WEBVTT - Smart Talks with IBM - Salesforce & IBM: Revolutionizing Experiences with Generative AI 0:00:04.680 --> 0:00:12.840 Welcome to Tech Stuff, a production from iHeartRadio. Today, we 0:00:12.880 --> 0:00:15.880 are witnessed to one of those rare moments in history, 0:00:16.239 --> 0:00:19.439 the rise of an innovative technology with the potential to 0:00:19.600 --> 0:00:24.320 radically transform business and society forever. That technology, of course, 0:00:24.800 --> 0:00:28.360 is artificial intelligence, and it's the central focus for this 0:00:28.520 --> 0:00:32.519 new season of Smart Talks with IBM. Join hosts from 0:00:32.560 --> 0:00:36.280 your favorite Pushkin podcasts as they talk with industry experts 0:00:36.280 --> 0:00:39.920 and leaders to explore how businesses can integrate AI into 0:00:39.960 --> 0:00:43.280 their workflows and help drive real change in this new 0:00:43.360 --> 0:00:47.040 era of AI, and of course, host Malcolm Gladwell will 0:00:47.040 --> 0:00:49.360 be there to guide you through the season and throw 0:00:49.440 --> 0:00:52.360 in his two cents as well. Look out for new 0:00:52.360 --> 0:00:55.280 episodes of Smart Talks with IBM every other week on 0:00:55.320 --> 0:00:59.560 the iHeartRadio app, Apple Podcasts, wherever you get your podcasts. 0:01:00.080 --> 0:01:04.000 Learn more at IBM dot com slash smart Talks. 0:01:06.319 --> 0:01:09.920 Hello, Hello, Welcome to Smart Talks with IBM, a podcast 0:01:09.959 --> 0:01:16.319 from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Glappo. This season, 0:01:16.360 --> 0:01:20.479 we're continuing our conversations with new creators visionaries who are 0:01:20.560 --> 0:01:25.280 creatively applying technology and business to drive change, but with 0:01:25.319 --> 0:01:30.080 a focus on the transformative power of artificial intelligence and 0:01:30.120 --> 0:01:33.320 what it means to leverage AI as a game changing 0:01:33.440 --> 0:01:39.080 multiplier for your business. Today's episode highlights the power of collaboration. 0:01:39.880 --> 0:01:42.600 IBM has long been a supporter of the better Together 0:01:42.720 --> 0:01:47.000 mindset and embrace his partnerships. They have been working together 0:01:47.040 --> 0:01:50.920 with Salesforce for more than two decades, but have recently 0:01:51.000 --> 0:01:56.600 launched a new collaborative effort surrounding generative AI. Pushkin's very 0:01:56.600 --> 0:02:00.800 own Jacob Goldstein sat down with Matt Candy and sus Emerson. 0:02:01.360 --> 0:02:04.920 Matt is the global managing Partner of Generative AI at 0:02:04.960 --> 0:02:09.360 IBM Consulting, helping clients and partners around the world embrace 0:02:09.440 --> 0:02:14.000 this new era of technology, and Susan is a senior 0:02:14.080 --> 0:02:19.400 vice president for Salesforce dedicated to AI, analytics and data. 0:02:20.280 --> 0:02:23.880 They discussed the historic collaboration between the two tech giants, 0:02:24.480 --> 0:02:28.560 explored the opportunity AI presents for customer service, and walk 0:02:28.639 --> 0:02:35.480 through how businesses can use generative AI to interface with clients. Okay, 0:02:35.800 --> 0:02:37.080 let's get to the conversation. 0:02:39.680 --> 0:02:42.919 Thank you guys for coming this morning. So I'm interested 0:02:42.960 --> 0:02:46.760 in how you both came to generative AI, or maybe 0:02:46.760 --> 0:02:48.120 it sort of came to you in the way it 0:02:48.160 --> 0:02:50.160 sort of came to all of us, But how did 0:02:50.200 --> 0:02:52.360 you arrive at working on generative AI. 0:02:52.760 --> 0:02:55.560 As part of my remitted Salesforce. Over the years, I've 0:02:55.600 --> 0:02:59.280 brought a lot of analytics and data and machine learning 0:02:59.280 --> 0:03:04.040 products to life under the Einstein brand at Salesforce. So 0:03:04.520 --> 0:03:09.480 as we pivoted Salesforce into taking advantage of the generative 0:03:09.480 --> 0:03:12.600 AI moment, it was natural that I became part of 0:03:12.639 --> 0:03:19.519 the advanced team leveraging generative AI, and it's become interesting. 