1 00:00:04,440 --> 00:00:12,600 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 and 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 00:00:28,280 --> 00:00:32,320 Speaker 1: new season of Smart Talks with IBM. Join hosts from 7 00:00:32,320 --> 00:00:36,040 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 00:00:43,120 --> 00:00:46,800 Speaker 1: era of AI, and of course, host Malcolm Gladwell will 11 00:00:46,840 --> 00:00:49,120 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, wherever you get your podcasts 15 00:00:59,520 --> 00:01:13,959 Speaker 1: and learn at IBM dot com, slash smart Talks. 16 00:01:11,160 --> 00:01:19,160 Speaker 2: Pushkin Hello, Hello, Welcome to Smart Talks with IBM, a 17 00:01:19,200 --> 00:01:24,440 Speaker 2: podcast from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Gladwell. 18 00:01:25,000 --> 00:01:29,440 Speaker 2: This season, we're continuing our conversations with new creators visionaries 19 00:01:29,480 --> 00:01:33,280 Speaker 2: who are creatively applying technology and business to drive change, 20 00:01:33,720 --> 00:01:36,920 Speaker 2: but with a focus on the transformative power of artificial 21 00:01:36,920 --> 00:01:40,200 Speaker 2: intelligence and what it means to leverage AI as a 22 00:01:40,240 --> 00:01:45,000 Speaker 2: game changing multiplier for your business. On this special bonus 23 00:01:45,040 --> 00:01:48,280 Speaker 2: episode of Smart Talks, Tim Harford, host of the Pushkin 24 00:01:48,320 --> 00:01:52,240 Speaker 2: podcast Cautionary Tales, sat down for a conversation with two 25 00:01:52,320 --> 00:01:57,360 Speaker 2: leaders forging new ways of working together encouraging collaboration to 26 00:01:57,480 --> 00:02:03,160 Speaker 2: better serve clients. Shrineyvassan Bentickarajan is the Director of Global 27 00:02:03,160 --> 00:02:06,960 Speaker 2: Partner Business, supervising Azure Data and AI as well as 28 00:02:07,000 --> 00:02:12,520 Speaker 2: Azure OpenAI at Microsoft. He's a leading thinker behind digital transformation, 29 00:02:13,080 --> 00:02:18,120 Speaker 2: business growth, and strategic innovation. And Chris maguire is General 30 00:02:18,160 --> 00:02:22,640 Speaker 2: Manager of the Global Microsoft Partnership for IBM Consulting. He 31 00:02:22,720 --> 00:02:27,639 Speaker 2: is responsible for driving IBM Consulting strategic alignment and collaboration 32 00:02:27,800 --> 00:02:31,760 Speaker 2: with Microsoft, bringing clients to technologies they need with a 33 00:02:31,800 --> 00:02:35,480 Speaker 2: focus on hybrid cloud and AI. They talked to Tim 34 00:02:35,560 --> 00:02:39,160 Speaker 2: about the efforts of IBM and Microsoft in the generative 35 00:02:39,400 --> 00:02:43,280 Speaker 2: AI space. We're just at the beginning of understanding what 36 00:02:43,480 --> 00:02:48,200 Speaker 2: generative AI can create for customers, businesses, and the broader world. 37 00:02:48,560 --> 00:02:52,519 Speaker 2: This collaboration forward model will expand the impact of AI, 38 00:02:52,680 --> 00:02:55,959 Speaker 2: allowing innovation to thrive. Let's dive in. 39 00:02:58,960 --> 00:03:01,799 Speaker 3: Chris Schreeney, welcome both of you. Thank you so much 40 00:03:01,800 --> 00:03:05,880 Speaker 3: for joining me. Tell me a little bit about your roles. Seney, 41 00:03:05,960 --> 00:03:08,040 Speaker 3: maybe you first just tell me a little bit about 42 00:03:08,040 --> 00:03:09,040 Speaker 3: what you do at Microsoft. 43 00:03:09,320 --> 00:03:11,520 Speaker 4: So I have about twenty seven years of experience in 44 00:03:11,560 --> 00:03:16,120 Speaker 4: the tech and consulting services industry, and in these twenty 45 00:03:16,120 --> 00:03:19,000 Speaker 4: seven years, I've had the privilege of leading the charge 46 00:03:19,000 --> 00:03:24,639 Speaker 4: and driving digital transformation, business growth, and strategic innovation for clients. 