1 00:00:04,440 --> 00:00:12,639 Speaker 1: Welcome to Tech Stuff, a production from iHeartRadio. Today, we 2 00:00:12,680 --> 00:00:15,640 Speaker 1: are witnessed to one of those rare moments in history, 3 00:00:16,000 --> 00:00:19,239 Speaker 1: the rise of an innovative technology with the potential to 4 00:00:19,360 --> 00:00:24,080 Speaker 1: radically transform business in society forever. That technology, of course, 5 00:00:24,560 --> 00:00:28,120 Speaker 1: is artificial intelligence, and it's the central focus for this 6 00:00:28,320 --> 00:00:32,279 Speaker 1: new season of Smart Talks with IBM. Join hosts from 7 00:00:32,360 --> 00:00:36,080 Speaker 1: your favorite Pushkin podcasts as they talk with industry experts 8 00:00:36,080 --> 00:00:39,680 Speaker 1: and leaders to explore how businesses can integrate AI into 9 00:00:39,720 --> 00:00:43,040 Speaker 1: their workflows and help drive real change in this new 10 00:00:43,159 --> 00:00:46,800 Speaker 1: era of AI, and of course, host Malcolm Gladwell will 11 00:00:46,840 --> 00:00:49,160 Speaker 1: be there to guide you through the season and throw 12 00:00:49,240 --> 00:00:52,120 Speaker 1: in his two cents as well. Look out for new 13 00:00:52,159 --> 00:00:55,040 Speaker 1: episodes of Smart Talks with IBM every other week on 14 00:00:55,080 --> 00:00:59,320 Speaker 1: the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts, 15 00:00:59,520 --> 00:01:03,760 Speaker 1: and learn more at IBM dot com slash smart Talks. 16 00:01:05,720 --> 00:01:09,160 Speaker 2: Hello, Hello, Welcome to Smart Talks with IBM, a podcast 17 00:01:09,160 --> 00:01:14,959 Speaker 2: from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Glabo. This season, 18 00:01:15,120 --> 00:01:19,360 Speaker 2: we're continuing our conversation with new creators visionaries who are 19 00:01:19,400 --> 00:01:23,360 Speaker 2: creatively applying technology in business to drive change, but with 20 00:01:23,400 --> 00:01:27,920 Speaker 2: a focus on the transformative power of artificial intelligence and 21 00:01:27,959 --> 00:01:31,040 Speaker 2: what it means to leverage AI as a game changing 22 00:01:31,160 --> 00:01:35,360 Speaker 2: multiplier for your business. Our guest today is Kareem Yousef, 23 00:01:35,640 --> 00:01:40,200 Speaker 2: Senior Vice President of Product Management and Growth for IBM Software. 24 00:01:40,720 --> 00:01:44,679 Speaker 2: Kareem's focus at IBM is on product strategy, thinking about 25 00:01:44,680 --> 00:01:47,680 Speaker 2: the roadmap for IBM Software products and how they can 26 00:01:47,720 --> 00:01:53,120 Speaker 2: deliver effective and compelling customer experiences with the current boom 27 00:01:53,160 --> 00:01:56,279 Speaker 2: and generative AI. Kareem's job is to help businesses figure 28 00:01:56,280 --> 00:02:00,520 Speaker 2: out how they can apply artificial intelligence at scale to 29 00:02:00,560 --> 00:02:04,520 Speaker 2: help solve big problems and boost productivity at new orders 30 00:02:04,520 --> 00:02:08,640 Speaker 2: of magnitude. In today's episode, you'll hear Kareem explain how 31 00:02:08,720 --> 00:02:12,919 Speaker 2: AI powered by foundation models can make AI adoption by 32 00:02:13,080 --> 00:02:18,040 Speaker 2: enterprise businesses even easier, how generative AI will change the 33 00:02:18,040 --> 00:02:21,680 Speaker 2: way businesses process data and make decisions, and how these 34 00:02:21,720 --> 00:02:27,639 Speaker 2: considerations influence the design of Watson x, IBM's next generation 35 00:02:28,200 --> 00:02:32,960 Speaker 2: AI and data platform. Kareem spoke with Jacob Goldstein, host 36 00:02:32,960 --> 00:02:37,040 Speaker 2: of the Pushkin podcast What's Your Problem. A veteran business journalist, 37 00:02:37,240 --> 00:02:40,760 Speaker 2: Jacob has reported for The Wall Street Journal, the Miami Herald, 38 00:02:41,120 --> 00:02:46,160 Speaker 2: and was a longtime host of the NPR program Planet Money. Okay, 39 00:02:46,639 --> 00:02:47,880 Speaker 2: let's get to the interview. 40 00:02:49,440 --> 00:02:52,560 Speaker 3: I'm Jacob Goldstein. I'm one of the hosts at Pushkin 41 00:02:52,720 --> 00:02:55,600 Speaker 3: and a correspondent on this show, and I'm delighted to 42 00:02:55,600 --> 00:02:57,200 Speaker 3: have you here. Can you introduce yourself? 43 00:02:57,639 --> 00:03:00,520 Speaker 4: Ah? Hi, I'm Kareem Yusuf. I'm this and your vice 44 00:03:00,520 --> 00:03:03,920 Speaker 4: president of Product management and Growth for IBM Software. You 45 00:03:03,919 --> 00:03:05,960 Speaker 4: can think of me as the chief product officer for 46 00:03:06,000 --> 00:03:06,880 Speaker 4: IBM Software. 47 00:03:07,320 --> 00:03:12,040 Speaker 3: Okay, sounds like a big job. We're here today to 48 00:03:12,080 --> 00:03:18,000 Speaker 3: talk about AI. We've heard really an extraordinary amount in 49 00:03:18,040 --> 00:03:22,000 Speaker 3: the last few months about chat GPT and you know, 50 00:03:22,080 --> 00:03:24,560 Speaker 3: particularly in how it's used in the very kind of 51 00:03:24,639 --> 00:03:27,919 Speaker 3: consumer facing way. But I'm curious what is the rise 52 00:03:27,960 --> 00:03:30,760 Speaker 3: of chat GPT and you know, AI more generally, what 53 00:03:30,800 --> 00:03:32,160 Speaker 3: does it mean for business? 54 00:03:32,720 --> 00:03:35,080 Speaker 4: Well, you know, it's if you kind of step back 55 00:03:35,120 --> 00:03:39,760 Speaker 4: and think about what really happens. You know, in a business, 56 00:03:40,240 --> 00:03:43,160 Speaker 4: you're really talking about a set of processes, right, you know, 57 00:03:43,240 --> 00:03:47,120 Speaker 4: activities that represent what a business needs to get done, 58 00:03:47,280 --> 00:03:50,680 Speaker 4: whether it's product they produce and then sell or service 59 00:03:50,760 --> 00:03:54,640 Speaker 4: that they provide. And inherent to operating the business, I 60 00:03:54,640 --> 00:03:58,440 Speaker 4: would say are two very key factors. Data and then 61 00:03:58,480 --> 00:04:02,320 Speaker 4: the decisions you make around data and then actually lastly 62 00:04:02,840 --> 00:04:06,760 Speaker 4: the processes or activities you do in accordance with that decision. 