0:03:19.639 --> 0:03:23.360 But what I see as I speak with customers the 0:03:23.440 --> 0:03:25.840 moment that everyone is facing in terms of how they 0:03:26.040 --> 0:03:31.959 incorporate genitive AI into their businesses, their workforces, and their 0:03:32.000 --> 0:03:35.280 technical stacks. It's actually opening up a lot of doors 0:03:35.360 --> 0:03:40.320 to other utility of analytics, data and AI. So it's 0:03:40.360 --> 0:03:44.400 been this big pull through in terms of incorporating not 0:03:44.520 --> 0:03:48.640 just generative AI, but a larger conversation around how we 0:03:49.080 --> 0:03:52.720 become all better using data in our day jobs. 0:03:54.280 --> 0:03:56.960 So that's a great frame for sort of what's going 0:03:57.000 --> 0:04:00.840 on at Salesforce with generative AI. Tell us a little 0:04:00.880 --> 0:04:03.080 bit about you know, how that fits with the way 0:04:03.520 --> 0:04:04.920 IBM is approaching with space. 0:04:05.680 --> 0:04:08.880 Yeah, so I guess through three sides to that question. 0:04:09.520 --> 0:04:12.360 And so there's the technology side of it. So IBM 0:04:12.440 --> 0:04:15.720 has a technology organization, and so you know, we are 0:04:15.760 --> 0:04:19.400 building and have been over many years decades. In fact, 0:04:19.480 --> 0:04:22.919 IBM has been working in this space a generative AI 0:04:23.080 --> 0:04:30.600 stack that allows organizations to adopt generative AI technology aimed 0:04:30.640 --> 0:04:35.200 at enterprise and business use within their organizations. So then 0:04:35.240 --> 0:04:37.839 within the consulting business, you know, we have one hundred 0:04:37.839 --> 0:04:41.400 and sixty thousand people who work every day with clients 0:04:41.440 --> 0:04:47.080 across every industry, regulated industries, government organizations, and so this, 0:04:47.320 --> 0:04:50.800 you know, is a really important technology that those companies 0:04:50.839 --> 0:04:53.080 are going to be using to drive the next level 0:04:53.080 --> 0:04:57.359 of transformation in their enterprises processes and the types of 0:04:57.400 --> 0:05:00.479 experiences they build for their customers. And so you know, 0:05:00.520 --> 0:05:05.880 we work extensively with partners technology such as Salesforce, AWS, Microsoft, 0:05:06.400 --> 0:05:09.760 as well as our own technology. And then finally, I 0:05:09.760 --> 0:05:12.600 guess the third angle is the work that we've got 0:05:12.640 --> 0:05:15.560 to do to reinvent the business of consulting. And so 0:05:15.720 --> 0:05:18.880 if I think about you know, consulting in systems integration, 0:05:19.320 --> 0:05:22.839 you know, ultimately we are knowledge workers, right, and so 0:05:23.120 --> 0:05:25.880 from an industry perspective, I think, you know, our industry 0:05:25.960 --> 0:05:29.080 is same as many others is it's going to go 0:05:29.279 --> 0:05:33.359 undergo a level of disruption caused by this technology, but 0:05:33.440 --> 0:05:36.160 therefore that will also create a huge opportunity for us 0:05:36.640 --> 0:05:37.039 as well. 0:05:37.120 --> 0:05:40.080 So those three aspects, Jacob, great. 0:05:39.920 --> 0:05:42.040 So, so that's the point of view sort of from 0:05:42.080 --> 0:05:46.279 your companies in your work. I'm curious to talk for 0:05:46.360 --> 0:05:48.960 a moment about AI from the point of view of 0:05:49.720 --> 0:05:53.599 consumers and employees kind of out in the world today. 0:05:53.640 --> 0:05:56.560 So just to start with consumers, when I'm just out 0:05:57.160 --> 0:05:59.479 as a person as a consumer in the world, how 0:05:59.480 --> 0:06:01.320 am I AI today? 0:06:02.480 --> 0:06:03.840 I'll give you a great little use case. 0:06:03.880 --> 0:06:07.919 Actually, I was on holiday three weeks ago in Tenerif 0:06:07.960 --> 0:06:10.919 in Spain, and I was trying to find somewhere to 0:06:10.960 --> 0:06:11.960 park the car with the. 0:06:11.920 --> 0:06:13.360 Family for dinner that evening. 