47 00:03:24,960 --> 00:03:29,400 Speaker 4: And in my current role as at Microsoft, I managed 48 00:03:29,400 --> 00:03:33,440 Speaker 4: the strategic partnership with IBM. I helped craft strategic quitions 49 00:03:33,440 --> 00:03:37,119 Speaker 4: that align IBM's potential impact with Microsoft and did any 50 00:03:37,200 --> 00:03:37,960 Speaker 4: value proposition. 51 00:03:38,640 --> 00:03:43,000 Speaker 3: So, Chris, we've heard Schweney runs the Microsoft half of 52 00:03:43,040 --> 00:03:45,640 Speaker 3: the partnership with IBM. I presumably you're on the other 53 00:03:45,640 --> 00:03:47,440 Speaker 3: half of that partnership. 54 00:03:47,320 --> 00:03:51,600 Speaker 5: Correct, correct, Yeah, about three years ago we decided to 55 00:03:52,320 --> 00:03:55,920 Speaker 5: really go big with Microsoft as far as the chief partnership. 56 00:03:56,720 --> 00:04:01,640 Speaker 3: So Microsoft and IBM are these giant aims in technology, 57 00:04:02,000 --> 00:04:05,280 Speaker 3: very well known for decades. So why is it so 58 00:04:05,360 --> 00:04:09,240 Speaker 3: important to have collaboration as well as competition in the 59 00:04:09,400 --> 00:04:11,280 Speaker 3: enterprise generative AI space? 60 00:04:12,120 --> 00:04:16,200 Speaker 5: It goes back to client first. Their needs have to 61 00:04:16,240 --> 00:04:19,200 Speaker 5: be above everybody else's and if we're not meeting their needs, 62 00:04:19,279 --> 00:04:23,039 Speaker 5: then we're not responsibly doing our job. And we have 63 00:04:23,080 --> 00:04:26,800 Speaker 5: a platform. On the IBM tech side, Microsoft is a 64 00:04:26,839 --> 00:04:29,880 Speaker 5: platform and we in the middle, as IBM consulting have 65 00:04:30,000 --> 00:04:34,559 Speaker 5: the expertise to properly design and take the best interest 66 00:04:34,600 --> 00:04:37,320 Speaker 5: of the client to heart and implement and help them 67 00:04:37,360 --> 00:04:40,640 Speaker 5: get to whatever outcomes they're trying to get to, utilizing 68 00:04:40,720 --> 00:04:44,080 Speaker 5: genera of AI to make better use of data and 69 00:04:44,480 --> 00:04:47,520 Speaker 5: the investment they've made, and to properly scale. I mean, 70 00:04:48,120 --> 00:04:51,680 Speaker 5: Microsoft has a unique approach to GENDERAI. They're doing desktop 71 00:04:51,880 --> 00:04:56,200 Speaker 5: up so they have a great user base globally with 72 00:04:56,240 --> 00:04:59,400 Speaker 5: all of their office products and other solutions that most 73 00:04:59,720 --> 00:05:02,360 Speaker 5: people we use in the world today, so making general 74 00:05:02,360 --> 00:05:05,600 Speaker 5: of AI available to them is fantastic. And then us, 75 00:05:05,640 --> 00:05:08,400 Speaker 5: you know, we have a platform down approach at IBM, 76 00:05:08,839 --> 00:05:11,159 Speaker 5: and if we do things right together, we'll meet in 77 00:05:11,160 --> 00:05:14,320 Speaker 5: the middle and jointly help solve those clients problems. 78 00:05:15,240 --> 00:05:17,200 Speaker 3: So just want to understand what this looks like. We're 79 00:05:17,240 --> 00:05:21,640 Speaker 3: starting to discover that these AI systems actually it's possible 80 00:05:21,640 --> 00:05:23,520 Speaker 3: to build lots of lots of different ones. There are 81 00:05:23,640 --> 00:05:28,720 Speaker 3: different varieties that have different strengths different weaknesses. So from 82 00:05:28,720 --> 00:05:32,960 Speaker 3: the point of view of a customer when they approach you, 83 00:05:32,960 --> 00:05:34,920 Speaker 3: when you say, well, we've got this ecosystem, you've got 84 00:05:34,920 --> 00:05:36,760 Speaker 3: access to various models. How what does that look like? 85 00:05:36,880 --> 00:05:38,480 Speaker 3: Is it like an app store or is it something 86 00:05:38,480 --> 00:05:39,080 Speaker 3: a bit different. 