63 00:04:07,520 --> 00:04:10,600 Speaker 4: So if you then think about AI as applied to 64 00:04:10,680 --> 00:04:13,400 Speaker 4: business right in that context, well, the first place it 65 00:04:13,440 --> 00:04:16,480 Speaker 4: often starts is how do you make sense of a 66 00:04:16,520 --> 00:04:20,560 Speaker 4: lot of the data associated with driving the business? And 67 00:04:20,640 --> 00:04:23,320 Speaker 4: so AI has always been, in my mind at its 68 00:04:23,360 --> 00:04:29,560 Speaker 4: foremost about gaining insights then lead in to supporting decisions, 69 00:04:30,040 --> 00:04:34,360 Speaker 4: and ultimately ending at helping to automate the activities that 70 00:04:34,560 --> 00:04:38,200 Speaker 4: then are executed as a result of those decisions. So 71 00:04:38,240 --> 00:04:40,480 Speaker 4: that's kind of my simple way of thinking of AI, 72 00:04:40,800 --> 00:04:43,560 Speaker 4: and we can obviously coloring with examples, but that's my 73 00:04:43,640 --> 00:04:46,000 Speaker 4: simplest way of thinking about AI. When you think about 74 00:04:46,080 --> 00:04:49,400 Speaker 4: in the business context, gain insights from masses of data 75 00:04:49,720 --> 00:04:52,240 Speaker 4: to support decisions and then drive. 76 00:04:52,040 --> 00:04:55,320 Speaker 3: Actions, that's a really helpful framework. And then if we 77 00:04:55,360 --> 00:04:57,880 Speaker 3: think about sort of what's happening in the world now 78 00:04:57,920 --> 00:05:02,080 Speaker 3: with you know, enterprise businesses NAI, what are you seeing 79 00:05:02,120 --> 00:05:05,360 Speaker 3: with enterprise adoption of AI at this moment? 80 00:05:06,200 --> 00:05:10,000 Speaker 4: So we're really talking about almost a tale of two periods. 81 00:05:10,000 --> 00:05:12,400 Speaker 4: So let me first of all kind of take you 82 00:05:12,480 --> 00:05:16,479 Speaker 4: back before the advent of what I would call generative AI, 83 00:05:16,560 --> 00:05:19,919 Speaker 4: and the whole chat gpt to what has been going 84 00:05:20,000 --> 00:05:22,480 Speaker 4: on in what I would term the realm of more 85 00:05:22,600 --> 00:05:26,640 Speaker 4: standardized machine learning models. A lot of what has been 86 00:05:26,640 --> 00:05:29,040 Speaker 4: going on has been very much in the realms of 87 00:05:29,160 --> 00:05:34,440 Speaker 4: certain things like anomaly detection or optimization, right, using machine 88 00:05:34,480 --> 00:05:36,920 Speaker 4: learning models to do that kind of work, and where 89 00:05:36,960 --> 00:05:41,800 Speaker 4: might it apply well, think of anomaly detection in security 90 00:05:41,920 --> 00:05:46,440 Speaker 4: software right detecting threats based upon different events flowing through 91 00:05:47,120 --> 00:05:52,760 Speaker 4: or in enterprise asset management software monitoring equipment and detecting 92 00:05:52,839 --> 00:05:58,080 Speaker 4: anomalies within their behavior, or even in IT automation software 93 00:05:58,400 --> 00:06:01,680 Speaker 4: once again detecting anomalies based upon what's going on with 94 00:06:01,760 --> 00:06:06,360 Speaker 4: various IT events and then tasks that should occur. Optimizations 95 00:06:06,480 --> 00:06:09,640 Speaker 4: often play around in the realm as you might imagine 96 00:06:09,800 --> 00:06:13,279 Speaker 4: to solve problems of resource optimization, whether you think of 97 00:06:13,320 --> 00:06:16,919 Speaker 4: that in the context of application resource management for IT 98 00:06:17,680 --> 00:06:20,160 Speaker 4: or in the context of supply chain. These have been 99 00:06:20,279 --> 00:06:25,479 Speaker 4: very classical applications of machine learning AI to really make 100 00:06:25,560 --> 00:06:30,360 Speaker 4: sense of the data and provide a basis to drive decisions. Now, 101 00:06:30,760 --> 00:06:34,920 Speaker 4: what is characterized by all those examples have given and 102 00:06:35,000 --> 00:06:37,680 Speaker 4: the state of the art of that kind of technology 103 00:06:38,240 --> 00:06:43,039 Speaker 4: has always been it's very task specific. So there was 104 00:06:43,120 --> 00:06:48,040 Speaker 4: a air quotes, if I may, kind of limitation in 105 00:06:48,120 --> 00:06:50,720 Speaker 4: the sense that the tak it had to be very 106 00:06:50,880 --> 00:06:53,800 Speaker 4: task specific. And so we've seen a lot of broad 107 00:06:53,839 --> 00:06:57,560 Speaker 4: based adoption within the enterprise, right, but it's very very 108 00:06:57,680 --> 00:07:01,760 Speaker 4: task specific. As you might imagine. Now, what has happened 109 00:07:02,040 --> 00:07:05,960 Speaker 4: recently and has been brought to the four has been 110 00:07:06,040 --> 00:07:09,640 Speaker 4: this discussion of generative AI, which is powered by a 111 00:07:09,760 --> 00:07:14,880 Speaker 4: very specific innovation, this notion of foundation models. And in 112 00:07:14,920 --> 00:07:18,440 Speaker 4: the simplest way to think about it, it's about training 113 00:07:18,480 --> 00:07:25,480 Speaker 4: this large model that can then be refined to various tasks. 114 00:07:26,480 --> 00:07:30,600 Speaker 4: And the easiest one that everybody recognized at the moment 115 00:07:30,680 --> 00:07:33,840 Speaker 4: is the notion of a large language model, a model 116 00:07:33,880 --> 00:07:37,080 Speaker 4: that has an understanding of a lot of the elements 117 00:07:37,080 --> 00:07:40,480 Speaker 4: of a language such that it can be refined to 118 00:07:40,720 --> 00:07:44,320 Speaker 4: a variety of tasks. Write an essay, answer a question, 119 00:07:44,560 --> 00:07:48,720 Speaker 4: singer songs, so on, answers so forth. I like to 120 00:07:49,080 --> 00:07:52,720 Speaker 4: liken the power if you like, and this will speak 121 00:07:52,760 --> 00:07:55,360 Speaker 4: to the why everybody is so excited about it. Why 122 00:07:55,400 --> 00:07:58,440 Speaker 4: would argue at an inflection point? I like to liken 123 00:07:58,480 --> 00:08:02,960 Speaker 4: it to teaching a child the alphabet. When you teach 124 00:08:02,960 --> 00:08:06,480 Speaker 4: a child and alphabet, it's a set of letters. Right, 125 00:08:06,800 --> 00:08:11,280 Speaker 4: Let's call that our foundation model. But over time that 126 00:08:11,480 --> 00:08:15,080 Speaker 4: knowledge of the alphabet is tuned to read a book, 127 00:08:15,120 --> 