0:06:14.000 --> 0:06:19.240 And I found this area next to this kind of 0:06:19.279 --> 0:06:22.359 shopping center and there was this sign there and I 0:06:22.360 --> 0:06:24.719 couldn't quite work out if it was saying I could 0:06:24.760 --> 0:06:27.760 park there or not, And so I took a photo 0:06:27.839 --> 0:06:30.719 of the sign and I uploaded it to an AI 0:06:31.080 --> 0:06:33.279 tool and I said, what does this mean? And it 0:06:33.320 --> 0:06:35.479 basically explained to me what the sign was saying and 0:06:35.520 --> 0:06:37.640 basically told me that I shouldn't be parking there, and 0:06:37.680 --> 0:06:40.920 so I drove on and I found some somewhere else 0:06:40.960 --> 0:06:45.520 to park. But you know, that allowed me in under 0:06:45.560 --> 0:06:49.440 sixty seconds to probably avoid one hundred euro fine by 0:06:49.480 --> 0:06:52.960 parking the car there. So just a simple example, but 0:06:53.080 --> 0:06:56.400 I think the ability that these tools have to take 0:06:56.480 --> 0:06:59.320 friction out of our daily lives, you know, and to 0:06:59.400 --> 0:07:02.400 be able to make just things that we do in 0:07:02.440 --> 0:07:06.040 our everyday life simple and more frictionless. You know. That's 0:07:06.080 --> 0:07:09.080 how I look at how mat the consumer is going 0:07:09.120 --> 0:07:11.120 to benefit from some of this type of technology. 0:07:11.840 --> 0:07:15.400 And from my perspective, it's also a travel story. I 0:07:15.440 --> 0:07:18.480 spend a lot of time on the road for work, 0:07:18.840 --> 0:07:22.840 but recently had to send my sister and her family 0:07:23.000 --> 0:07:25.960 to a destination they had never been to for a wedding. 0:07:26.640 --> 0:07:30.240 And it was really quick and easy to use some 0:07:30.600 --> 0:07:33.520 generitive tools to come up with a whole plan for 0:07:33.560 --> 0:07:35.800 them because they love to hike and to be outdoors 0:07:36.160 --> 0:07:39.920 and to hike in areas that aren't overly crowded with people, 0:07:40.520 --> 0:07:43.840 and so Jenai very quickly gave me an itinerary of 0:07:43.880 --> 0:07:47.280 a bunch of terrific hikes for them for a destination. 0:07:47.680 --> 0:07:48.720 So things like that. 0:07:48.920 --> 0:07:53.440 Great, And then what about the effect of AI and 0:07:53.480 --> 0:07:57.600 of automation more generally on employees on the workforce. 0:07:58.280 --> 0:08:01.360 Well, there's so many dimensions to take that from. Generative 0:08:01.400 --> 0:08:05.119 AI really can up level a workforce in all sorts 0:08:05.120 --> 0:08:09.400 of ways by providing these consistent ways to engage with technology, 0:08:09.480 --> 0:08:13.080 with these natural language experiences. So I think it changes 0:08:13.120 --> 0:08:17.200 everything from it finds us content, it generates us content, 0:08:17.480 --> 0:08:20.200 It makes it easier to work with our systems of 0:08:20.240 --> 0:08:25.520 engagement and operation, and for many organizations it can be 0:08:25.960 --> 0:08:29.120 a lifting factor in terms of bringing a more consistent 0:08:29.400 --> 0:08:33.600 workforce experience because these tools can just be ever present 0:08:33.840 --> 0:08:35.800 in our systems of work. 0:08:36.679 --> 0:08:38.760 I mean, I'll give you a little example here in IBM, 0:08:38.880 --> 0:08:42.240 we have something called our Skajar and so that's our 0:08:42.320 --> 0:08:47.320 conversational AI interface that we use to interact with HR 0:08:47.520 --> 0:08:52.200 services and ninety four percent of every employee interaction now 0:08:52.640 --> 0:08:56.040 happens without human intervention through that interface. 0:08:56.360 --> 0:08:57.320 But you would never know that. 0:08:58.280 --> 0:09:01.560 And so if I think about, you know, our HR processes, 0:09:01.760 --> 0:09:05.480 You know, we have this amazing conversational based AI that 0:09:05.520 --> 0:09:08.520 we use for all of our HR interactions, and we 0:09:08.640 --> 0:09:12.199 surface that through SLACK and so Slack becomes the front 0:09:12.240 --> 0:09:15.200 door for how we access a lot of these different 0:09:15.320 --> 0:09:19.600 enterprise processes and capabilities and how we surface AI. In fact, 0:09:19.600 --> 0:09:21.640 I'm taking a flight shortly back to the UK and 0:09:22.120 --> 0:09:24.600 our our skar Bos is reminding me that it's raining 0:09:24.600 --> 0:09:26.640 in the UK and I should take an umbrella. 