87 00:05:40,040 --> 00:05:43,440 Speaker 4: Certain models are good for certain use cases. Now I 88 00:05:43,480 --> 00:05:46,240 Speaker 4: think you might have heard that earlier. We used to 89 00:05:46,279 --> 00:05:50,800 Speaker 4: hear about large language modess lms. Now the smaller language 90 00:05:50,800 --> 00:05:54,400 Speaker 4: model also are becoming popular because it can do certain 91 00:05:54,400 --> 00:05:58,600 Speaker 4: things very effectively. It just trained on certain domains and 92 00:05:58,680 --> 00:05:59,719 Speaker 4: also respond faster. 93 00:06:00,120 --> 00:06:02,160 Speaker 3: So a small language model is basically just we're going 94 00:06:02,200 --> 00:06:05,600 Speaker 3: to train you on I know, the manuals for all 95 00:06:05,600 --> 00:06:08,359 Speaker 3: our technical products, so you really understand if people have 96 00:06:08,360 --> 00:06:09,320 Speaker 3: a problem with our product. 97 00:06:09,800 --> 00:06:14,840 Speaker 4: Yeah, it could be for example, trained specifically for healthcare domain, right, 98 00:06:15,720 --> 00:06:19,440 Speaker 4: things like that, or it could also be trained for 99 00:06:19,680 --> 00:06:23,960 Speaker 4: certain user profiles, right for the typical work that they do, 100 00:06:24,080 --> 00:06:28,760 Speaker 4: for example in a call center or in a hospital. 101 00:06:28,880 --> 00:06:33,240 Speaker 4: How the certain things can be done faster, right. The 102 00:06:33,320 --> 00:06:36,480 Speaker 4: advantage of small language models is that it makes it 103 00:06:36,560 --> 00:06:39,200 Speaker 4: possible to run on smaller machines, so you don't need 104 00:06:39,400 --> 00:06:40,800 Speaker 4: large service and things like that. 105 00:06:41,920 --> 00:06:45,279 Speaker 3: So what I'm hearing is that people are coming to 106 00:06:45,360 --> 00:06:48,640 Speaker 3: you while they're coming to IBM and basically going, hey, 107 00:06:48,680 --> 00:06:51,560 Speaker 3: we've heard about all this cool AI stuff and what 108 00:06:51,560 --> 00:06:54,040 Speaker 3: do we do? Because of course that's that's where your 109 00:06:54,040 --> 00:06:57,520 Speaker 3: starting point is because the technology is so new the. 110 00:06:57,560 --> 00:07:00,880 Speaker 4: In fact, in fact, IBM did a Hapaton Global hackat 111 00:07:00,960 --> 00:07:04,480 Speaker 4: on and this was the first of its kind that 112 00:07:04,560 --> 00:07:07,719 Speaker 4: they did where they actually brought in the client teams 113 00:07:07,760 --> 00:07:10,960 Speaker 4: also okay, so they said that, okay, let us see 114 00:07:11,000 --> 00:07:14,280 Speaker 4: how we can actually ideate with the clients to solve 115 00:07:14,280 --> 00:07:18,000 Speaker 4: their problems, right, and they came up with quick innovative 116 00:07:18,200 --> 00:07:22,240 Speaker 4: use cases and some of these actually translated into projects 117 00:07:22,280 --> 00:07:23,080 Speaker 4: that the implemented. 118 00:07:23,720 --> 00:07:25,800 Speaker 3: Can you think of an example one of these projects 119 00:07:26,040 --> 00:07:27,000 Speaker 3: that sticks in your mind? 120 00:07:27,280 --> 00:07:32,160 Speaker 4: Yeah, So there's a client Wintershell where IBM actually co 121 00:07:32,280 --> 00:07:36,440 Speaker 4: created a knowledge extraction too to help the field engineers 122 00:07:36,520 --> 00:07:40,480 Speaker 4: to retrieve relevant insights from the vast knowledge baser. So 123 00:07:40,760 --> 00:07:45,000 Speaker 4: Wintershall is an energy company and as part of their 124 00:07:45,040 --> 00:07:50,160 Speaker 4: innovation effort Edwinterishal, they also did identified eighty new AI 125 00:07:50,280 --> 00:07:50,880 Speaker 4: use cases. 