00:08:17,720 Speaker 4: write an essay, do a composition, create a song, write 128 00:08:17,760 --> 00:08:20,880 Speaker 4: a poem, write an invoice. You understand what I mean, right, 129 00:08:21,120 --> 00:08:26,920 Speaker 4: And so from one foundation model you can support multiple 130 00:08:27,040 --> 00:08:31,360 Speaker 4: targeted tasks as opposed sticking with the analogy to having 131 00:08:31,360 --> 00:08:35,640 Speaker 4: a model for reading, writing, thinking of doing a poem, 132 00:08:35,920 --> 00:08:38,560 Speaker 4: doing an essay, so on and so forth. And so 133 00:08:38,679 --> 00:08:42,200 Speaker 4: in the enterprise context, that means that we're now talking 134 00:08:42,240 --> 00:08:46,680 Speaker 4: about being able to unlock even additional value at scale 135 00:08:47,600 --> 00:08:52,120 Speaker 4: because of the nation of nature foundation models and their 136 00:08:52,160 --> 00:08:56,120 Speaker 4: appeal to generative use cases. Generative in this case meaning 137 00:08:56,559 --> 00:08:57,840 Speaker 4: creation of new content. 138 00:08:58,600 --> 00:09:02,920 Speaker 3: So let's talk about what's in X. IBM recently announced 139 00:09:02,920 --> 00:09:05,120 Speaker 3: what's an X. Just first of all, what is that? 140 00:09:05,160 --> 00:09:06,000 Speaker 3: What is what's an X? 141 00:09:06,880 --> 00:09:10,480 Speaker 4: Well, what's an X refers to our is our brand 142 00:09:10,520 --> 00:09:14,560 Speaker 4: for our platform, the WhatsApp platform for really taking advantage 143 00:09:15,080 --> 00:09:19,560 Speaker 4: of generative AI within the enterprise, within business. And so 144 00:09:19,760 --> 00:09:22,720 Speaker 4: when you begin to think about what does that mean, 145 00:09:22,800 --> 00:09:24,840 Speaker 4: while it leads you to the components of what's an 146 00:09:25,040 --> 00:09:27,199 Speaker 4: X and to a set of use cases. So let 147 00:09:27,200 --> 00:09:31,400 Speaker 4: me paint a few quick pictures for you here. What's 148 00:09:31,440 --> 00:09:35,000 Speaker 4: an X first of all, is about enabling our customers 149 00:09:35,080 --> 00:09:40,080 Speaker 4: to manipulate models against their task, manipulate these foundation models 150 00:09:40,320 --> 00:09:43,800 Speaker 4: against their task. Our belief is that the world is 151 00:09:43,880 --> 00:09:48,160 Speaker 4: a multi model world, right and especially when you think 152 00:09:48,200 --> 00:09:51,320 Speaker 4: about it in the context of business. Models are going 153 00:09:51,400 --> 00:09:54,560 Speaker 4: to come from various sources, the ones we supply, the 154 00:09:54,559 --> 00:09:56,680 Speaker 4: ones out there in open source, and so of you. 155 00:09:57,000 --> 00:09:59,600 Speaker 4: But there are activities you need to do around these 156 00:09:59,640 --> 00:10:03,600 Speaker 4: models to as I said, apply them to your use case. 157 00:10:03,679 --> 00:10:05,560 Speaker 4: And we'll talk about use cases in a bit. So 158 00:10:06,080 --> 00:10:09,480 Speaker 4: what's next. Dot AI is that environment that build a 159 00:10:09,559 --> 00:10:11,960 Speaker 4: tool if you like, for being able to do those 160 00:10:12,000 --> 00:10:16,480 Speaker 4: manipulation of models to meet your specific use case. Thinks 161 00:10:16,520 --> 00:10:20,400 Speaker 4: that people will recognize in the field prompt engineering, prompt tuning, 162 00:10:20,559 --> 00:10:23,840 Speaker 4: fine tuning, those kinds of activities which are all the 163 00:10:23,920 --> 00:10:27,160 Speaker 4: manipulation of models to meet your use case. Yeah. The 164 00:10:27,200 --> 00:10:30,679 Speaker 4: second component is dot data, So what's the next? Dot 165 00:10:30,800 --> 00:10:34,800 Speaker 4: data is essentially a next generation data store is based 166 00:10:34,880 --> 00:10:38,840 Speaker 4: upon something referred to as an open data lakehouse architecture 167 00:10:39,160 --> 00:10:42,960 Speaker 4: that helps to bring together the data that's needed to 168 00:10:43,120 --> 00:10:45,520 Speaker 4: actually do the AI. In this case, when you think 169 00:10:45,520 --> 00:10:49,160 Speaker 4: about manipulating a model, a foundation model, you're generally using 170 00:10:49,200 --> 00:10:51,920 Speaker 4: some data to prompt it, tune it, to train it 171 00:10:51,960 --> 00:10:55,800 Speaker 4: to your use cases. And so we provide a very 172 00:10:55,880 --> 00:10:58,600 Speaker 4: open data store that allows all manner of data and 173 00:10:58,640 --> 00:11:01,319 Speaker 4: formats to be brought through. Today you that and the 174 00:11:01,360 --> 00:11:05,720 Speaker 4: third component is what's next up governance And this is 175 00:11:05,760 --> 00:11:09,680 Speaker 4: all about the framework and the toolkit required to apply 176 00:11:09,920 --> 00:11:14,080 Speaker 4: the right governance principles across doing this kind of work, 177 00:11:14,320 --> 00:11:19,000 Speaker 4: because when you're deploying AI within the enterprise, governance is 178 00:11:19,040 --> 00:11:23,000 Speaker 4: actually important, right, It's critical to understand why is your 179 00:11:23,080 --> 00:11:25,800 Speaker 4: data coming from? What data did you add in? How 180 00:11:25,920 --> 00:11:28,520 Speaker 4: is your model performing? Are you able to keep an 181 00:11:28,559 --> 00:11:32,280 Speaker 4: appropriate audit trail of your activities for your own internal 182 00:11:32,640 --> 00:11:35,920 Speaker 4: policy and compliance needs or for regulatory needs as well. 183 00:11:36,520 --> 00:11:40,720 Speaker 3: So this platform, the system that you're describing, I'm curious, 184 00:11:41,280 --> 00:11:44,520 Speaker 3: how is it different from the you know, the generative 185 00:11:44,640 --> 00:11:47,040 Speaker 3: AI options that you know we've all been hearing about 186 00:11:47,080 --> 00:11:48,000 Speaker 3: sort of in the press. 