0:09:27.440 --> 0:09:29.880 Isn't it always like raining in England? 0:09:32.240 --> 0:09:34.320 Yeah, I don't think there's any AI needed for that. 0:09:34.360 --> 0:09:37.720 I think that's just a hard coded If England, then 0:09:37.760 --> 0:09:38.480 take umbrella. 0:09:38.520 --> 0:09:39.640 That's right, that's just a rule. 0:09:39.800 --> 0:09:40.280 That's just a. 0:09:40.320 --> 0:09:43.600 Rule, right, and you're able to converse and yeah, I 0:09:43.640 --> 0:09:46.640 need to book holiday, I need to move somebody between managers. 0:09:46.840 --> 0:09:49.640 I need to figure out the policy on this. And 0:09:49.720 --> 0:09:53.400 the AI basically navigates across the different systems to be 0:09:53.440 --> 0:09:56.440 able to help get that information, to summarize it back, 0:09:56.920 --> 0:09:59.040 to be able to carry out the transactions that I 0:09:59.080 --> 0:10:01.760 need carried out, and it just just removes all of 0:10:01.760 --> 0:10:05.800 that complexity and makes it easier to get things done. 0:10:06.520 --> 0:10:13.640 When you are working with companies to implement generative AI. Now, 0:10:14.360 --> 0:10:17.720 what do you find tends to be their primary focus? 0:10:18.400 --> 0:10:20.680 I mean I speak with a lot of customers each week, 0:10:20.760 --> 0:10:25.000 and for the last several months, most organizations have just 0:10:25.080 --> 0:10:29.280 been reorienting themselves in terms of where are we in 0:10:29.280 --> 0:10:32.880 this moment, what is this technology capable of? What are 0:10:32.920 --> 0:10:36.520 the risks and governance and frameworks that I need to 0:10:36.880 --> 0:10:41.040 establish in order to engage and talk to everyone. Talk 0:10:41.040 --> 0:10:44.040 to my vendors, talk to my cloud providers, talk to 0:10:44.120 --> 0:10:48.960 my consultants, talk to academics, and generally get your sea 0:10:49.000 --> 0:10:53.440 legs under them. And the sort of the unstructured hand 0:10:53.480 --> 0:10:57.680 on keyboards fiddling with technology seems to be moving towards 0:10:57.880 --> 0:11:00.760 let's get some points on the board, turn this stuff 0:11:00.800 --> 0:11:03.320 on and go. So that's what I've been seeing in 0:11:03.400 --> 0:11:07.440 terms of, you know, the work within the salesforce ecosystem. Matt, 0:11:07.480 --> 0:11:11.199 you've got a larger aperture as well. What are you seeing? 0:11:11.800 --> 0:11:14.400 Yeah, so I definitely agree. 0:11:14.440 --> 0:11:17.959 I think, you know, there's been lots of getting sea 0:11:18.040 --> 0:11:21.520 legs experimentation, just trying to build knowledge, being able to 0:11:21.559 --> 0:11:27.040 try and build almost you know, internal organizational point of 0:11:27.080 --> 0:11:29.959 view and reference framework. I've seen lots of what I 0:11:30.000 --> 0:11:32.040 would have referred to as random acts of AI. 0:11:34.000 --> 0:11:36.359 In terms of in terms of experimentation. 0:11:36.440 --> 0:11:38.559 But I think I think people now looking into twenty 0:11:38.600 --> 0:11:41.680 twenty four and this is all about now adoption and scaling. 0:11:42.280 --> 0:11:46.160 What's become really clear is organizations have started to realize 0:11:46.200 --> 0:11:48.280 this is going to be a very multi model world 0:11:48.400 --> 0:11:49.880 that they're going to live in. There is no one 0:11:49.920 --> 0:11:53.719 AI that is the answer for their organization, and so 0:11:53.960 --> 0:11:57.360 they're going to have lots of different generative AI models 0:11:58.040 --> 0:12:00.640 and technologies that they're going to sit in the organization 0:12:00.920 --> 0:12:05.880 servicing different use cases, different domain areas, different products and services, 0:12:07.160 --> 0:12:10.040 and so therefore having to figure out how they're going 0:12:10.080 --> 0:12:13.160 to navigate and manage this kind of open world that 0:12:13.160 --> 0:12:15.439 they're going to be sitting in and the decisions that 0:12:15.480 --> 0:12:17.920 they're going to have to make around that. I think 0:12:17.960 --> 0:12:21.160 the second thing that I've seen that people are now 