WEBVTT - Smart Talks with IBM: Salesforce & IBM: Revolutionizing Experiences with Generative AI 0:00:00.120 --> 0:00:02.840 Hey everyone, it's Robert and Joe here. Today we've got 0:00:02.840 --> 0:00:04.720 something a little bit different to share with you. It 0:00:04.840 --> 0:00:08.000 is a new season of the Smart Talks with IBM 0:00:08.119 --> 0:00:09.119 podcast series. 0:00:09.600 --> 0:00:11.680 Today we are witnessed to one of those rare moments 0:00:11.680 --> 0:00:14.360 in history, the rise of an innovative technology with the 0:00:14.360 --> 0:00:18.680 potential to radically transform business and society forever. The technology, 0:00:18.760 --> 0:00:22.200 of course, is artificial intelligence, and it's the central focus 0:00:22.239 --> 0:00:24.800 for this new season of Smart Talks with IBM. 0:00:25.320 --> 0:00:28.400 Join hosts from your favorite Pushkin podcasts as they talk 0:00:28.480 --> 0:00:31.640 with industry experts and leaders to explore how businesses can 0:00:31.680 --> 0:00:35.360 integrate AI into their workflows and help drive real change 0:00:35.400 --> 0:00:38.200 in this new era of AI. And of course, host 0:00:38.280 --> 0:00:40.440 Malcolm Gladwell will be there to guide you through the 0:00:40.479 --> 0:00:42.640 season and throw in his two cents as well. 0:00:43.120 --> 0:00:46.120 Look out for new episodes of Smart Talks with IBM 0:00:46.400 --> 0:00:49.519 every other week on the iHeartRadio app, Apple Podcasts, or 0:00:49.560 --> 0:00:53.360 wherever you get your podcasts, and learn more at IBM 0:00:53.479 --> 0:00:55.480 dot com slash smart talks. 0:00:57.200 --> 0:01:00.840 Hello, Hello, Welcome to Smart Talks with IBM, a podcast 0:01:00.840 --> 0:01:06.760 from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Glapo. This 0:01:06.880 --> 0:01:11.160 season we're continuing our conversations with new creators visionaries who 0:01:11.200 --> 0:01:15.760 are creatively applying technology in business to drive change, but 0:01:16.080 --> 0:01:20.360 with a focus on the transformative power of artificial intelligence 0:01:20.880 --> 0:01:23.679 and what it means to leverage AI as a game 0:01:23.800 --> 0:01:28.959 changing multiplier for your business. Today's episode highlights the power 0:01:29.040 --> 0:01:32.720 of collaboration. IBM has long been a supporter of the 0:01:32.760 --> 0:01:37.520 better Together mindset and embraces partnerships. They have been working 0:01:37.560 --> 0:01:41.280 together with Salesforce for more than two decades, but have 0:01:41.400 --> 0:01:47.319 recently launched a new collaborative effort surrounding generative AI. Pushkin's 0:01:47.360 --> 0:01:50.600 very own Jacob Goldstein sat down with Matt Candy and 0:01:50.680 --> 0:01:55.120 Susan Emerson. Matt is the global managing partner of Generative 0:01:55.200 --> 0:01:59.320 AI at IBM Consulting, helping clients and partners around the 0:01:59.360 --> 0:02:03.920 world in raised this new era of technology, and Susan 0:02:04.400 --> 0:02:08.160 is a senior vice president for Salesforce dedicated to AI, 0:02:08.760 --> 0:02:13.720 analytics and data. They discussed the historic collaboration between the 0:02:13.760 --> 0:02:18.560 two tech giants, explored the opportunity AI presents for customer service, 0:02:19.040 --> 0:02:23.440 and walk through how businesses can use generative AI to 0:02:23.639 --> 0:02:28.520 interface with clients. Okay, let's get to the conversation. 0:02:30.600 --> 0:02:33.799 Thank you guys for coming this morning. So I'm interested 0:02:34.120 --> 0:02:37.639 in how you both came to generative AI, or maybe 0:02:37.639 --> 0:02:39.000 it sort of came to you in the way it 0:02:39.040 --> 0:02:41.040 sort of came to all of us, But how did 0:02:41.080 --> 0:02:43.320 you arrive at working on generative AI. 0:02:43.639 --> 0:02:46.440 As part of my remitted Salesforce. Over the years, I've 0:02:46.480 --> 0:02:50.160 brought a lot of analytics and data and machine learning 0:02:50.200 --> 0:02:54.880 products to life under the Einstein brand at Salesforce. So 0:02:55.400 --> 0:03:00.360 as we pivoted Salesforce into taking advantage of the enertive 0:03:00.400 --> 0:03:03.480 AI moment, it was natural that I became part of 0:03:03.520 --> 0:03:10.440 the advanced team leveraging generative AI and it's become interesting. 