187 00:11:48,679 --> 00:11:51,960 Speaker 4: Well, I think it really comes down to the ethos 188 00:11:52,120 --> 00:11:55,160 Speaker 4: or the principles that first of all drive the work 189 00:11:55,200 --> 00:11:58,280 Speaker 4: that we're doing. The first I would fixate on is 190 00:11:58,559 --> 00:12:02,880 Speaker 4: being open. Right. We fundamentally believe that to do this 191 00:12:02,960 --> 00:12:05,680 Speaker 4: kind of work within the enterprise, you need an open 192 00:12:05,760 --> 00:12:09,360 Speaker 4: platform that, as I said, is open to all manner 193 00:12:09,360 --> 00:12:11,880 Speaker 4: of models from all sources. It's one of the reasons 194 00:12:11,880 --> 00:12:15,079 Speaker 4: why we announced our partnership with hugging Face to make 195 00:12:15,120 --> 00:12:18,960 Speaker 4: sure that our clients can gain access to open source 196 00:12:19,040 --> 00:12:22,320 Speaker 4: innovation within the platform to do their work. 197 00:12:22,800 --> 00:12:24,440 Speaker 3: And hugging Face, to be clear, is sort of the 198 00:12:25,320 --> 00:12:27,280 Speaker 3: open source AI kind of hub. 199 00:12:27,760 --> 00:12:31,320 Speaker 4: That's right, that's correct. Yes, it's a marketplace hub for 200 00:12:31,520 --> 00:12:35,880 Speaker 4: all kind of ecosystem coordinator for open source models. And 201 00:12:35,960 --> 00:12:38,720 Speaker 4: I believe there's a lot of innovation going on out there. So, 202 00:12:38,800 --> 00:12:44,840 Speaker 4: first of all, open becomes important. The second targeted So 203 00:12:45,040 --> 00:12:50,960 Speaker 4: our focus is very much on enabling these business use cases, right, 204 00:12:51,200 --> 00:12:53,080 Speaker 4: And you might say what kind of use cases are 205 00:12:53,120 --> 00:12:55,319 Speaker 4: we talking about? I give you three very quick ones 206 00:12:55,360 --> 00:12:58,600 Speaker 4: that with our customers are focused on a lot of 207 00:12:58,640 --> 00:13:03,320 Speaker 4: focus around and enhancing customer service use cases. Think of 208 00:13:03,360 --> 00:13:07,640 Speaker 4: this as chatbots or digital assistance that are further trained 209 00:13:08,200 --> 00:13:11,319 Speaker 4: in more and more information about what the company has 210 00:13:11,360 --> 00:13:14,760 Speaker 4: to offer, or could be internal policies, external policy, so 211 00:13:14,800 --> 00:13:17,560 Speaker 4: on and so forth. This means a platform that makes 212 00:13:17,600 --> 00:13:22,160 Speaker 4: it really easy to bring your own data to train 213 00:13:22,280 --> 00:13:26,640 Speaker 4: and tune the model, while protecting your own data as 214 00:13:26,720 --> 00:13:31,839 Speaker 4: extremely important for the enterprise right. Another important use case 215 00:13:32,160 --> 00:13:34,160 Speaker 4: seeing a lot of focused on what i'd call AI 216 00:13:34,200 --> 00:13:38,600 Speaker 4: based orchestration or automation of task whereby think about like 217 00:13:38,960 --> 00:13:42,959 Speaker 4: an HR professional as an example, going through a job 218 00:13:43,000 --> 00:13:47,040 Speaker 4: requisition is able to interact with multiple systems via a 219 00:13:47,120 --> 00:13:52,359 Speaker 4: very simple chat interface and have work dynamically sequenced to 220 00:13:52,400 --> 00:13:56,719 Speaker 4: support them in doing their task. That once again requires 221 00:13:56,800 --> 00:14:00,360 Speaker 4: a notion of working with models and technology in a 222 00:14:00,440 --> 00:14:03,439 Speaker 4: way that in many ways can be unique to how 223 00:14:03,440 --> 00:14:06,280 Speaker 4: a business wishes to work and indeed, in various cases 224 00:14:06,320 --> 00:14:09,640 Speaker 4: can embody what they consider their their secret source or 225 00:14:09,640 --> 00:14:13,719 Speaker 4: their differentiated advantage. So once again, a platform that recognizes 226 00:14:13,760 --> 00:14:16,640 Speaker 4: that and designed for business that's not the same scope 227 00:14:16,720 --> 00:14:21,280 Speaker 4: or frame of reference for a consumer platform. And then 228 00:14:21,400 --> 00:14:23,360 Speaker 4: you know, we're also seeing a lot of work around 229 00:14:23,720 --> 00:14:28,720 Speaker 4: cod generation, application modernization, you know, and people enhancing their skills. 230 00:14:28,920 --> 00:14:32,960 Speaker 4: So targeted becomes really important. Mentioned open and I mentioned 231 00:14:33,040 --> 00:14:36,400 Speaker 4: targeted targeted to the business to the use cases that 232 00:14:36,440 --> 00:14:40,960 Speaker 4: they need to do. Underpinning that then it's trusted. So 233 00:14:41,040 --> 00:14:44,120 Speaker 4: everything I gave you in those targeted use cases talk 234 00:14:44,200 --> 00:14:51,320 Speaker 4: about handling enterprise proprietary and specific data. We are trusted 235 00:14:51,360 --> 00:14:53,840 Speaker 4: in this regard right. We have been serving the business 236 00:14:54,280 --> 00:14:57,720 Speaker 4: for many, many a year, and we are designing our 237 00:14:57,800 --> 00:15:00,920 Speaker 4: platform and even our principles and way of creating to 238 00:15:01,120 --> 00:15:04,280 Speaker 4: recognize and enable that. Both in terms of the work 239 00:15:04,320 --> 00:15:07,880 Speaker 4: we do around the governance framework and transparency that you're 240 00:15:07,920 --> 00:15:10,560 Speaker 4: able to gain and apply, but even in the way 241 00:15:10,600 --> 00:15:15,040 Speaker 4: we allow our platform to be deployed in multiple kind 242 00:15:15,080 --> 00:15:18,800 Speaker 4: of locations, of footprints consumed as a service on a hyperscaler, 243 00:15:19,160 --> 00:15:22,720 Speaker 4: running your own private footprint on prem or your cloud footprint. 244 00:15:23,040 --> 00:15:25,280 Speaker 4: All of these need to be brought together to meet 245 00:15:25,320 --> 00:15:30,320 Speaker 4: the needs of an actual enterprise business. My last comment 246 00:15:30,400 --> 00:15:34,360 Speaker 4: is where I think we're fundamentally differentiated is we're really 247 00:15:34,480 --> 00:15:41,680 Speaker 4: about empowering our customers to take advantage of AI to 248 00:15:41,800 --> 00:15:47,320 Speaker 4: unleash the intelligence, capabilities productivity of their own business. This 249 00:15:47,360 --> 00:15:51,200 Speaker 4: isn't about, oh, we've established a bunch of APIs that 250 00:15:51,240 --> 00:15:54,960 Speaker 4: you can ask questions. This is about how do you 251 00:15:55,480 --> 00:15:59,920 Speaker 4: craft what you need for your business to deliver different 252 00:16:00,080 --> 00:16:06,280 Speaker 4: shaped value to your customers, shareholders, employees with all the 253 00:16:06,400 --> 00:16:09,400 Speaker 4: appropriate protections as well. And so there's a lot of 254 00:16:09,440 --> 00:16:11,440 Speaker 4: focus on what we've done with the platform and the 255 00:16:11,480 --> 00:16:14,080 Speaker 4: tool set to enable that, to enable what we like 256 00:16:14,120 --> 00:16:19,720 Speaker 4: to call AI value creators, not just consumers of AI value. 