0:12:21.200 --> 0:12:23.640 becoming very clear that this needs to be what I 0:12:23.679 --> 0:12:26.400 would refer to as use case lead and outcome focused, 0:12:27.679 --> 0:12:31.080 and so really needing to start with thinking about the 0:12:31.120 --> 0:12:34.120 business outcome and the problem that you know we're trying 0:12:34.200 --> 0:12:38.199 to solve, and therefore, how do I use generative AI 0:12:38.880 --> 0:12:42.280 as part of the mechanism to solve that problem? And 0:12:42.320 --> 0:12:44.960 I think you know what Susan and the Salesforce team 0:12:45.000 --> 0:12:46.840 do is an amazing example of that. You know, they've 0:12:46.840 --> 0:12:51.240 got this incredible platform and engine that allows companies to 0:12:51.280 --> 0:12:54.319 transform their sales and service processes and to be able 0:12:54.320 --> 0:12:56.360 to put data in the hands of users, to be 0:12:56.400 --> 0:12:59.440 able to make better decisions, et cetera. And so now 0:12:59.480 --> 0:13:02.320 by weaving generative AI into that platform, we're going to 0:13:02.320 --> 0:13:05.319 be able to make those processes workflows even more efficient. Right, 0:13:05.360 --> 0:13:08.319 So it's generative AI plus all of these other amazing 0:13:08.360 --> 0:13:10.160 things that are there, but it will be led through 0:13:10.360 --> 0:13:13.679 business outcome, and it will be led through use case 0:13:13.800 --> 0:13:16.760 and the business problem or workflow that we're trying to improve. 0:13:17.520 --> 0:13:19.360 And then I think the third thing is shifting from 0:13:19.360 --> 0:13:22.280 this experimentation to scale. You know, I think everybody's really 0:13:22.320 --> 0:13:26.679 early in this journey, but what's become clear is that 0:13:26.800 --> 0:13:30.840 you know, everybody now need realizes and is starting to 0:13:30.920 --> 0:13:35.360 lay down these these ground rules, the guardrails, the frameworks 0:13:35.400 --> 0:13:39.760 to allow them to scale this across the organization. So, 0:13:40.600 --> 0:13:43.080 you know, I think we're in for an exciting, exciting 0:13:43.120 --> 0:13:44.400 time in twenty twenty four. 0:13:45.160 --> 0:13:47.880 So now that we're getting to this moment, what are 0:13:47.880 --> 0:13:51.199 the key things companies have to figure out about scaling 0:13:51.280 --> 0:13:51.920 generative AI? 0:13:54.360 --> 0:13:57.600 I would put that in kind of two categories and 0:13:57.679 --> 0:14:01.320 following on what Matt was saying in terms of use, 0:14:01.360 --> 0:14:04.800 case defined and outcome lead one hundred percent on that 0:14:04.880 --> 0:14:07.600 in terms of starting with a hypothesis of value, while 0:14:07.600 --> 0:14:11.480 at the same time people are getting closer to the 0:14:11.520 --> 0:14:14.280 technology to know what their bounds are. But the biggest 0:14:14.360 --> 0:14:18.480 you know, set of conversations is in the enterprise area 0:14:19.520 --> 0:14:22.800 in terms of embarking and using with generative AI, how 0:14:22.800 --> 0:14:26.560 to do it in ways that is safe for use 0:14:26.600 --> 0:14:30.560 of data that is safe around not just the larger 0:14:30.640 --> 0:14:35.440 topic of generative AI and hallucinations, which which are fun 0:14:35.480 --> 0:14:36.640 to talk about in the media. 0:14:36.800 --> 0:14:39.560 But it's a fun word, right. If it was called 0:14:39.600 --> 0:14:42.720 something other than hallucinations, people wouldn't talk about it as much. 0:14:42.840 --> 0:14:46.360 It was just mistakes, Yeah, that's right, just things that 0:14:46.400 --> 0:14:48.880 aren't factually true. We've been doing a lot of work 0:14:48.880 --> 0:14:53.280 at Salesforce around using you know, dynamic and structured grounding 0:14:53.320 --> 0:14:56.360 the data so we can give very strong and non 0:14:56.480 --> 0:15:00.440 naive prompt instructions to lllms to get return on that. So, 0:15:00.920 --> 0:15:04.680 just to summarize, top of mind for organizations using you know, 0:15:04.960 --> 0:15:08.160 large language models is using their data in ways that 0:15:08.240 --> 0:15:13.520 are safe, trusted, not exposed, and reducing the opportunity for 0:15:13.560 --> 0:15:16.640 hallucinations and maximizing relevant content. 