0:03:10.520 --> 0:03:14.240 But what I see as I speak with customers the 0:03:14.320 --> 0:03:16.760 moment that everyone is facing in terms of how they 0:03:16.919 --> 0:03:22.839 incorporate genitive AI into their businesses, their workforces, and their 0:03:22.880 --> 0:03:26.160 technical stacks. It's actually opening up a lot of doors 0:03:26.240 --> 0:03:31.239 to other utility of analytics, data and AI. So it's 0:03:31.240 --> 0:03:35.320 been this big pull through in terms of incorporating not 0:03:35.400 --> 0:03:39.520 just generative AI, but a larger conversation around how we 0:03:39.960 --> 0:03:43.640 become all better using data in our day jobs. 0:03:45.200 --> 0:03:47.839 So that's a great frame for sort of what's going 0:03:47.880 --> 0:03:51.480 on at Salesforce with generative AI. Matt tell us a 0:03:51.520 --> 0:03:54.800 little bit about how that fits with the way IBM 0:03:54.920 --> 0:03:55.800 is approaching the space. 0:03:56.560 --> 0:03:59.800 Yeah, so I guess through three sides to that question. 0:04:00.400 --> 0:04:03.280 And so there's the technology side of it. So IBM 0:04:03.320 --> 0:04:06.640 has a technology organization, and so you know, we are 0:04:06.680 --> 0:04:10.360 building and have been over many years, decades In fact, 0:04:10.400 --> 0:04:13.800 IBM has been working in this space a generative AI 0:04:13.960 --> 0:04:21.480 stack that allows organizations to adopt generative AI technology aimed 0:04:21.520 --> 0:04:26.080 at enterprise and business use within their organizations. So then 0:04:26.120 --> 0:04:28.719 within the consulting business, you know, we have one hundred 0:04:28.760 --> 0:04:32.279 and sixty thousand people who work every day with clients 0:04:32.320 --> 0:04:38.000 across every industry, regulated industries, government organizations, and so this, 0:04:38.240 --> 0:04:41.720 you know, is a really important technology that those companies 0:04:41.720 --> 0:04:43.960 are going to be using to drive the next level 0:04:43.960 --> 0:04:48.279 of transformation in their enterprises processes and the types of 0:04:48.279 --> 0:04:51.359 experiences they build for their customers. And so you know, 0:04:51.440 --> 0:04:56.720 we work extensively with partners technology such as Salesforce, AWS, Microsoft, 0:04:57.279 --> 0:05:00.680 as well as our own technology. And then find I 0:05:00.680 --> 0:05:03.520 guess the third angle is the work that we've got 0:05:03.520 --> 0:05:06.440 to do to reinvent the business of consulting. And so 0:05:06.600 --> 0:05:09.760 if I think about you know, consulting in systems integration. 0:05:10.200 --> 0:05:13.719 You know, ultimately we are knowledge workers, right, and so 0:05:14.000 --> 0:05:16.919 from an industry perspective, I think you know, our industry is, 0:05:17.160 --> 0:05:19.839 same as many others, is going to is going to 0:05:19.880 --> 0:05:23.880 go undergo a level of disruption caused by this technology. 0:05:24.160 --> 0:05:26.800 But therefore that will also create a huge opportunity for 0:05:26.880 --> 0:05:27.960 us as well. 0:05:28.000 --> 0:05:30.160 So those three aspects, Jacob. 0:05:30.400 --> 0:05:32.680 Great, So, so that's the point of view sort of 0:05:32.720 --> 0:05:37.080 from your companies in your work. I'm curious to talk 0:05:37.120 --> 0:05:39.720 for a moment about AI from the point of view 0:05:39.800 --> 0:05:44.480 of consumers and employees kind of out in the world today. 0:05:44.520 --> 0:05:47.440 So just to start with consumers, when I'm just out 0:05:48.040 --> 0:05:50.360 as a person as a consumer in the world, how 0:05:50.400 --> 0:05:52.200 am I experiencing AI today? 