257 00:16:20,480 --> 00:16:26,480 Speaker 3: When you were talking about basically enterprise adoption of AI, 258 00:16:27,320 --> 00:16:30,880 Speaker 3: you use the word trust, and I'm curious, you know, 259 00:16:31,360 --> 00:16:35,720 Speaker 3: what does trust mean in the context of AI and 260 00:16:35,800 --> 00:16:36,400 Speaker 3: the enterprise. 261 00:16:37,800 --> 00:16:43,120 Speaker 4: I would kind of deconstruct trust along these k avenues. 262 00:16:45,240 --> 00:16:49,520 Speaker 4: If AI is about giving you insights to help you 263 00:16:49,560 --> 00:16:55,800 Speaker 4: support decisions, how do you trust what insights it's provided? 264 00:16:56,680 --> 00:17:01,760 Speaker 4: What data did it use? What did it consider based 265 00:17:01,840 --> 00:17:08,120 Speaker 4: upon that data that therefore led to the insight provided? 266 00:17:10,440 --> 00:17:14,280 Speaker 4: Why is this important? Why this notion of trust? Well, One, 267 00:17:14,680 --> 00:17:17,520 Speaker 4: you're about to make a decision, so you want to 268 00:17:17,600 --> 00:17:22,360 Speaker 4: understand the basis for a decision. It's no different than 269 00:17:22,400 --> 00:17:25,520 Speaker 4: me asking you something and then saying, okay, can you 270 00:17:25,560 --> 00:17:27,879 Speaker 4: explain you're working? Right, that would be a notion of 271 00:17:27,920 --> 00:17:32,080 Speaker 4: trust that we establish and a very natural interaction as humans, right, 272 00:17:32,080 --> 00:17:35,399 Speaker 4: we do it all the time, right, So there is 273 00:17:35,440 --> 00:17:39,000 Speaker 4: that element. The other reason why it becomes important if 274 00:17:39,040 --> 00:17:43,680 Speaker 4: you're applying AI into business processes and therefore how your 275 00:17:43,720 --> 00:17:48,239 Speaker 4: business works. You want to make sure that you know 276 00:17:48,320 --> 00:17:52,600 Speaker 4: what biases are built in to any decision or not 277 00:17:53,040 --> 00:17:56,880 Speaker 4: or if the AI the model in effect is drifting 278 00:17:57,920 --> 00:18:01,840 Speaker 4: away from kind of the parameters that you would want 279 00:18:01,880 --> 00:18:06,320 Speaker 4: it to remain within, right or go trust and so 280 00:18:07,440 --> 00:18:11,320 Speaker 4: in many ways, that's one big aspect of trusting the 281 00:18:11,400 --> 00:18:14,879 Speaker 4: technology because you're applying it into decisions you need to 282 00:18:14,880 --> 00:18:17,240 Speaker 4: make every day, and you need to know in very 283 00:18:17,240 --> 00:18:20,840 Speaker 4: simple terms how it works and how it is working. 284 00:18:22,280 --> 00:18:25,760 Speaker 4: The element of trust that I think is important in 285 00:18:25,760 --> 00:18:32,080 Speaker 4: this discussion. Who are you getting your AI from? That's 286 00:18:32,320 --> 00:18:35,840 Speaker 4: very important to us as a company here at IBM. Right, 287 00:18:36,440 --> 00:18:42,320 Speaker 4: given we serve business, that trust becomes extremely important and 288 00:18:42,359 --> 00:18:44,080 Speaker 4: what are the elements of that trust? What are the 289 00:18:44,119 --> 00:18:49,000 Speaker 4: customers trying to understand? Well, first and foremost, what's your 290 00:18:49,000 --> 00:18:52,920 Speaker 4: ethos around AI? We're very clear on the customer's data 291 00:18:53,080 --> 00:18:56,880 Speaker 4: is their data when they tune or refine those models 292 00:18:56,920 --> 00:19:00,359 Speaker 4: to meet their use cases. That is all THEIRS actually 293 00:19:00,359 --> 00:19:02,880 Speaker 4: provide the ability for them to do that in very 294 00:19:02,960 --> 00:19:07,800 Speaker 4: isolated and protected ways as they choose, and we never 295 00:19:07,960 --> 00:19:13,200 Speaker 4: use that data without explicit opting and permissions. Right, customers 296 00:19:13,240 --> 00:19:15,440 Speaker 4: might say Oh yeah, use this so that you can 297 00:19:15,440 --> 00:19:18,600 Speaker 4: make a generally overall better model. But it's full awareness, 298 00:19:18,800 --> 00:19:23,160 Speaker 4: full transparency that is important. That's a trust of who 299 00:19:23,200 --> 00:19:26,520 Speaker 4: you're doing business with. So that's how I think about trust. 300 00:19:27,160 --> 00:19:30,720 Speaker 4: How do you build systems you trust? And are you 301 00:19:31,760 --> 00:19:34,479 Speaker 4: working with people you trust? 302 00:19:35,280 --> 00:19:37,600 Speaker 2: I find Kareem's point about trust when it comes to 303 00:19:37,720 --> 00:19:41,080 Speaker 2: data to be so important because as powerful as AI 304 00:19:41,200 --> 00:19:45,240 Speaker 2: tools can be, their helpfulness is dependent on how trustworthy 305 00:19:45,280 --> 00:19:49,120 Speaker 2: the data is. Humans will have to decide if our data, 306 00:19:49,200 --> 00:19:52,600 Speaker 2: our decision making, and our AI insights live up to 307 00:19:52,640 --> 00:19:55,720 Speaker 2: the vision we hope to achieve in business. As Green 308 00:19:55,800 --> 00:19:59,359 Speaker 2: and Jacob continue the conversation, Jacob asks some more practical 309 00:19:59,440 --> 00:20:04,720 Speaker 2: questions about how businesses can adopt AI into their own processes. 310 00:20:05,440 --> 00:20:11,040 Speaker 3: Let's listen, how can businesses move toward integrating AI as 311 00:20:11,119 --> 00:20:14,479 Speaker 3: part of their core business model instead of, you know, 312 00:20:14,520 --> 00:20:16,480 Speaker 3: sort of as an add on on the periphery. 313 00:20:17,280 --> 00:20:20,119 Speaker 4: It's funny, you know. My simple answer to that is 314 00:20:20,320 --> 00:20:22,720 Speaker 4: it's actually the simplest thing in the world to do. 315 00:20:23,280 --> 00:20:30,080 Speaker 4: By thinking about your business, thinking about your elements of differentiation, 316 00:20:31,359 --> 00:20:37,640 Speaker 4: and then thinking about how AI can help you extend 317 00:20:37,800 --> 00:20:40,160 Speaker 4: expand those Right, what do you want to be known for. 318 00:20:40,480 --> 00:20:44,040 Speaker 4: I picked a very simple use case of customer service interaction. 