hand, the sort of 634 00:36:31,239 --> 00:36:35,319 Speaker 4: ecosystem partnerships and on the other hand, creativity, I mean, 635 00:36:35,600 --> 00:36:40,440 Speaker 4: can you talk a little bit about how working with 636 00:36:40,520 --> 00:36:43,399 Speaker 4: partners can foster a different kind of creativity. 637 00:36:44,080 --> 00:36:47,000 Speaker 5: More perspectives are always better than few perspectives. 638 00:36:47,680 --> 00:36:48,760 Speaker 7: I completely agree. 639 00:36:48,800 --> 00:36:52,759 Speaker 6: I think the more minds, the more perspectives, the more experiences. 640 00:36:54,080 --> 00:36:56,279 Speaker 6: You know, if I think about some of the best sessions, 641 00:36:56,640 --> 00:37:00,560 Speaker 6: best workshops, best work we do with clients when you've 642 00:37:00,600 --> 00:37:05,440 Speaker 6: got people not just from one industry, but from many industries, 643 00:37:05,520 --> 00:37:09,080 Speaker 6: because actually the adjacencies and the things that are happening 644 00:37:09,120 --> 00:37:12,160 Speaker 6: in these other spaces trigger new thoughts and new ideas, 645 00:37:12,200 --> 00:37:15,160 Speaker 6: and so, you know, I think the richness that we 646 00:37:15,239 --> 00:37:19,560 Speaker 6: get when we partner with salesforce together around helping clients 647 00:37:19,600 --> 00:37:23,560 Speaker 6: transform their front office, their sales service marketing processes. We 648 00:37:23,640 --> 00:37:26,360 Speaker 6: all bring these unique experiences, and I think that just 649 00:37:26,520 --> 00:37:30,920 Speaker 6: opens the aperture to better outcomes and better perspectives for 650 00:37:31,000 --> 00:37:31,560 Speaker 6: our clients. 651 00:37:32,520 --> 00:37:34,719 Speaker 5: Well, you know, you've been asking these questions about like 652 00:37:34,960 --> 00:37:38,160 Speaker 5: the use of tech and AI and creativity are sort 653 00:37:38,160 --> 00:37:40,239 Speaker 5: of in the same sentence. And one of the things 654 00:37:40,239 --> 00:37:44,040 Speaker 5: that I also think of is in terms of remaining 655 00:37:44,080 --> 00:37:48,600 Speaker 5: deeply creative, is the actual process of unplugging from all 656 00:37:48,600 --> 00:37:53,399 Speaker 5: that stuff. So taking a trail run with no earphones 657 00:37:53,600 --> 00:37:57,560 Speaker 5: in your head, for me, is always a really good 658 00:37:57,560 --> 00:38:02,120 Speaker 5: way of unleashing and writing a lot of creative spirit. 659 00:38:02,719 --> 00:38:06,200 Speaker 5: Just that downtime and the unstructured time where your brain 660 00:38:06,280 --> 00:38:09,200 Speaker 5: can just run free, actually not assisted by any kind 661 00:38:09,200 --> 00:38:11,600 Speaker 5: of device in my head or in my face. 662 00:38:11,760 --> 00:38:15,840 Speaker 4: So I think with that praise of unplugged time, we 663 00:38:15,880 --> 00:38:18,960 Speaker 4: should say goodbye and let's unplug it. It's lovely to talk. 664 00:38:18,840 --> 00:38:19,279 Speaker 3: With you guys. 665 00:38:19,280 --> 00:38:21,480 Speaker 4: It was really interesting to learn about your work and 666 00:38:21,520 --> 00:38:24,120 Speaker 4: the relationship between the company. So thank you for your time. 667 00:38:24,840 --> 00:38:26,120 Speaker 7: Thank you, Jacob, thank you. 668 00:38:27,640 --> 00:38:30,000 Speaker 3: A huge thanks is due to Jacob, Matt and Susan 669 00:38:30,080 --> 00:38:35,279 Speaker 3: for illuminating the possibilities of generative AI. This technology has 670 00:38:35,320 --> 00:38:39,200 Speaker 3: great promise for creating new experiences in the future, but 671 00:38:39,360 --> 00:38:44,920 Speaker 3: requires the scaling capabilities made possible by partnerships like IBM 672 00:38:45,480 --> 00:38:49,719 Speaker 3: and Salesforce. As our conversation with Susan and Matt illustrated, 673 00:38:50,120 --> 00:38:54,360 Speaker 3: we're at an exciting phase of adoption. Most companies have 674 00:38:54,480 --> 00:38:59,160 Speaker 3: moved beyond experimentation and are now prioritizing scaling. The key 675 00:38:59,160 --> 00:39:04,120 Speaker 3: areas of focus for organizations now include managing multiple AI models, 676 00:39:04,560 --> 00:39:09,680 Speaker 3: as well as thinking about specific use cases and desired outcomes. However, 677 00:39:09,760 --> 00:39:12,879 Speaker 3: this scale is difficult for companies to do on their own. 678 00:39:13,480 --> 00:39:18,200 Speaker 3: To unlock the real potential of generative AI in transforming experiences, 679 00:39:18,560 --> 00:39:23,960 Speaker 3: they'll require the scaling capabilities made possible by partnerships like IBM, 680 00:39:24,400 --> 00:39:29,160 Speaker 3: and salesforce. This conversation showed the promise of teamwork. When 681 00:39:29,200 --> 00:39:33,239 Speaker 3: massive companies combine their brain power to push forward technology, 682 00:39:33,600 --> 00:39:40,160 Speaker 3: their collaborative efforts have the potential to revolutionize industries. One 683 00:39:40,239 --> 00:39:43,160 Speaker 3: quick programming note, we will be taking a little time 684 00:39:43,200 --> 00:39:46,000 Speaker 3: off and will be returning in just a few weeks 685 00:39:46,520 --> 00:39:50,320 Speaker 3: with a new episode. Smart Talks with IBM is produced 686 00:39:50,320 --> 00:39:55,040 Speaker 3: by Matt Romano, Joey fish Ground, David Jaw and Jacob Goldstein. 687 00:39:55,480 --> 00:39:59,240 Speaker 3: We're edited by Lydia gene Kott. Our engineers are Jason Gambrel, 688 00:39:59,600 --> 00:40:05,440 Speaker 3: Sarah Bruger and Ben Holliday. Theme song by Gramoscope. Special 689 00:40:05,480 --> 00:40:08,960 Speaker 3: thanks to Andy Kelly, Kathy Callahan and the eight Bar 690 00:40:09,120 --> 00:40:13,000 Speaker 3: and IBM teams, as well as the Pushkin marketing team. 691 00:40:13,360 --> 00:40:16,320 Speaker 3: Smart Talks with IBM is a production of Pushkin Industries 692 00:40:16,560 --> 00:40:21,040 Speaker 3: and Ruby Studio at iHeartMedia. To find more Pushkin podcasts, 693 00:40:21,360 --> 00:40:25,680 Speaker 3: listen on the iHeartRadio app, Apple Podcasts, or wherever you 694 00:40:25,840 --> 00:40:30,360 Speaker 3: listen to podcasts. I'm Malcolm Gladwell. This is a paid 695 00:40:30,360 --> 00:40:39,080 Speaker 3: advertisement from IBM.