0:05:53.360 --> 0:05:54.719 I'll give you a great little use case. 0:05:54.760 --> 0:05:58.839 Actually, I was on holiday three weeks ago in Tenerif 0:05:58.880 --> 0:06:01.800 in Spain, and I was trying to find somewhere to 0:06:01.839 --> 0:06:04.240 park the car with the family for dinner that evening, 0:06:04.880 --> 0:06:10.080 and I found this area next to this kind of 0:06:10.160 --> 0:06:13.240 shopping center and there was this sign there and I 0:06:13.279 --> 0:06:15.640 couldn't quite work out if it was saying I could 0:06:15.640 --> 0:06:18.640 park there or not, And so I took a photo 0:06:18.720 --> 0:06:21.599 of the sign and I uploaded it to an AI 0:06:21.960 --> 0:06:24.159 tool and I said what does this mean? And it 0:06:24.200 --> 0:06:26.359 basically explained to me what the sign was saying and 0:06:26.400 --> 0:06:28.520 basically told me that I shouldn't be parking there, and 0:06:28.560 --> 0:06:31.800 so I drove on and I found some somewhere else 0:06:31.839 --> 0:06:36.360 to park. But you know, that allowed me, in under 0:06:36.440 --> 0:06:40.360 sixty seconds to probably avoid one hundred euro fine by 0:06:40.400 --> 0:06:43.880 parking the car there. So just a simple example, but 0:06:43.960 --> 0:06:47.280 I think the ability that these tools have to take 0:06:47.360 --> 0:06:50.240 friction out of our daily lives, you know, and to 0:06:50.279 --> 0:06:53.320 be able to make just things that we do in 0:06:53.320 --> 0:06:55.680 our everyday life simple and more frictionless. 0:06:56.440 --> 0:06:56.600 You know. 0:06:56.680 --> 0:06:59.880 That's how I look at how mat the consumer's going 0:06:59.920 --> 0:07:02.000 to benefit from some of this type of technology. 0:07:02.720 --> 0:07:06.279 And from my perspective, it's also a travel story. I 0:07:06.320 --> 0:07:09.400 spend a lot of time on the road for work, 0:07:09.760 --> 0:07:13.720 but recently had to send my sister and her family 0:07:13.880 --> 0:07:16.840 to a destination they had never been to for a wedding, 0:07:17.520 --> 0:07:21.120 and it was really quick and easy to use some 0:07:21.480 --> 0:07:24.560 generitive tools to come up with a whole plan for them. 0:07:24.560 --> 0:07:27.160 Because they love to hike and to be outdoors and 0:07:27.200 --> 0:07:30.840 to hike in areas that aren't overly crowded with people, 0:07:31.400 --> 0:07:34.760 and so Jenai very quickly gave me an itinerary of 0:07:34.760 --> 0:07:38.200 a bunch of terrific hikes for them for a destination. 0:07:38.560 --> 0:07:40.120 So things like that great. 0:07:40.560 --> 0:07:44.480 And then what about the effect of AI and of 0:07:44.520 --> 0:07:47.600 automation more generally on employees on the. 0:07:47.520 --> 0:07:51.520 Workforce, Well, there's so many dimensions to take that from 0:07:51.800 --> 0:07:55.680 generitive AI really can up level a workforce in all 0:07:55.720 --> 0:07:59.280 sorts of ways by providing these consistent ways to engage 0:07:59.360 --> 0:08:03.400 with technology, with these natural language experiences. So I think 0:08:03.400 --> 0:08:07.360 it changes everything from it finds us content, it generates 0:08:07.440 --> 0:08:10.000 us content, it makes it easier to work with our 0:08:10.040 --> 0:08:16.000 systems of engagement and operation, and for many organizations it 0:08:16.040 --> 0:08:19.160 can be a lifting factor in terms of bringing a 0:08:19.200 --> 0:08:23.760 more consistent workforce experience because these tools can just be 0:08:23.800 --> 0:08:26.679 ever present in our systems of work. 0:08:27.560 --> 0:08:29.640 I mean, I'll give you a little example here in IBM, 0:08:29.800 --> 0:08:33.120 we have something called our skjar and so that's our 0:08:33.200 --> 0:08:38.200 conversational AI interface that we use to interact with HR 0:08:38.400 --> 0:08:43.080 services and ninety four percent of every employee interaction now 0:08:43.520 --> 0:08:47.760 happens without human intervention through that interface. But you would 0:08:47.760 --> 0:08:50.520 never know that. And so if I think about, you know, 0:08:51.200 --> 0:08:54.520 our HR processes. You know, we have this amazing conversational 0:08:54.559 --> 0:08:57.880 based AI that we use for all of our HR 0:08:58.040 --> 0:09:02.280 interactions and we surface that through SLACK, and so SLACK 0:09:02.320 --> 0:09:05.360 becomes the front door for how we access a lot 0:09:05.400 --> 0:09:09.360 of these different enterprise processes and capabilities and how we 0:09:09.440 --> 0:09:11.839 surface AI. In fact, I'm taking a flight shortly back 0:09:11.840 --> 0:09:14.679 to the UK and our our skhar boss is reminding 0:09:14.720 --> 0:09:16.439 me that it's raining in the UK and I should 0:09:16.440 --> 0:09:17.559 take an umbrella. 