126 00:07:51,840 --> 00:07:54,720 Speaker 3: Craz if a corporate leader were to come to you 127 00:07:55,000 --> 00:07:58,680 Speaker 3: and say, look, I'm sure Jeni AI is going to 128 00:07:59,080 --> 00:08:02,840 Speaker 3: shake up my industry. It's going to be a competitive threat. 129 00:08:02,880 --> 00:08:05,760 Speaker 3: I'm sure there are loads of opportunities as well, but 130 00:08:05,840 --> 00:08:08,520 Speaker 3: I don't know how to start thinking about it. What's 131 00:08:08,560 --> 00:08:11,600 Speaker 3: the basic advice that you you'd give them to orient 132 00:08:11,680 --> 00:08:14,840 Speaker 3: themselves and the questions that they should be asking themselves. 133 00:08:15,280 --> 00:08:18,720 Speaker 5: Well, I mean, obviously it's our ability to get with 134 00:08:18,800 --> 00:08:22,360 Speaker 5: a client and bring the relevant partners to the table 135 00:08:23,040 --> 00:08:25,720 Speaker 5: to discuss the outcome that the client is trying to 136 00:08:25,800 --> 00:08:30,120 Speaker 5: achieve and then design a solution. Because I mean, obviously, 137 00:08:30,120 --> 00:08:32,080 Speaker 5: if you're you know, you want to make sure your 138 00:08:32,360 --> 00:08:34,720 Speaker 5: your your money is well spent. And given that in 139 00:08:34,720 --> 00:08:37,480 Speaker 5: the world of software everything has moved to a consumption 140 00:08:37,640 --> 00:08:40,480 Speaker 5: model and you're only paying for what you use, you 141 00:08:40,520 --> 00:08:42,559 Speaker 5: want to make sure you're getting the most efficient use 142 00:08:42,600 --> 00:08:45,120 Speaker 5: out of those platforms. And you know, we at IBM 143 00:08:45,200 --> 00:08:49,600 Speaker 5: Consulting have become extreme experts on advising clients how to 144 00:08:49,640 --> 00:08:52,360 Speaker 5: do that. And you know, it's it's a great story 145 00:08:52,440 --> 00:08:56,720 Speaker 5: now when we walk in together because over decades and decades, 146 00:08:56,840 --> 00:09:00,199 Speaker 5: IBM and Microsoft have been there at the table as 147 00:09:00,240 --> 00:09:04,080 Speaker 5: a trusted technology advisor and service provider. 148 00:09:05,800 --> 00:09:09,400 Speaker 3: The theme of this season of Smart Talks is new 149 00:09:09,480 --> 00:09:15,600 Speaker 3: Creators and that's you guys. Yeah, your new creators. So 150 00:09:15,679 --> 00:09:17,720 Speaker 3: I wanted to ask you both, maybe start with Chris, 151 00:09:18,120 --> 00:09:20,640 Speaker 3: what do you see as the most creative part of 152 00:09:22,160 --> 00:09:22,800 Speaker 3: what you do? 153 00:09:24,559 --> 00:09:24,760 Speaker 2: Well? 154 00:09:24,800 --> 00:09:26,959 Speaker 5: I think it's it goes back to the ecosystem, but 155 00:09:27,200 --> 00:09:29,920 Speaker 5: you know it's the age old saying two heads better 156 00:09:29,920 --> 00:09:31,920 Speaker 5: than one, three heads better than two, on and on, 157 00:09:32,280 --> 00:09:34,839 Speaker 5: and also that you know, what we like to say 158 00:09:35,000 --> 00:09:37,040 Speaker 5: is the way we're doing our ecosmus one plus one 159 00:09:37,040 --> 00:09:40,040 Speaker 5: equals three, especially when it comes to Microsoft. 160 00:09:40,160 --> 00:09:42,360 Speaker 3: You know, generative aies never were very good at maths, 161 00:09:42,360 --> 00:09:42,800 Speaker 3: were they so? 162 00:09:43,080 --> 00:09:44,680 Speaker 5: Okay? 163 00:09:44,679 --> 00:09:46,880 Speaker 3: Exactly, But they're creative so that's great. 164 00:09:47,080 --> 00:09:53,720 Speaker 5: So it really is about solving clients real problems and 165 00:09:53,880 --> 00:09:56,920 Speaker 5: using the very best of the technology that's available today 166 00:09:56,960 --> 00:09:59,040 Speaker 5: to do that as fast as possible, and it get 167 00:09:59,120 --> 00:10:02,680 Speaker 5: them to a where they're actually monetizing as fast as 168 00:10:02,720 --> 00:10:05,559 Speaker 5: they can. It is really important that we take our 169 00:10:05,840 --> 00:10:09,000 Speaker 5: part in this whole AI revolution very seriously and be 170 00:10:09,160 --> 00:10:11,920 Speaker 5: very very responsible, and we take that job very seriously. 