319 00:20:44,400 --> 00:20:46,919 Speaker 4: Almost every business needs to do that and wants to 320 00:20:46,960 --> 00:20:49,560 Speaker 4: do it better, and so it becomes a way to stop. 321 00:20:49,560 --> 00:20:51,359 Speaker 4: But then as you begin to work your way through, 322 00:20:51,600 --> 00:20:55,040 Speaker 4: you think about various automation of business processes. You think 323 00:20:55,040 --> 00:20:58,080 Speaker 4: about decisions that need to be made right or how 324 00:20:58,119 --> 00:21:01,119 Speaker 4: can individuals be helped, how can they made more productive? 325 00:21:01,560 --> 00:21:04,879 Speaker 4: I think always becomes a very important one. Right, So, 326 00:21:05,000 --> 00:21:07,760 Speaker 4: and you can apply this in many context a financial 327 00:21:07,800 --> 00:21:11,520 Speaker 4: analyst looking at reams of data and trying to derive insights. 328 00:21:12,040 --> 00:21:14,720 Speaker 4: How do you leverage AI to make that financial analyst 329 00:21:14,960 --> 00:21:17,879 Speaker 4: even more powerful? And so that's how I advise you, know, 330 00:21:17,920 --> 00:21:20,440 Speaker 4: people to always look at it. Think about your task, 331 00:21:20,600 --> 00:21:24,119 Speaker 4: think about your business processes, think about where help is 332 00:21:24,160 --> 00:21:27,360 Speaker 4: needed or where new value could be unlocked, and then 333 00:21:27,400 --> 00:21:30,680 Speaker 4: you're applying AI as a tool to achieve that end. 334 00:21:31,600 --> 00:21:34,680 Speaker 3: One of the themes we return to on this show 335 00:21:34,840 --> 00:21:40,840 Speaker 3: a lot is creativity and the relationship between technology and creativity. 336 00:21:41,640 --> 00:21:46,600 Speaker 3: And I'm curious how you think that AI can help 337 00:21:46,640 --> 00:21:48,439 Speaker 3: people be more creative at work. 338 00:21:50,320 --> 00:21:53,200 Speaker 4: I think AI can help people be more creative at 339 00:21:53,240 --> 00:21:57,240 Speaker 4: work by automating the mundane to unlock your mind to 340 00:21:57,240 --> 00:22:00,359 Speaker 4: be able to focus on higher value. You know, I've 341 00:22:00,440 --> 00:22:04,000 Speaker 4: used a couple of times I've talked about deriving insights 342 00:22:04,040 --> 00:22:09,200 Speaker 4: from data right to drive informed decisions. If you can 343 00:22:09,400 --> 00:22:14,119 Speaker 4: use AI to gather a lot more insights into one place, 344 00:22:14,160 --> 00:22:17,000 Speaker 4: then you could typically do yourself or more manually, you'd 345 00:22:17,040 --> 00:22:19,600 Speaker 4: have to like write it down, look at six spreadsheets, 346 00:22:19,600 --> 00:22:22,720 Speaker 4: copy from here to there. Then you actually have more 347 00:22:22,800 --> 00:22:26,760 Speaker 4: time to look at that data, digest those insights, and 348 00:22:26,880 --> 00:22:29,280 Speaker 4: think about what do I need to do with these 349 00:22:29,320 --> 00:22:32,120 Speaker 4: as a business, which direction do I want to go? 350 00:22:32,640 --> 00:22:35,720 Speaker 4: I think of its freeing us up to do more 351 00:22:35,760 --> 00:22:39,120 Speaker 4: of what we actually as humans do extremely well, which 352 00:22:39,160 --> 00:22:43,320 Speaker 4: is actually that creative thinking exactly simple terms. Why do 353 00:22:43,400 --> 00:22:47,760 Speaker 4: we use a calculator to do arithmetic? It's not that 354 00:22:47,800 --> 00:22:51,120 Speaker 4: we cannot necessarily knock it out ourselves. But if you're 355 00:22:51,119 --> 00:22:53,960 Speaker 4: trying to balance your checkbook, to use an old phrase 356 00:22:54,320 --> 00:23:01,080 Speaker 4: or dare I say, what's a check but so modernize that. 357 00:23:02,280 --> 00:23:06,240 Speaker 4: If you're trying to check your expenses for the month 358 00:23:06,520 --> 00:23:11,200 Speaker 4: and your performance against budget, yes you could print out 359 00:23:11,240 --> 00:23:16,320 Speaker 4: all your statements, circle everything and add it all up. 360 00:23:17,240 --> 00:23:22,120 Speaker 4: Or you could begin to use technology to improve that experience, 361 00:23:22,160 --> 00:23:24,560 Speaker 4: so you can get more time to think about what 362 00:23:24,640 --> 00:23:27,280 Speaker 4: really am I learning from my spending patterns and what 363 00:23:27,320 --> 00:23:29,240 Speaker 4: do I want to do about it. It's a very 364 00:23:29,280 --> 00:23:32,920 Speaker 4: simple personal example, but I think it's fundamentally what we're 365 00:23:32,960 --> 00:23:36,359 Speaker 4: talking about here, and that's always been in my mind, 366 00:23:36,400 --> 00:23:40,919 Speaker 4: the promise of technology freeing us up to actually apply 367 00:23:41,000 --> 00:23:45,520 Speaker 4: ourselves to higher value thought and higher value problems. 368 00:23:46,080 --> 00:23:50,320 Speaker 3: So we've been talking basically about the present so far, 369 00:23:50,440 --> 00:23:53,040 Speaker 3: and I'm curious if if you think about the future 370 00:23:53,040 --> 00:23:56,560 Speaker 3: and you think, you know, medium to long term, how 371 00:23:56,560 --> 00:23:59,479 Speaker 3: do you think AI is going to transform business? And 372 00:24:00,080 --> 00:24:03,720 Speaker 3: you know, how can people now, business leaders now prepare 373 00:24:03,760 --> 00:24:04,560 Speaker 3: for what's coming. 374 00:24:05,640 --> 00:24:10,040 Speaker 4: So to an earlier comment I made, I do really 375 00:24:10,119 --> 00:24:14,800 Speaker 4: think that we are at an inflection point with the 376 00:24:14,880 --> 00:24:20,480 Speaker 4: advancement of the technologies of AI. I talked about foundation models. 