0:15:17.200 --> 0:15:17.480 Great. 0:15:17.520 --> 0:15:19.800 So, so Matt Susan was talking about, you know, both 0:15:19.840 --> 0:15:23.760 what organizations are concerned with as they scale generative AI 0:15:23.960 --> 0:15:27.680 and how Salesforce is working to sort of address those concerns. 0:15:28.240 --> 0:15:31.200 What are you seeing at IBM. 0:15:30.720 --> 0:15:34.360 Here, So I think certainly from a scaling of generative 0:15:34.360 --> 0:15:39.560 AI perspective, you know this topic of governance, you know, 0:15:39.600 --> 0:15:41.600 and how organizations are going to have to govern all 0:15:41.600 --> 0:15:45.400 of these models that sit withinside, how they manage kind 0:15:45.400 --> 0:15:49.760 of bias fairness, model drift, you know, if you think 0:15:49.760 --> 0:15:53.080 about the data that's gone into a model and the 0:15:53.120 --> 0:15:56.480 output it gives to start with, not because the model changes, 0:15:56.480 --> 0:15:58.760 but because the context of the world moves on. And 0:15:58.800 --> 0:16:01.080 so being able to kind of manage this model drift 0:16:01.160 --> 0:16:03.160 is going to be a really important thing. I think 0:16:03.240 --> 0:16:08.760 data really matters, and so quality access security around data 0:16:08.800 --> 0:16:11.040 within the enterprise is going to be critical to scaling 0:16:11.080 --> 0:16:13.560 generative AI. And the other one I think that's going 0:16:13.560 --> 0:16:16.120 to be really important, and I think many organizations haven't 0:16:16.120 --> 0:16:18.080 even got there yet in their thinking is around the 0:16:18.320 --> 0:16:22.120 ESG implications. So carbon you know, the use of this 0:16:22.200 --> 0:16:25.560 technology does not come without a cost of carbon. 0:16:25.880 --> 0:16:28.640 Carbon meaning it's very energy intensive. 0:16:28.520 --> 0:16:32.120 Correct, Yeah, the training of the models and so thinking 0:16:32.120 --> 0:16:36.200 about carbon disclosures and thinking about where I'm infusing it 0:16:36.240 --> 0:16:38.560 into my business and how much I'm using it and 0:16:38.600 --> 0:16:41.560 what the carbon cost of that is. As I think 0:16:41.600 --> 0:16:46.960 about the you know, you know, my own organizational responsibilities 0:16:46.960 --> 0:16:49.000 to reduce carbon I think, you know, there's all of 0:16:49.040 --> 0:16:51.240 these things that I think are going to become important 0:16:51.280 --> 0:16:54.320 factors as people are thinking about the scaling implications of 0:16:54.360 --> 0:16:55.160 this technology. 0:16:56.080 --> 0:16:59.560 AI is already making new experiences possible, but we must 0:16:59.600 --> 0:17:02.600 be in mind in how we integrate this new technology 0:17:02.760 --> 0:17:07.119 as we continue scaling generative AI. Matt touched on some 0:17:07.200 --> 0:17:11.520 crucial aspects from an IBM perspective. Governance, bias, fairness, and 0:17:11.520 --> 0:17:16.040 security are all key considerations when organizations aim to expand 0:17:16.080 --> 0:17:21.400 their use of generative AI. The environmental aspect is especially important, 0:17:21.960 --> 0:17:25.040 and it's refreshing to hear leading thinkers like Matt and 0:17:25.119 --> 0:17:30.240 Susan highlight these issues. As this technology continues to evolve, 0:17:30.720 --> 0:17:35.640 these factors are becoming increasingly important for organizations to address. 0:17:36.520 --> 0:17:40.679 The Historic collaboration between IBM and Salesforce is helping to 0:17:40.800 --> 0:17:44.159 remedy issues companies face when scaling AI. 0:17:45.320 --> 0:17:50.240 So IBM and Salesforce recently announced a new collaborative project 0:17:50.400 --> 0:17:53.280 around generative AI. Tell me more about that. 0:17:54.520 --> 0:17:59.240 We've been partners for over two decades now IBM and Salesforce, 0:17:59.280 --> 0:18:02.359 and so within our consulting business, you know, we work 0:18:02.400 --> 0:18:05.919 with Salesforce technology to help our clients implement that technology 0:18:05.960 --> 0:18:09.640 to transform their businesses. We've got a huge practice, over 0:18:09.800 --> 0:18:14.000 twelve thousand people with certifications around Salesforce platforms, and so 0:18:14.760 --> 0:18:16.760 you know, as Susan and her team and the broader 0:18:16.760 --> 0:18:20.120 team in Salesforce are infusing more capability into the platform 0:18:20.160 --> 0:18:23.480 around generative AI, then our mission is really simple. It's 0:18:23.560 --> 0:18:27.680 to help clients who are using the Salesforce platform adopt 0:18:27.720 --> 0:18:29.200 those capabilities to help. 0:18:29.040 --> 0:18:31.240 Them get more benefit within their organization. 