WEBVTT 1 00:00:05.790 --> 00:00:09.690 New York Life has been around for 179 years. 2 00:00:09.690 --> 00:00:12.270 We're in the middle of a really important transformation 3 00:00:12.270 --> 00:00:15.120 around how do we operate more effectively, 4 00:00:15.120 --> 00:00:18.690 how do we deliver really outstanding experiences 5 00:00:18.690 --> 00:00:20.790 for our agents and for our clients, 6 00:00:20.790 --> 00:00:23.580 and how do we make sure that we're modernizing 7 00:00:23.580 --> 00:00:26.340 a lot of things that we do, the full tech stack? 8 00:00:26.340 --> 00:00:28.980 One of the biggest drivers for leaning into generative AI 9 00:00:28.980 --> 00:00:31.710 was a shift in our overall business strategy. 10 00:00:31.710 --> 00:00:34.950 The wave of new capabilities that came with generative AI, 11 00:00:34.950 --> 00:00:37.620 we certainly saw that as an opportunity to embrace 12 00:00:37.620 --> 00:00:39.510 a way to have a step function impact 13 00:00:39.510 --> 00:00:41.430 on our client-agent experience. 14 00:00:41.430 --> 00:00:44.190 We faced a variety of challenges during this transformation. 15 00:00:44.190 --> 00:00:45.600 We had legacy technology, 16 00:00:45.600 --> 00:00:47.610 like a lot of old insurance companies, 17 00:00:47.610 --> 00:00:49.200 that we had to navigate and traverse 18 00:00:49.200 --> 00:00:51.390 as we were trying to bring these solutions to life. 19 00:00:51.390 --> 00:00:54.090 We had new ways of working that we had to embrace, 20 00:00:54.090 --> 00:00:56.940 business, technology, data, all working together 21 00:00:56.940 --> 00:00:58.920 in iterative and agile ways. 22 00:00:58.920 --> 00:01:01.680 Another challenge we faced was change management. 23 00:01:01.680 --> 00:01:03.450 Building a tremendous AI solution 24 00:01:03.450 --> 00:01:04.620 is only part of the battle. 25 00:01:04.620 --> 00:01:06.510 To really affect business value 26 00:01:06.510 --> 00:01:07.800 and actually have real change, 27 00:01:07.800 --> 00:01:09.420 you need business process reengineering 28 00:01:09.420 --> 00:01:12.000 and change management to unlock its full potential. 29 00:01:12.000 --> 00:01:14.250 Early in the journey, we determined that, 30 00:01:14.250 --> 00:01:17.040 although we had been in the AI space for a while, 31 00:01:17.040 --> 00:01:20.040 generative AI was moving very rapidly, 32 00:01:20.040 --> 00:01:22.920 and we took some time to deliberate. 33 00:01:22.920 --> 00:01:24.900 We did a good process to select 34 00:01:24.900 --> 00:01:26.430 who we wanted in a partner. 35 00:01:26.430 --> 00:01:28.710 BCG quickly rose to the top as a company 36 00:01:28.710 --> 00:01:31.530 that was really going to help us think through 37 00:01:31.530 --> 00:01:35.580 both the strategy lens as well as how do we do this well 38 00:01:35.580 --> 00:01:37.800 from a more operational perspective, 39 00:01:37.800 --> 00:01:40.770 as well as tapping into some of their technical expertise. 40 00:01:40.770 --> 00:01:42.630 They had very good technical expertise, 41 00:01:42.630 --> 00:01:45.030 were a little bit ahead of us in the GenAI journey, 42 00:01:45.030 --> 00:01:47.370 and very importantly brought a good combination 43 00:01:47.370 --> 00:01:51.750 of a strategic thinking lens as well as some good know-how. 44 00:01:51.750 --> 00:01:54.330 When GenAI first came to life about a year, 45 00:01:54.330 --> 00:01:57.390 year and a half ago, New York Life thought about it 46 00:01:57.390 --> 00:02:00.300 as truly transformational, as a way for them 47 00:02:00.300 --> 00:02:01.830 to leapfrog competition. 48 00:02:01.830 --> 00:02:05.550 Change management is a very important topic for BCG. 49 00:02:05.550 --> 00:02:08.640 We truly believe that any technology project 50 00:02:08.640 --> 00:02:10.710 should come with change management, 51 00:02:10.710 --> 00:02:12.960 because technology alone is not enough. 52 00:02:12.960 --> 00:02:15.930 So much of it is not just an automation 53 00:02:15.930 --> 00:02:17.970 and replacement of what people do. 54 00:02:17.970 --> 00:02:21.300 For GenAI in particular, it's largely augmentation, 55 00:02:21.300 --> 00:02:24.000 and that makes it, in some ways, even harder 56 00:02:24.000 --> 00:02:26.850 because now you have to ensure that you're doing 57 00:02:26.850 --> 00:02:30.330 the right things to enable people to work really effectively 58 00:02:30.330 --> 00:02:31.680 with these new capabilities. 59 00:02:31.680 --> 00:02:35.310 We wanted to make sure we had a good, responsible AI focus, 60 00:02:35.310 --> 00:02:37.290 and BCG also helped us think through 61 00:02:37.290 --> 00:02:39.420 some of the framework there, how to do this well, 62 00:02:39.420 --> 00:02:41.880 particularly in a heavily regulated industry, 63 00:02:41.880 --> 00:02:44.730 but also just from a pure moral standpoint. 64 00:02:44.730 --> 00:02:46.290 We have similar values, 65 00:02:46.290 --> 00:02:48.600 and we're excited to see now New York Life 66 00:02:48.600 --> 00:02:52.350 truly owning and driving the risk-management aspect 67 00:02:52.350 --> 00:02:53.760 of AI implementation. 68 00:02:53.760 --> 00:02:56.040 We spent some time prioritizing which areas 69 00:02:56.040 --> 00:02:58.740 we wanted to invest in, and we decided to invest 70 00:02:58.740 --> 00:03:01.680 in four different areas: claims, servicing, 71 00:03:01.680 --> 00:03:02.907 sales, and marketing. 72 00:03:02.907 --> 00:03:05.820 And the idea is how can we take one function 73 00:03:05.820 --> 00:03:09.120 and truly transform that function end to end? 74 00:03:09.120 --> 00:03:11.700 We really feel that these transformations set us up well 75 00:03:11.700 --> 00:03:12.690 for the road ahead. 76 00:03:12.690 --> 00:03:14.160 The way forward's incredibly exciting. 77 00:03:14.160 --> 00:03:17.610 I think it's clear to all of us that AI is moving at a pace 78 00:03:17.610 --> 00:03:19.560 faster than anything we've ever seen before. 79 00:03:19.560 --> 00:03:20.460 And here at New York Life, 80 00:03:20.460 --> 00:03:23.700 we always say that we don't predict, we actually prepare. 81 00:03:23.700 --> 00:03:26.010 And the way that we feel that we can best prepare 82 00:03:26.010 --> 00:03:27.420 is to be as agile as possible 83 00:03:27.420 --> 00:03:29.020 across people, process, and tech.