0:09:18.360 --> 0:09:20.840 Isn't it always like raining in England? 0:09:23.120 --> 0:09:25.240 Yeah, I don't think there's any AI needed for that. 0:09:25.280 --> 0:09:28.600 I think that's just a hard coded If England, then 0:09:28.640 --> 0:09:29.360 take umbrella. 0:09:29.440 --> 0:09:30.520 That's right, that's just a rule. 0:09:30.679 --> 0:09:33.760 That's just a rule, right, and you're able to converse 0:09:33.800 --> 0:09:35.840 and yeah, I need to book holiday, I need to 0:09:35.880 --> 0:09:38.880 move somebody between managers. I need to figure out the 0:09:38.920 --> 0:09:43.120 policy on this. And the AI basically navigates across the 0:09:43.160 --> 0:09:46.040 different systems to be able to help get that information, 0:09:46.240 --> 0:09:48.800 to summarize it back to be able to carry out 0:09:48.800 --> 0:09:51.480 the transactions that I need carried out, and it just 0:09:51.760 --> 0:09:54.720 removes all of that complexity and makes it easier to 0:09:54.720 --> 0:09:55.319 get things. 0:09:55.160 --> 0:10:04.520 Done when you are working with companies to implement generative AI. Now, 0:10:05.240 --> 0:10:08.600 what do you find tends to be their primary focus? 0:10:09.280 --> 0:10:11.560 I mean I speak with a lot of customers each week, 0:10:11.640 --> 0:10:15.880 and for the last several months, most organizations have just 0:10:15.960 --> 0:10:20.160 been reorienting themselves in terms of where are we in 0:10:20.200 --> 0:10:23.760 this moment, what is this technology capable of? What are 0:10:23.800 --> 0:10:27.400 the risks and governance and frameworks that I need to 0:10:27.760 --> 0:10:31.920 establish in order to engage and talk to everyone. Talk 0:10:31.960 --> 0:10:34.960 to my vendors, talk to my cloud providers, talk to 0:10:35.000 --> 0:10:39.840 my consultants, talk to academics, and generally get your sea 0:10:39.920 --> 0:10:44.320 legs under them. And the sort of the unstructured hand 0:10:44.360 --> 0:10:48.600 on keyboards fiddling with technology seems to be moving towards 0:10:48.760 --> 0:10:51.280 let's get some points on the board, let's turn this 0:10:51.320 --> 0:10:54.040 stuff on and go. So that's what I've been seeing 0:10:54.120 --> 0:10:58.360 in terms of the work within the salesforce ecosystem. Matt, 0:10:58.360 --> 0:11:02.079 you've got a larger apperture as well. What are you seeing? 0:11:02.679 --> 0:11:05.280 Yeah, so I definitely agree. 0:11:05.320 --> 0:11:10.400 I think, you know, there's been lots of getting sea legs, experimentation, 0:11:10.760 --> 0:11:12.840 just trying to build knowledge, being able to try and 0:11:13.840 --> 0:11:18.240 build almost you know, internal organizational point of view and 0:11:18.280 --> 0:11:21.160 reference framework. I've seen lots of what I would have 0:11:21.200 --> 0:11:22.920 referred to as random acts of AI. 0:11:24.920 --> 0:11:27.280 In terms of in terms of experimentation. 0:11:27.320 --> 0:11:29.439 But I think I think people now looking into twenty 0:11:29.480 --> 0:11:32.600 twenty four and this is all about now adoption and scaling, 0:11:33.200 --> 0:11:37.040 what's become really clear is organizations have started to realize 0:11:37.080 --> 0:11:39.200 this is going to be a very multi model world 0:11:39.280 --> 0:11:40.760 that they're going to live in. There is no one 0:11:40.800 --> 0:11:44.560 AI that is the answer for their organization, and so 0:11:44.840 --> 0:11:48.240 they're going to have lots of different generative AI models 0:11:48.960 --> 0:11:51.520 and technologies that they're going to sit in the organization 0:11:51.840 --> 0:11:56.760 servicing different use cases, different domain areas, different products and services, 0:11:58.040 --> 0:12:00.959 and so therefore having to figure out how they're going 0:12:01.000 --> 0:12:04.079 to navigate and manage this kind of