0:18:31.800 --> 0:18:34.960 You know, we're also a significant user of Salesforce technology 0:18:35.000 --> 0:18:38.520 within IBM. We're one of Salesforce's largest customers globally, and 0:18:38.600 --> 0:18:41.400 so you know, as we continue to transform our own 0:18:41.480 --> 0:18:45.560 sales and service processes within IBM, then you know, our 0:18:45.680 --> 0:18:48.760 use of the generative AI capabilities that they're infusing into 0:18:48.800 --> 0:18:52.399 sales cloud, service, cloud slack, et cetera will be something 0:18:52.400 --> 0:18:56.000 that will become really important to us driving productivity within 0:18:56.080 --> 0:18:58.160 the company. And then the other thing that I would 0:18:58.160 --> 0:19:00.160 say is, you know, as I think about the work 0:19:00.160 --> 0:19:03.119 that we do with clients, you know, as they're implementing 0:19:03.119 --> 0:19:05.639 and on their generative AI journeys, you know, they're going 0:19:05.680 --> 0:19:09.600 to utilize and leverage the salesforce capabilities within the platform 0:19:09.640 --> 0:19:13.240 and their generative AI technologies. But then you start thinking 0:19:13.280 --> 0:19:16.840 about processes and workflows that run beyond the walls of CRM, 0:19:16.920 --> 0:19:20.120 right that run into supply chain and into the finance 0:19:20.200 --> 0:19:23.280 area of the organization. And so there is work that 0:19:23.320 --> 0:19:26.080 we're doing with clients where we're using ibms. What's the 0:19:26.160 --> 0:19:30.120 next platform to be able to help get access to 0:19:30.119 --> 0:19:33.440 to generate insights from data sources that sit in all 0:19:33.480 --> 0:19:35.680 of these kind of back office areas of the enterprise, 0:19:36.240 --> 0:19:38.399 and to be able to get that data across the 0:19:38.480 --> 0:19:43.040 salesforce into these customer interaction points and into the employees 0:19:43.080 --> 0:19:47.280 who are servicing those customers using salesforces AI and generative 0:19:47.280 --> 0:19:48.199 AI technologies. 0:19:48.200 --> 0:19:49.160 So there's a. 0:19:49.160 --> 0:19:52.080 Kind of one plus one equals three kind of you know, 0:19:52.240 --> 0:19:55.399 better together, you know, and being able to bring our 0:19:55.440 --> 0:19:59.520 technologies together in service of these clients. Problems as you 0:19:59.560 --> 0:20:03.680 think about these processes that run across their enterprise. So, yeah, 0:20:03.840 --> 0:20:06.560 it's so huge hutunity and what we're doing together in 0:20:06.560 --> 0:20:08.000 the market to help clients. 0:20:08.600 --> 0:20:10.879 Yeah, and just building it on that. It is a 0:20:10.960 --> 0:20:15.840 huge moment for organizations and for technology companies like Salesforce, 0:20:15.840 --> 0:20:18.639 and we couldn't be happier to have partnerships like we 0:20:18.720 --> 0:20:23.439 have with IBM. Like the range of thought leadership that 0:20:24.280 --> 0:20:27.679 is appropriate at the moment is everything from what is 0:20:27.680 --> 0:20:30.720 that hypothesis of value and what are those use cases? 