377 00:24:21,359 --> 00:24:25,600 Speaker 4: We definitely at the cusp of being able to address 378 00:24:25,720 --> 00:24:30,640 Speaker 4: use cases at scale that were more challenging before, and 379 00:24:30,720 --> 00:24:35,440 Speaker 4: so I do think the future looks like a lot 380 00:24:35,560 --> 00:24:40,919 Speaker 4: more generative AI surfacing within the enterprise and within business 381 00:24:40,960 --> 00:24:47,600 Speaker 4: processes and manifesting in interesting ways. I think it's almost 382 00:24:47,640 --> 00:24:53,400 Speaker 4: a given that any piece of software right think, whether 383 00:24:53,440 --> 00:24:55,960 Speaker 4: you think of it in terms of an application or 384 00:24:56,000 --> 00:24:58,080 Speaker 4: you think about it in terms of you know, the 385 00:24:58,240 --> 00:25:04,480 Speaker 4: interact with the website will have conversational enabled interfaces from 386 00:25:04,520 --> 00:25:07,159 Speaker 4: the analyst saying give me the latest reports for the 387 00:25:07,240 --> 00:25:10,200 Speaker 4: last three months, you know, typing that or saying it 388 00:25:10,440 --> 00:25:14,040 Speaker 4: versus the right click file blah blah. I think you're 389 00:25:14,080 --> 00:25:19,119 Speaker 4: going to see that change in interaction to more conversational interaction. 390 00:25:19,720 --> 00:25:22,040 Speaker 4: I think, particularly chat based. 391 00:25:22,040 --> 00:25:25,679 Speaker 3: We forget that the graphical user interface is just a metaphor, right, 392 00:25:25,760 --> 00:25:29,119 Speaker 3: It's not like the way computers work. It's just an interface. 393 00:25:29,160 --> 00:25:32,000 Speaker 3: And if chat is a better interface, people will use chat. 394 00:25:32,680 --> 00:25:34,760 Speaker 4: And I think we're going to see that rarely explode. 395 00:25:34,800 --> 00:25:37,920 Speaker 4: And that's powered by a lot of this generative AI 396 00:25:38,000 --> 00:25:41,000 Speaker 4: work because it becomes for it to feel natural, for 397 00:25:41,080 --> 00:25:43,879 Speaker 4: it to be as informed, to readily, as I said, 398 00:25:44,119 --> 00:25:46,440 Speaker 4: link things to get and orchestrate. That's a big part. 399 00:25:46,440 --> 00:25:50,080 Speaker 4: So I think I see that happening and the appropriate 400 00:25:50,200 --> 00:25:53,639 Speaker 4: or associated productivity on locks you begin to see with 401 00:25:53,760 --> 00:25:58,240 Speaker 4: that will just change what kind of decisions, the ease 402 00:25:58,359 --> 00:26:01,480 Speaker 4: with which we can make more and more formed business decisions. 403 00:26:02,080 --> 00:26:06,600 Speaker 4: And so for me, it's that rolling out at scale, 404 00:26:07,000 --> 00:26:12,280 Speaker 4: touching everything, procurement, hr think about the advent of the 405 00:26:12,359 --> 00:26:18,520 Speaker 4: spreadsheet and how many different roles it just ended up 406 00:26:18,560 --> 00:26:23,040 Speaker 4: touching and everybody can use or does user spreadsheeting business 407 00:26:23,080 --> 00:26:25,720 Speaker 4: in some shape, size or form. So I think of 408 00:26:25,800 --> 00:26:28,720 Speaker 4: this as AI at scale. And so what it therefore 409 00:26:28,840 --> 00:26:33,320 Speaker 4: means from as you said, getting prepared, Well, it's all 410 00:26:33,359 --> 00:26:37,960 Speaker 4: about gaining first of all, the right understanding of the technologies. 411 00:26:38,000 --> 00:26:40,000 Speaker 4: And part of what a lot we'll be talking about 412 00:26:40,800 --> 00:26:44,360 Speaker 4: necessary ingredients began to be well, where do I want 413 00:26:44,359 --> 00:26:47,000 Speaker 4: to apply it first? What data do I need to 414 00:26:47,040 --> 00:26:51,840 Speaker 4: bring together to readily support that? What unlocks what new value? 415 00:26:51,920 --> 00:26:53,960 Speaker 4: And I think it's going to be like this rollout right, 416 00:26:54,000 --> 00:26:55,680 Speaker 4: you got to start with this project and then there's 417 00:26:55,680 --> 00:26:59,120 Speaker 4: another project, and very soon it will be so much 418 00:26:59,200 --> 00:27:02,679 Speaker 4: it will be ubiquitu just in the way it supports 419 00:27:02,680 --> 00:27:05,199 Speaker 4: the work we need to do. That it will just 420 00:27:05,240 --> 00:27:07,800 Speaker 4: speak to a new way of us working that is, 421 00:27:08,119 --> 00:27:10,840 Speaker 4: when you now look back, will be pretty different from 422 00:27:10,880 --> 00:27:14,800 Speaker 4: how we work today. You see the seeds today, but 423 00:27:14,880 --> 00:27:19,040 Speaker 4: I would argue, think of that now like fully bloomed. 424 00:27:19,359 --> 00:27:25,240 Speaker 4: It's a forest, not a not a flowerbed. You know, yeah, yeah, yeah, great. 425 00:27:25,720 --> 00:27:28,520 Speaker 3: One other one other sort of loose thread I want 426 00:27:28,560 --> 00:27:33,000 Speaker 3: to I want to return to UH and that's that's governance. Right, 427 00:27:33,080 --> 00:27:37,440 Speaker 3: you talked about governance and maybe just just to help 428 00:27:37,520 --> 00:27:40,119 Speaker 3: sort of set the table, like you mentioned it in 429 00:27:40,160 --> 00:27:43,720 Speaker 3: a broadway but narrowly, what does governance mean in the 430 00:27:43,720 --> 00:27:46,600 Speaker 3: context of IBM's work on enterprise AHI. 431 00:27:46,880 --> 00:27:53,479 Speaker 4: I think, as the word tries to suggest, it is 432 00:27:53,640 --> 00:28:01,399 Speaker 4: about having the way to govern one's activity in this realm, 433 00:28:01,680 --> 00:28:10,040 Speaker 4: which really speaks to policies, rules and frameworks within which 434 00:28:10,160 --> 00:28:14,600 Speaker 4: to understand all of that. Now, before we dive in 435 00:28:14,640 --> 00:28:18,640 Speaker 4: the direction of regulation, which is where people often go, 436 00:28:19,680 --> 00:28:26,480 Speaker 4: policies can be all internal. So think about it this way. 437 00:28:26,720 --> 00:28:30,359 Speaker 4: If I say to you, when I build AI, I 438 00:28:30,440 --> 00:28:34,440 Speaker 4: do not use my customer's data. Is their customer's data, 439 00:28:34,840 --> 00:28:39,560 Speaker 4: Then from a governance perspective, I need processes that ensure 440 00:28:39,720 --> 00:28:43,920 Speaker 4: I know what data I'm using and I can prove 441 00:28:44,760 --> 00:28:48,080 Speaker 4: to myself just first of all internally, forget about anybody else, 442 00:28:48,440 --> 00:28:51,840 Speaker 4: that I'm actually adhering to the policies I've laid out. 443 00:28:53,320 --> 00:28:56,040 Speaker 4: That in my mind is a lot of what governance 444 00:28:56,080 --> 00:28:59,280 Speaker 4: is about, and in the context of AI, it