WEBVTT 1 00:00:00.200 --> 00:00:01.800 One thing I thought maybe I could share 2 00:00:01.800 --> 00:00:06.433 to get us started is BCG was looking at some recent data and 3 00:00:06.433 --> 00:00:10.633 we found that the spend in AI last year 4 00:00:10.633 --> 00:00:13.266 was a 100% higher than the previous year. 5 00:00:13.266 --> 00:00:14.833 And then you say, okay, well is that a blip? 6 00:00:14.833 --> 00:00:16.200 Is that a bubble? 7 00:00:16.200 --> 00:00:20.100 And we did some research with CEOs and found no, 8 00:00:20.100 --> 00:00:23.100 that CEOs, 94% of them, are planning on spending 9 00:00:23.100 --> 00:00:25.200 as much on AI next year. 10 00:00:25.200 --> 00:00:28.200 And they say that's irrespective of the ROI. 11 00:00:28.200 --> 00:00:32.333 Okay, so they believe they must invest in AI for the future. 12 00:00:32.333 --> 00:00:35.033 And so on the one hand, companies are continuing to invest. 13 00:00:35.033 --> 00:00:37.466 And on the other hand, for those that are doing it well, 14 00:00:37.466 --> 00:00:39.566 it's really, really working. 15 00:00:39.566 --> 00:00:40.733 I would love your perspective 16 00:00:40.733 --> 00:00:42.933 from a Microsoft angle. 17 00:00:42.933 --> 00:00:44.133 What are you seeing there? 18 00:00:44.133 --> 00:00:45.533 Well, it's great to hear the data 19 00:00:45.533 --> 00:00:48.233 is actually in line with what we're seeing. 20 00:00:48.233 --> 00:00:50.900 So I think we're seeing that our companies, 21 00:00:50.900 --> 00:00:52.766 our customers, are trying to figure out 22 00:00:52.766 --> 00:00:54.266 what's the right approach for them. 23 00:00:54.266 --> 00:00:55.466 And I will tell you that the ones 24 00:00:55.466 --> 00:00:57.800 that we really believe are probably in the category 25 00:00:57.800 --> 00:00:58.966 that you're describing, 26 00:00:58.966 --> 00:01:00.833 are the ones that are thinking AI first. 27 00:01:00.833 --> 00:01:01.866 And what we mean by that are, 28 00:01:01.866 --> 00:01:03.400 we talk about a frontier firm, 29 00:01:03.400 --> 00:01:04.933 sometimes is the way we think about it, 30 00:01:04.933 --> 00:01:07.666 is that they're starting sort of with the human 31 00:01:07.666 --> 00:01:08.733 at the center 32 00:01:08.733 --> 00:01:11.466 and that they don't think necessarily right about, 33 00:01:11.466 --> 00:01:12.633 oh, what's the business process. 34 00:01:12.633 --> 00:01:16.000 But first they say, okay, how do we enable our humans, 35 00:01:16.000 --> 00:01:18.333 how do we create the ambition for that human, 36 00:01:18.333 --> 00:01:19.700 to do more and better things? 37 00:01:19.700 --> 00:01:21.766 And so with that, they kind of have that frame 38 00:01:21.766 --> 00:01:24.533 and then they say, okay, with that frame, 39 00:01:24.533 --> 00:01:26.300 how do I think about a framework 40 00:01:26.300 --> 00:01:27.600 or an approach that I want to take 41 00:01:27.600 --> 00:01:29.366 to make sure that I am executing in every way 42 00:01:29.366 --> 00:01:30.633 that I possibly can? 43 00:01:30.633 --> 00:01:32.866 And so often they build a framework 44 00:01:32.866 --> 00:01:35.700 around sort of four key pillars is how we normally see it. 45 00:01:35.700 --> 00:01:37.433 And these four pillars really help them 46 00:01:37.433 --> 00:01:39.666 think across their organization. 47 00:01:39.666 --> 00:01:42.566 The first pillar is really the employee experience. 48 00:01:42.566 --> 00:01:44.766 And back to kind of AI first starts with the employee. 