open world that 0:12:04.080 --> 0:12:06.360 they're going to be sitting in and the decisions that 0:12:06.360 --> 0:12:08.800 they're going to have to make around that. I think 0:12:08.840 --> 0:12:12.079 the second thing that I've seen that people are now 0:12:12.120 --> 0:12:14.559 becoming very clear that this needs to be what I 0:12:14.600 --> 0:12:17.199 would refer to as use case lead and outcome focus, 0:12:18.600 --> 0:12:21.960 and so really needing to start with thinking about the 0:12:22.040 --> 0:12:25.000 business outcome and the problem that you know, we're trying 0:12:25.080 --> 0:12:29.080 to solve, and therefore, how do I use generative AI 0:12:29.760 --> 0:12:33.160 as part of the mechanism to solve that problem. And 0:12:33.200 --> 0:12:35.840 I think, you know, what Susan and the Salesforce team 0:12:35.880 --> 0:12:37.720 do is an amazing example of that. You know, they've 0:12:37.720 --> 0:12:42.120 got this incredible platform and engine that allows companies to 0:12:42.160 --> 0:12:45.199 transform their sales and service processes and to be able 0:12:45.240 --> 0:12:47.280 to put data in the hands of users, to be 0:12:47.280 --> 0:12:50.320 able to make better decisions, et cetera. And so now 0:12:50.360 --> 0:12:53.200 by weaving generative AI into that platform, we're going to 0:12:53.200 --> 0:12:56.199 be able to make those processes workflows even more efficient. Right, 0:12:56.280 --> 0:12:59.240 So it's generative AI plus all of these other amazing 0:12:59.240 --> 0:13:02.079 things that are there. It will be led through business outcome, 0:13:02.520 --> 0:13:04.960 and it will be led through use case and the 0:13:05.040 --> 0:13:08.480 business problem or workflow that we're trying to improve. And 0:13:08.520 --> 0:13:10.320 then I think the third thing is shifting from this 0:13:10.440 --> 0:13:13.520 experimentation to scale. You know, I think everybody's really early 0:13:13.559 --> 0:13:17.960 in this journey, but what's become clear is that you know, 0:13:18.360 --> 0:13:22.320 everybody now need realizes and is starting to lay down 0:13:22.400 --> 0:13:27.280 these ground rules, the guardrails, the frameworks to allow them 0:13:27.760 --> 0:13:31.719 to scale this across the organization. So, you know, I 0:13:32.080 --> 0:13:34.760 think we're in for an exciting, exciting time in twenty 0:13:34.840 --> 0:13:35.320 twenty four. 0:13:36.040 --> 0:13:38.760 So now that we're getting to this moment, what are 0:13:38.800 --> 0:13:42.079 the key things companies have to figure out about scaling 0:13:42.160 --> 0:13:42.840 generative AI. 0:13:45.240 --> 0:13:48.520 I would put that in kind of two categories, and 0:13:48.559 --> 0:13:52.199 following on what Matt was saying in terms of use, 0:13:52.240 --> 0:13:55.720 case defined and outcome, LAD one hundred percent on that 0:13:55.760 --> 0:13:58.480 in terms of starting with a hypothesis of value while 0:13:58.520 --> 0:14:02.160 at the same time people are getting you know, closer 0:14:02.160 --> 0:14:04.600 to the technology to know what their bounds are. But 0:14:04.640 --> 0:14:07.720 the biggest, you know, set of conversations is in the 0:14:07.880 --> 0:14:13.040 enterprise area in terms of embarking and using with generative AI, 0:14:13.520 --> 0:14:16.280 how to do it in ways that is safe for 0:14:17.280 --> 0:14:21.000 use of data that is safe around not just the 0:14:21.120 --> 0:14:26.120 larger topic of generative AI and hallucinations, which which are 0:14:26.160 --> 0:14:27.520 fun to talk about in the media. 0:14:27.680 --> 0:14:30.440 But it's a fun word, right If it was called 0:14:30.480 --> 0:14:33.640 something other than hallucinations, people wouldn't talk about it as much. 0:14:33.720 --> 0:14:34.200 It was just. 0:14:34.280 --> 0:14:38.440 Mistakes, Yeah, that's right, just things that aren't factually true. 0:14:38.640 --> 0:14:40.840 We've been doing a lot of work at Salesforce around 0:14:40.920 --> 0:14:44.800 using you know, dynamic and structured grounding the data so 0:14:44.840 --> 0:14:49.120 we can give very strong and non naive prompt instructions 0:14:49.120 --> 0:14:51.920 to lllms to get return on that. So so just 0:14:51.920 --> 0:14:55.560 to summarize top of mind for organizations using you know, 0:14:55.840 --> 0:14:59.040 large language models, is using their data in ways that 0:14:59.120 --> 0:15:04.440 are safe, trusted, not exposed, and reducing the opportunity for 0:15:04.480 --> 0:15:07.520 hallucinations and maximizing relevant content. 