0:20:30.760 --> 0:20:33.240 And what is the order of operation in terms of 0:20:33.280 --> 0:20:37.120 approaching it just in terms of focus, but then things 0:20:37.200 --> 0:20:41.480 that would help organizations assess their AI readiness and then 0:20:41.520 --> 0:20:44.719 their approach like you know, we talked earlier about frameworks 0:20:44.760 --> 0:20:48.040 and guardrails. You know, what are use cases that we're 0:20:48.080 --> 0:20:50.920 comfortable with given the state of the technology that face 0:20:50.960 --> 0:20:55.160 employees or face customers. So creating these much larger roadmaps 0:20:55.200 --> 0:20:58.000 in terms of how to approach this over a series 0:20:58.080 --> 0:21:03.400 of initiatives, it can fundamentally change the way we engage 0:21:03.400 --> 0:21:07.800 with technology and what that means for the you know, 0:21:07.880 --> 0:21:13.000 training and change management and use cases that fundamentally shift 0:21:13.160 --> 0:21:17.080 how you engage with systems like salesforces. There's just a 0:21:17.080 --> 0:21:19.040 massive opportunity for us together. 0:21:19.840 --> 0:21:23.680 So you're talking in sort of general terms, I'm interested in, 0:21:23.840 --> 0:21:28.240 you know, thinking in particular about the way generitive AI 0:21:28.359 --> 0:21:32.280 can essentially lead to better business outcomes, right Like, what 0:21:32.320 --> 0:21:34.840 does that look like? How do you measure it? You know, 0:21:34.960 --> 0:21:37.480 there's a certain bottom line question there, right like, how 0:21:37.480 --> 0:21:40.000 does AI make businesses work better? And in what ways? 0:21:40.640 --> 0:21:44.920 You know, as consumers of products and services, we all 0:21:44.960 --> 0:21:47.560 love and respect great service, you know, in terms of 0:21:47.560 --> 0:21:50.920 getting timely, quick answers, resolving issues quickly, all those those 0:21:50.960 --> 0:21:55.920 types of things. And from the perspective of using generative 0:21:55.960 --> 0:22:00.160 and predictive capabilities for agents who are interacting with customers, 0:22:00.520 --> 0:22:03.760 there is just a whole ton of opportunity to take 0:22:03.760 --> 0:22:06.280 friction out of the process in terms of finding answers, 0:22:06.320 --> 0:22:10.000 resolving issues, in terms of using these generative capabilities that 0:22:10.080 --> 0:22:13.200 will bring you know, answers and content to the fingertips 0:22:13.240 --> 0:22:18.480 more easily to the human agents that are working with customers. Now, 0:22:18.600 --> 0:22:22.200 taking that to the next step for organizations when they're 0:22:22.240 --> 0:22:26.040 ready to move into more customer facing automation, that's yet 0:22:26.080 --> 0:22:28.919 another channel. As a consumer, we'll all enjoy with the 0:22:28.920 --> 0:22:31.120 brands and the products and the services that we want 0:22:31.160 --> 0:22:34.679 in terms of fast answers and resolutions to customers. And 0:22:34.720 --> 0:22:39.480 as we all know, great customer experience yields return business. 0:22:40.000 --> 0:22:43.240 Now on the sales side, you know, maybe a different example, 0:22:43.800 --> 0:22:46.760 and these are areas where I think the capability of 0:22:46.920 --> 0:22:50.439 predictive and generative go very well together in terms of 0:22:50.480 --> 0:22:54.280 focusing on business outcomes. And a classic example would be, 0:22:54.800 --> 0:22:59.159 you know, predictions that help us understand customer health. You know, 0:22:59.280 --> 0:23:03.200 is this customer engaged, is this customer at risk? Predictions 0:23:03.240 --> 0:23:07.359 that help us understand next best product or next best conversation. 0:23:07.880 --> 0:23:13.520 These all help focus sales team's time on a customer 0:23:13.600 --> 0:23:17.200 or a territory, and so that deep focus puts all 0:23:17.200 --> 0:23:19.520 the wood behind an arrow, so to speak, in terms 0:23:19.560 --> 0:23:24.000 of where we should be engaging. And those types of 0:23:24.520 --> 0:23:29.199 driven sales organizations that have these capabilities just lead to 0:23:29.240 --> 0:23:34.080 better performance and outcomes and customer experience too. Now, let's 0:23:34.119 --> 0:23:38.600 also layer in generitive capabilities where we're using the generative 0:23:38.640 --> 0:23:42.680 capabilities to assist and augment a sales team, where we're 0:23:42.720 --> 0:23:46.800 using the power de generitive for everything like generating personalized 0:23:46.880 --> 0:23:51.720 and relevant customer interaction content, for example, leveraging our customer 0:23:51.800 --> 0:23:56.720