171 00:10:11,960 --> 00:10:15,640 Speaker 5: And Microsoft is a very strong partner with us when 172 00:10:15,679 --> 00:10:16,880 Speaker 5: we go into clients together. 173 00:10:18,120 --> 00:10:20,080 Speaker 3: Shoney, what's creative about what you do. 174 00:10:20,920 --> 00:10:21,160 Speaker 5: Yeah. 175 00:10:21,280 --> 00:10:25,000 Speaker 4: So when I started my career, I was a software developer, 176 00:10:25,440 --> 00:10:28,719 Speaker 4: so problem solving was one of the core competency that 177 00:10:29,240 --> 00:10:32,840 Speaker 4: I had to work on. And that problem solving mindset, 178 00:10:33,360 --> 00:10:37,080 Speaker 4: along with the industry knowledge that I gained over the years, 179 00:10:37,360 --> 00:10:42,400 Speaker 4: help me identify the market trends, the consumer behavior, the 180 00:10:42,559 --> 00:10:45,840 Speaker 4: disruptive technologies helped me come up with some creative ideas 181 00:10:45,840 --> 00:10:47,240 Speaker 4: and solutions as part of my job. 182 00:10:48,400 --> 00:10:50,600 Speaker 3: Chris Sheeney, thank you both very much. 183 00:10:55,200 --> 00:10:59,000 Speaker 2: What an insightful conversation with Chris and Shreey shedding light 184 00:10:59,080 --> 00:11:03,920 Speaker 2: on the efforts of IBM and Microsoft. Technologies like AI 185 00:11:04,040 --> 00:11:08,440 Speaker 2: are complex and often difficult to scale without help. A 186 00:11:08,480 --> 00:11:12,120 Speaker 2: partner ecosystem approach is crucial in the world of AI. 187 00:11:12,840 --> 00:11:16,960 Speaker 2: By bringing together diverse expertise, collaboration can cater to a 188 00:11:17,040 --> 00:11:22,319 Speaker 2: variety of industries, providing specialized solutions for unique challenges. As 189 00:11:22,360 --> 00:11:26,720 Speaker 2: strategic partners, IBM and Microsoft aimed to guide enterprises through 190 00:11:26,760 --> 00:11:32,040 Speaker 2: these challenges responsibly looking ahead. The possibilities opened by an 191 00:11:32,080 --> 00:11:36,400 Speaker 2: ecosystem approach to AI are endless, from the integration of 192 00:11:36,440 --> 00:11:39,600 Speaker 2: the tech into everyday devices in our pockets, all the 193 00:11:39,640 --> 00:11:44,480 Speaker 2: way to its increased adoption in highly regulated intricate industries. 194 00:11:45,080 --> 00:11:47,880 Speaker 2: A huge thank you is due to Chris and Trainey 195 00:11:48,280 --> 00:11:53,680 Speaker 2: for sharing their expertise and insights. Smart Talks with IBM 196 00:11:53,800 --> 00:11:58,160 Speaker 2: is produced by Matt Romano, Joey Fishground and Jacob Goldstein 197 00:11:58,559 --> 00:12:01,760 Speaker 2: were edited by lydia Ji Being Caught. Our engineers are 198 00:12:01,800 --> 00:12:06,280 Speaker 2: Sarah Buguaer and Ben Tolladay. Theme song by Gramoscope. Special 199 00:12:06,320 --> 00:12:10,120 Speaker 2: thanks to Andy Kelly, Kathy Callahan and the eight Bar 200 00:12:10,280 --> 00:12:14,040 Speaker 2: and IBM teams, as well as the Pushkin marketing team. 201 00:12:14,320 --> 00:12:17,400 Speaker 2: Smart Talks with IBM is a production of Pushkin Industries 202 00:12:17,440 --> 00:12:21,520 Speaker 2: and Ruby Studio at iHeartMedia. To find more Pushkin podcasts, 203 00:12:21,760 --> 00:12:26,120 Speaker 2: listen on the iHeartRadio app, Apple Podcasts, or wherever you 204 00:12:26,240 --> 00:12:30,480 Speaker 2: listen to podcasts. I'm Malcolm Glabwell. This is a paid 205 00:12:30,600 --> 00:12:45,480 Speaker 2: advertisement from IBM.