0:15:08.080 --> 0:15:08.360 Great. 0:15:08.440 --> 0:15:10.920 So, Matt Susan was talking about, you know, both what 0:15:11.080 --> 0:15:15.000 organizations are concerned with as a scale generative AI and 0:15:15.040 --> 0:15:18.560 how Salesforce is working to sort of address those concerns. 0:15:19.120 --> 0:15:21.840 What are you seeing at IBM here? 0:15:21.920 --> 0:15:26.960 So, I think certainly from a scaling of generative AI perspective, 0:15:27.000 --> 0:15:30.760 you know, this topic of governance, you know, and how 0:15:30.840 --> 0:15:32.800 organizations are going to have to govern all of these 0:15:32.800 --> 0:15:38.360 models that sit withinside, how they manage kind of bias fairness, 0:15:38.560 --> 0:15:41.320 model drift, you know, if you think about the data 0:15:41.360 --> 0:15:44.880 that's gone into a model and the output it gives 0:15:44.920 --> 0:15:47.800 to start with, not because the model changes, but because 0:15:47.840 --> 0:15:50.120 the context of the world moves on. And so being 0:15:50.160 --> 0:15:52.200 able to kind of manage this model drift is going 0:15:52.280 --> 0:15:55.200 to be a really important thing. I think data really matters, 0:15:55.640 --> 0:16:00.480 and so quality access security around data within the enterprise 0:16:00.600 --> 0:16:03.240 is going to be critical to scaling generative AI. And 0:16:03.280 --> 0:16:05.320 the other one I think that's going to be really important, 0:16:05.320 --> 0:16:07.880 and I think many organizations haven't even got there yet. 0:16:07.880 --> 0:16:11.240 In their thinking is around the ESG implications. So carbon 0:16:12.080 --> 0:16:14.480 you know, the use of this technology does not come 0:16:14.560 --> 0:16:16.040 without a cost of carbon. 0:16:16.760 --> 0:16:19.040 Carbon meaning it's very energy intensive. 0:16:19.400 --> 0:16:23.000 Correct, Yeah, the training of the models and so thinking 0:16:23.000 --> 0:16:27.080 about carbon disclosures and thinking about where I'm infusing it 0:16:27.120 --> 0:16:29.440 into my business and how much I'm using it and 0:16:29.520 --> 0:16:32.440 what the carbon cost of that is. As I think 0:16:32.480 --> 0:16:37.840 about the you know, you know, my own organizational responsibilities 0:16:37.840 --> 0:16:39.880 to reduce carbon I think, you know, there's all of 0:16:39.920 --> 0:16:42.080 these things that I think are going to become important 0:16:42.160 --> 0:16:45.200 factors as people are thinking about the scaling implications of 0:16:45.240 --> 0:16:46.040 this technology. 0:16:47.000 --> 0:16:50.480 AI is already making new experiences possible, but we must 0:16:50.520 --> 0:16:53.840 be mindful in how we integrate this new technology as 0:16:53.840 --> 0:16:58.480 we continue scaling generative AI. Matt touched on some crucial 0:16:58.520 --> 0:17:02.960 aspects from an IBM perspective. Governance, bias, fairness, and security 0:17:03.240 --> 0:17:07.080 are all key considerations when organizations aim to expand their 0:17:07.160 --> 0:17:12.280 use of generative AI. The environmental aspect is especially important, 0:17:12.840 --> 0:17:15.919 and it's refreshing to hear leading thinkers like Matt and 0:17:16.000 --> 0:17:21.120 Susan highlight these issues As this technology continues to evolve. 