49 00:01:44.766 --> 00:01:46.200 The employee is closest to the work. 50 00:01:46.200 --> 00:01:49.200 So they're going to know exactly where the opportunities are. 51 00:01:49.200 --> 00:01:50.633 But are you enabling them in a way 52 00:01:50.633 --> 00:01:52.200 that they can really be effective? 53 00:01:52.200 --> 00:01:54.833 They use AI at home, they want to use AI at work. 54 00:01:54.833 --> 00:01:57.033 They want to work on things that are important and meaningful. 55 00:01:57.033 --> 00:01:58.000 So they focus there. 56 00:01:58.000 --> 00:02:00.166 They also think about the customer experience, of course. 57 00:02:00.166 --> 00:02:01.966 How do we engage the customer better? 58 00:02:01.966 --> 00:02:05.233 How do we make sure that we are personalizing things 59 00:02:05.233 --> 00:02:06.533 to that particular customer? 60 00:02:06.533 --> 00:02:08.266 How do we make sure that we are responding to them 61 00:02:08.266 --> 00:02:09.533 in a more effective way? 62 00:02:09.533 --> 00:02:11.800 AI can enable all of those types of things. 63 00:02:11.800 --> 00:02:13.200 Then we think about business process. 64 00:02:13.200 --> 00:02:15.366 And this is one of my favorites because it's meaty. 65 00:02:15.366 --> 00:02:18.233 And this is really about looking at a process end to end. 66 00:02:18.233 --> 00:02:21.366 A lot of people think in the context of an org chart. 67 00:02:21.366 --> 00:02:24.166 Business process happens in the context of a work chart. 68 00:02:24.166 --> 00:02:26.900 And so we often say, think about the process end to end. 69 00:02:26.900 --> 00:02:29.666 That could be supply chain, that could be claims processing. 70 00:02:29.666 --> 00:02:30.866 When you look at that process, 71 00:02:30.866 --> 00:02:33.100 are there areas to 72 00:02:33.100 --> 00:02:35.533 really take that process and apply AI, 73 00:02:35.533 --> 00:02:36.900 apply agents, perhaps, 74 00:02:36.900 --> 00:02:39.366 find ways to make that process more efficient? 75 00:02:39.366 --> 00:02:41.433 Tremendous amount of opportunity for customers there, 76 00:02:41.433 --> 00:02:43.066 not only in savings, but just again, 77 00:02:43.066 --> 00:02:45.200 in a more effective way to work. 78 00:02:45.200 --> 00:02:47.566 And the final one is around bending the curve in innovation. 79 00:02:47.566 --> 00:02:48.966 And we see this in many areas, 80 00:02:48.966 --> 00:02:51.233 but this is really capabilities to leapfrog. 81 00:02:51.233 --> 00:02:53.333 We see it in health care, we see it in pharmaceuticals. 82 00:02:53.333 --> 00:02:56.600 It's just incredible the things that we can do with AI today 83 00:02:56.600 --> 00:02:58.433 around diagnosing disease, 84 00:02:58.433 --> 00:03:01.133 around finding it earlier in the cycle 85 00:03:01.133 --> 00:03:02.166 so that you have a better chance 86 00:03:02.166 --> 00:03:04.300 of an positive outcome for a patient. 87 00:03:04.300 --> 00:03:05.666 All those things are pretty incredible 88 00:03:05.666 --> 00:03:07.966 in the context of what we can do in that space. 89 00:03:07.966 --> 00:03:10.200 So it's all those things together in this framework. 90 00:03:10.200 --> 00:03:13.700 And again, really thinking about keeping companies safe. 91 00:03:13.700 --> 00:03:16.933 So we care a lot about security and you do too. 92 00:03:16.933 --> 00:03:19.933 Making sure that we are focusing on this ambition 93 00:03:19.933 --> 00:03:22.333 and this potential for all of our employees 94 00:03:22.333 --> 00:03:24.000 and the companies at heart. 95 00:03:24.000 --> 00:03:26.133 And doing it in a way that really creates outcomes 96 00:03:26.133 --> 00:03:27.300 and value for the customer. 97 00:03:27.300 --> 00:03:29.966 (lively music)