always 445 00:28:59,360 --> 00:29:03,120 Speaker 4: tends to I think structure around three key areas data 446 00:29:03,440 --> 00:29:05,440 Speaker 4: where did it come from? And what did I do 447 00:29:05,520 --> 00:29:07,160 Speaker 4: with it? And how did I apply it? And where 448 00:29:07,160 --> 00:29:12,360 Speaker 4: did I use it? And then usage, what do I 449 00:29:12,360 --> 00:29:16,080 Speaker 4: expect this model to do? Is this model still performing 450 00:29:16,520 --> 00:29:19,800 Speaker 4: the way I think it should be performing? What are 451 00:29:19,840 --> 00:29:23,520 Speaker 4: my processes to address whether they answered that question is 452 00:29:23,600 --> 00:29:27,800 Speaker 4: yes or no? And manage that through. And then importantly 453 00:29:28,160 --> 00:29:30,880 Speaker 4: so this is then to bridge to regulation. If you 454 00:29:30,960 --> 00:29:33,320 Speaker 4: take a look at what's going on in the world 455 00:29:33,320 --> 00:29:37,840 Speaker 4: of AI regulation and our point of view on this, 456 00:29:38,000 --> 00:29:41,480 Speaker 4: by the way, is that you actually regulate the use cases, 457 00:29:41,640 --> 00:29:46,040 Speaker 4: not the technology. Then from a governance perspective, how are 458 00:29:46,040 --> 00:29:51,080 Speaker 4: you able to clearly understand, track and account for what 459 00:29:51,280 --> 00:29:54,840 Speaker 4: use cases you are leveraging AI for? And then back 460 00:29:54,880 --> 00:29:57,360 Speaker 4: to my earlier comments how that AI. 461 00:29:57,240 --> 00:30:01,000 Speaker 3: Is performing and when you talk about how do you 462 00:30:01,000 --> 00:30:04,320 Speaker 3: make sure that you have the governance you need without 463 00:30:04,960 --> 00:30:06,080 Speaker 3: inhibiting innovation? 464 00:30:06,960 --> 00:30:11,400 Speaker 4: I think what is key and this is key A 465 00:30:11,520 --> 00:30:13,880 Speaker 4: key design point for what we're doing with What's the 466 00:30:14,000 --> 00:30:21,640 Speaker 4: next is how you make governance seamless institute versus another 467 00:30:21,720 --> 00:30:26,600 Speaker 4: activity that you do right. And so our goal is 468 00:30:26,640 --> 00:30:31,160 Speaker 4: to try and drive that kind of seamless interactions or 469 00:30:31,520 --> 00:30:35,880 Speaker 4: value add in terms of governance, so that when oh, 470 00:30:36,080 --> 00:30:40,000 Speaker 4: let's pull through the history right of everything we've done here, 471 00:30:40,080 --> 00:30:43,120 Speaker 4: or what prompts we've created, or what data we've used, 472 00:30:44,440 --> 00:30:47,560 Speaker 4: it's kind of already there, right, and so you can 473 00:30:47,600 --> 00:30:51,120 Speaker 4: feel free to be innovating and testing out your different 474 00:30:51,240 --> 00:30:54,120 Speaker 4: prompts and all that stuff, or bringing in your data 475 00:30:54,160 --> 00:30:57,000 Speaker 4: sets without saying, oh, before I do that, I need 476 00:30:57,040 --> 00:30:59,000 Speaker 4: to make sure I run this checker. And now you 477 00:30:59,040 --> 00:31:03,479 Speaker 4: can kind of bring it systems kind of automatically categorizing it, 478 00:31:03,640 --> 00:31:05,280 Speaker 4: and then you can go in a lead very five, 479 00:31:05,360 --> 00:31:08,240 Speaker 4: validate or explore, say I'm no longer going to take 480 00:31:08,280 --> 00:31:11,040 Speaker 4: this path based upon these facts. I think the more 481 00:31:11,080 --> 00:31:14,120 Speaker 4: we can make it more of a natural extension of 482 00:31:14,240 --> 00:31:17,600 Speaker 4: the activities that need to be done, the more we 483 00:31:17,640 --> 00:31:19,960 Speaker 4: can make it then just a part of what needs 484 00:31:20,000 --> 00:31:22,560 Speaker 4: to be done. And as you're to your point, gain 485 00:31:22,640 --> 00:31:26,120 Speaker 4: our governance needs or supports the governance needs of our 486 00:31:26,160 --> 00:31:30,800 Speaker 4: customers without stifling the innovation of the individuals at the 487 00:31:30,880 --> 00:31:35,440 Speaker 4: glass trying to think through I iteratively think through new 488 00:31:35,640 --> 00:31:38,680 Speaker 4: value ways to do work excellent. 489 00:31:39,480 --> 00:31:41,720 Speaker 3: Let me ask you are there things I didn't ask 490 00:31:41,760 --> 00:31:43,560 Speaker 3: you that I should? Are there things you want to 491 00:31:43,560 --> 00:31:44,920 Speaker 3: talk about that we didn't talk about. 492 00:31:45,960 --> 00:31:48,720 Speaker 4: I think we covered quite a lot true it. Oh No, 493 00:31:48,880 --> 00:31:51,760 Speaker 4: I think we we covered the bases there. 494 00:31:54,120 --> 00:31:57,320 Speaker 2: Earlier, Green mentioned that we are at an inflection point 495 00:31:57,360 --> 00:32:02,120 Speaker 2: in AI technology. Implementing a in business will get easier, 496 00:32:02,520 --> 00:32:06,240 Speaker 2: and AI platforms like Watson x can empower even the 497 00:32:06,360 --> 00:32:10,760 Speaker 2: largest enterprise businesses to reinvent the way they run. As 498 00:32:10,800 --> 00:32:13,680 Speaker 2: Greem said, in the same way the spreadsheet took over 499 00:32:13,760 --> 00:32:18,480 Speaker 2: business operations, the adoption of AI at enterprise scale could 500 00:32:18,560 --> 00:32:23,000 Speaker 2: be just as ubiquitous. It's not an overstatement to say 501 00:32:23,360 --> 00:32:26,560 Speaker 2: that a new era of work may be upon us. 502 00:32:28,640 --> 00:32:33,360 Speaker 2: I'm Malcolm Gladwell. This is a paid advertisement from IBM. 503 00:32:33,960 --> 00:32:37,160 Speaker 2: Smart Talks with IBM is produced by Matt Romano, David 504 00:32:37,240 --> 00:32:42,040 Speaker 2: jaw Nisha Venkat and Royston Deserve with Jacob Goldstein. We're 505 00:32:42,160 --> 00:32:45,760 Speaker 2: edited by Lydia gene Kott. Our engineers are Jason Gambrel, 506 00:32:46,200 --> 00:32:51,640 Speaker 2: Sarah Brugaier and Ben Tolliday. Theme song by Gramoscope. Special 507 00:32:51,640 --> 00:32:55,480 Speaker 2: thanks to Carli Migliore, Andy Kelly, Kathy Callahan and eight 508 00:32:55,560 --> 00:32:58,520 Speaker 2: Bar and the eight Bar and IBM teams, as well 509 00:32:58,520 --> 00:33:02,120 Speaker 2: as the Pushkin marketing team. 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