0:17:21.640 --> 0:17:26.520 These factors are becoming increasingly important for organizations to address 0:17:27.400 --> 0:17:31.560 the historic collaboration between IBM and Salesforce is helping to 0:17:31.680 --> 0:17:35.040 remedy issues companies face when scaling AI. 0:17:36.200 --> 0:17:41.119 So IBM and Salesforce recently announced a new collaborative project 0:17:41.280 --> 0:17:44.240 around generative AI. Tell me more about that. 0:17:45.400 --> 0:17:50.119 We've been partners for over two decades now IBM and Salesforce, 0:17:50.160 --> 0:17:54.040 and so within our consulting business, we work with Salesforce 0:17:54.200 --> 0:17:57.400 technology to help our clients implement that technology to transform 0:17:57.440 --> 0:18:01.480 their businesses. We've got a huge practice, over twelve thousand 0:18:01.880 --> 0:18:05.840 people with certifications around Salesforce platforms, and so you know, 0:18:05.880 --> 0:18:07.920 as Susan and her team and the broader team in 0:18:07.960 --> 0:18:12.040 Salesforce are infusing more capability into the platform around generative AI, 0:18:12.600 --> 0:18:15.400 then our mission is really simple. It's to help clients 0:18:15.960 --> 0:18:20.040 who are using the Salesforce platform adopt those capabilities to help. 0:18:19.920 --> 0:18:22.119 Them get more benefit within their organization. 0:18:22.680 --> 0:18:25.879 You know, we're also a significant user of Salesforce technology 0:18:25.880 --> 0:18:29.399 within IBM. We're one of Salesforce's largest customers globally, and 0:18:29.480 --> 0:18:32.280 so you know, as we continue to transform our own 0:18:32.359 --> 0:18:36.440 sales and service processes within IBM, then you know our 0:18:36.560 --> 0:18:39.960 use of the generative AI capabilities that they're infusing into sales, 0:18:40.000 --> 0:18:43.359 cloud service, cloud slack, et cetera will be something that 0:18:43.359 --> 0:18:47.520 will become really important to us driving productivity within the company. 0:18:47.920 --> 0:18:49.439 And then the other thing that I would say is, 0:18:49.680 --> 0:18:51.240 you know, as I think about the work that we 0:18:51.320 --> 0:18:54.240 do with clients, you know, as they're implementing and on 0:18:54.280 --> 0:18:57.240 their generative AI journeys, you know they're going to utilize 0:18:57.240 --> 0:19:00.760 and leverage the salesforce capabilities within the platform and their 0:19:00.800 --> 0:19:05.040 generative AI technologies. But then you start thinking about processes 0:19:05.080 --> 0:19:07.959 and workflows that run beyond the walls of CRM right 0:19:08.000 --> 0:19:11.359 that run into supply chain and into the finance area 0:19:11.359 --> 0:19:14.360 of the organization. And so there is work that we're 0:19:14.359 --> 0:19:17.200 doing with clients where we're using ibms. What's the next 0:19:17.280 --> 0:19:21.080 platform to be able to help get access to to 0:19:21.200 --> 0:19:24.399 generate insights from data sources that sit in all of 0:19:24.440 --> 0:19:27.239 these kind of back office areas of the enterprise and 0:19:27.280 --> 0:19:29.959 to be able to get that data across the salesforce, 0:19:30.040 --> 0:19:34.160 into these customer interaction points and into the employees who 0:19:34.160 --> 0:19:39.080 are servicing those customers using salesforces AI and generative AI technologies. 0:19:39.080 --> 0:19:42.120 So there's a kind of one plus one equals three 0:19:42.320 --> 0:19:45.560 kind of you know, better together, you know, and being 0:19:45.560 --> 0:19:48.320 able to bring our technologies together in service of these 0:19:48.320 --> 0:19:52.280 clients problems as you think about these processes that run 0:19:52.320 --> 0:19:56.520 across their enterprise. So yeah, so huge hut unity and 0:19:56.520 --> 0:19:58.880 what we're doing together in the market to help clients. 0:19:59.480 --> 0:20:02.720 Yeah, building on that, it is a huge moment for 0:20:03.880 --> 0:20:07.240 organizations and for technology companies like Salesforce, and we couldn't 0:20:07.280 --> 0:20:10.360 be happier to have partnerships like we have with IBM. 0:20:10.880 --> 0:20:16.040 Like the range of thought leadership that is appropriate at 0:20:16.040 --> 0:20:19.520 the moment is everything from what is that hypothesis of 0:20:19.600 --> 0:20:22.359 value and what are those use cases? And what is 0:20:22.400 --> 0:20:25.000 the order of operation in terms of approaching it just 0:20:25.040 --> 0:20:28.760 in terms of focus, but then things that would help 0:20:28.880 --> 0:20:33.280 organizations assess their AI readiness and then their approach Like 0:20:33.320 --> 0:20:37.040 you know, we talked earlier about frameworks and guardrails. You know, 0:20:37.080 --> 0:20:40.000 what are use cases that we're comfortable with given the 0:20:40.040 --> 0:20:43.440 state of the technology that face employees or face customers. 0:20:43.480 --> 0:20:46.760 So creating these much larger roadmaps in terms of how 0:20:46.800 --> 0:20:51.080 to approach this over a series of initiatives, the way 0:20:51.280 --> 0:20:55.200 it can fundamentally change the way we engage with technology 0:20:55.800 --> 0:20:59.479 and what that means for the you know, training and 0:20:59.560 --> 0:21:04.399 change management and use cases that fundamentally shift how you 0:21:05.320 --> 0:21:09.080 engage with systems like Salesforce. There's just a massive opportunity 0:21:09.200 --> 0:21:09.919 for us together. 0:21:10.720 --> 0:21:14.560 So you're talking in sort of general terms, I'm interested in, 0:21:14.760 --> 0:21:19.120 you know, thinking in particular about the way generitive AI 0:21:19.240 --> 0:21:23.159 can essentially lead to better business outcomes, right Like, what 0:21:23.200 --> 0:21:25.760 does that look like? How do you measure it? You know, 0:21:25.880 --> 0:21:28.360 there's a certain bottom line question there, right like, how 0:21:28.359 --> 0:21:30.879 does AI make businesses work better? And in what ways? 0:21:31.520 --> 0:21:35.800 You know, as consumers of products and services, we all 0:21:35.840 --> 0:21:38.440 love and respect great service, you know, in terms of 0:21:38.480 --> 0:21:41.800 getting timely, quick answers, resolving issues quickly, all those those 0:21:41.840 --> 0:21:46.800 types of things. And from the perspective of using generitive 0:21:46.880 --> 0:21:51.040 and predictive capabilities for agents who are interacting with customers, 0:21:51.400 --> 0:21:54.640 there is just a whole ton of opportunity to take 0:21:54.680 --> 0:21:57.120 friction out of the process in terms of finding answers, 0:21:57.200 --> 0:22:00.919 resolving issues, in terms of using these generatives capabilities that 0:22:00.960 --> 0:22:04.080 will bring you know, answers and content to the fingertips 0:22:04.119 --> 0:22:09.399 more easily to the human agents that are working with customers. Now, 0:22:09.480 --> 0:22:13.080 taking that to the next step, for organizations when they're 0:22:13.119 --> 0:22:16.960 ready to move into more customer facing automation. That's yet 0:22:17.000 --> 0:22:19.800 another channel. As a consumer, we'll all enjoy with the 0:22:19.840 --> 0:22:22.000 brands and the products and the services that we want 0:22:22.040 --> 0:22:25.600 in terms of fast answers and resolutions to customers, and 0:22:25.640 --> 0:22:30.400 as we all know, great customer experience yields return business. 0:22:30.880 --> 0:22:34.119 Now on the sales side, you know, maybe a different example, 0:22:34.720 --> 0:22:37.639 and these are areas where I think the capability of 0:22:37.840 --> 0:22:41.320 predictive and generative go very well together in terms of 0:22:41.359 --> 0:22:45.160 focusing on business outcomes. And a classic example would be, 0:22:45.680 --> 0:22:50.040 you know, predictions that help us understand customer health. You know, 0:22:50.160 --> 0:22:54.120 is this customer engaged, is this customer at risk? Predictions 0:22:54.119 --> 0:22:58.240 that help us understand next best product or next best conversation. 0:22:58.760 --> 0:23:04.400 These all help focus sales team's time on a customer 0:23:04.480 --> 0:23:08.080 or a territory, and so that deep focus puts all 0:23:08.080 --> 0:23:10.359 the wood behind an arrow, so to speak, in terms 0:23:10.440 --> 0:23:14.919 of where we should be engaging. And those types of 0:23:15.400 --> 0:23:20.119 driven sales organizations that have these capabilities just lead to 0:23:20.160 --> 0:23:25.000 better performance and outcomes and customer experience too. Now, let's 0:23:25.040 --> 0:23:29.480 also layer in generitive capabilities, where we're using the generative 0:23:29.520 --> 0:23:33.560 capabilities to assist and augment a sales team where we're 0:23:33.640 --> 0:23:37.680