WEBVTT 1 00:00:37.666 --> 00:00:38.791 - Hi, I'm Neeraj Aggarwal. 2 00:00:38.791 --> 00:00:40.708 I lead BCG's business in Asia-Pacific. 3 00:00:41.541 --> 00:00:42.458 - I'm Dylan Bolden. 4 00:00:42.458 --> 00:00:45.750 I chair BCG's Functional Practices. 5 00:00:45.750 --> 00:00:48.000 And we're here today in Davos 6 00:00:48.000 --> 00:00:50.583 where a lot of the conversation has been about AI 7 00:00:50.583 --> 00:00:53.375 and its impact on business and society. 8 00:00:53.375 --> 00:00:55.750 And we're here today to share some questions 9 00:00:55.750 --> 00:00:57.458 that we've been hearing. 10 00:00:57.458 --> 00:01:00.166 - So Dylan, you led the digital practice in North America. 11 00:01:00.166 --> 00:01:02.250 Now you lead our work globally. 12 00:01:02.250 --> 00:01:04.625 Let me ask you a hard question straight up. 13 00:01:04.625 --> 00:01:06.708 What are clients getting right about AI 14 00:01:06.708 --> 00:01:07.541 and more importantly, 15 00:01:07.541 --> 00:01:08.625 what are they not getting right about AI? 16 00:01:08.625 --> 00:01:10.666 - Yeah, it's a great question, Neeraj. 17 00:01:10.666 --> 00:01:13.500 I think what we find is that at this point most companies 18 00:01:13.500 --> 00:01:16.916 have implemented or are trying to implement AI solutions, 19 00:01:16.916 --> 00:01:20.250 but only 11% of companies have actually found the value 20 00:01:20.250 --> 00:01:21.625 that they went after. - [Neeraj] Okay. 21 00:01:21.625 --> 00:01:24.000 - What we find is companies are not focusing enough 22 00:01:24.000 --> 00:01:25.250 on the people agenda. 23 00:01:25.250 --> 00:01:26.916 They're not focusing on the learning agenda 24 00:01:26.916 --> 00:01:27.833 within corporations. 25 00:01:27.833 --> 00:01:31.375 And in fact, 10% of the value from AI comes 26 00:01:31.375 --> 00:01:36.375 from the algorithm, 20% from the tech side 27 00:01:36.416 --> 00:01:39.625 and 70% from the people side of change. 28 00:01:39.625 --> 00:01:42.541 And so focusing more on the people side 29 00:01:42.541 --> 00:01:44.125 is actually where all the value is. 30 00:01:44.125 --> 00:01:46.375 Underneath that there's three key points that we find. 31 00:01:46.375 --> 00:01:49.125 One is it's absolutely critical to make sure 32 00:01:49.125 --> 00:01:52.375 that you know where the value is going to come from. 33 00:01:52.375 --> 00:01:53.458 Two, we make sure 34 00:01:53.458 --> 00:01:56.625 that you look to digital natives around ways of working. 35 00:01:56.625 --> 00:01:57.458 And then three, 36 00:01:57.458 --> 00:02:00.166 and finally you gotta really bring the people strategy 37 00:02:00.166 --> 00:02:01.250 into this work. 38 00:02:01.250 --> 00:02:03.625 What we find is that upskilling and reskilling 39 00:02:03.625 --> 00:02:06.208 of your employees is absolutely critical. 40 00:02:06.208 --> 00:02:07.708 - And in your experience, 41 00:02:07.708 --> 00:02:08.875 clients which are doing this right 42 00:02:08.875 --> 00:02:10.458 are genuinely getting the value? 43 00:02:10.458 --> 00:02:12.666 - Clients that do this right see the value. 44 00:02:12.666 --> 00:02:13.750 But the interesting part 45 00:02:13.750 --> 00:02:16.416 is they have to do all these things to get the value, 46 00:02:16.416 --> 00:02:18.833 not just doing one little piece gets the value. 47 00:02:18.833 --> 00:02:20.750 You need to pull all those levers together 48 00:02:20.750 --> 00:02:21.666 to actually see all the upside. 49 00:02:21.666 --> 00:02:24.375 - So the more you get all together the curve 50 00:02:24.375 --> 00:02:26.375 is not linear, it's... 51 00:02:26.375 --> 00:02:27.958 - The curve is exponential. 52 00:02:28.791 --> 00:02:32.125 Neeraj, there are a lot of misconceptions around AI 53 00:02:32.125 --> 00:02:33.916 and what it does to an organization. 54 00:02:33.916 --> 00:02:37.041 What's something surprising that you've learned recently? 55 00:02:37.041 --> 00:02:39.500 - AI is a lot about efficiency and productivity, 56 00:02:39.500 --> 00:02:41.208 and that happens, right? 57 00:02:41.208 --> 00:02:43.708 But what I have learned is at its best, 58 00:02:43.708 --> 00:02:48.083 it helps improves learning, collaboration and morale. 59 00:02:48.083 --> 00:02:50.333 And look, we are living in a world where talent wants 60 00:02:50.333 --> 00:02:53.000 to work in places where they feel actualized 61 00:02:53.000 --> 00:02:55.083 and learning, collaboration and morale 62 00:02:55.083 --> 00:02:56.666 are vitally important. 63 00:02:56.666 --> 00:02:59.708 So to me that itself is the reason to embrace AI, 64 00:02:59.708 --> 00:03:01.583 leave aside the value creation 65 00:03:01.583 --> 00:03:02.541 and the efficiency gains. - Yeah. 66 00:03:02.541 --> 00:03:05.250 - This world of AI is changing rapidly. 67 00:03:05.250 --> 00:03:07.416 As you crystal ball gaze into the future. 68 00:03:07.416 --> 00:03:08.583 What excites you about it? 69 00:03:08.583 --> 00:03:10.583 - Many things I'm excited about, Neeraj. 70 00:03:10.583 --> 00:03:12.583 I mean, one I'm just in general excited 71 00:03:12.583 --> 00:03:15.541 to see business transform and to really get a front seat 72 00:03:15.541 --> 00:03:17.625 to watch our clients get there. 73 00:03:17.625 --> 00:03:20.791 Two, I'm excited to see more attention 74 00:03:20.791 --> 00:03:23.458 being paid towards responsible AI. 75 00:03:23.458 --> 00:03:26.250 What we see is that 82% of the public 76 00:03:26.250 --> 00:03:30.333 wants to see it. But a lot of the companies are just learning how to do it. 77 00:03:30.333 --> 00:03:31.416 And then I'm also excited 78 00:03:31.416 --> 00:03:34.041 to see work BCG's doing bringing the AI 79 00:03:34.041 --> 00:03:35.666 and the climate agenda together. 80 00:03:35.666 --> 00:03:36.916 - So look, climate and sustainability 81 00:03:36.916 --> 00:03:38.333 is one of the toughest problems 82 00:03:38.333 --> 00:03:40.000 and to be able to do it well, 83 00:03:40.000 --> 00:03:42.000 it needs to leverage the power of data. 84 00:03:42.000 --> 00:03:43.666 You need data in measuring 85 00:03:43.666 --> 00:03:45.083 and then in controlling beyond that. 86 00:03:45.083 --> 00:03:47.083 So let me give you a couple of facts. 87 00:03:47.083 --> 00:03:50.291 So our analysis shows right now that only 9% 88 00:03:51.208 --> 00:03:53.708 of the companies measure their emissions effectively. 89 00:03:53.708 --> 00:03:56.666 And even those 9%, get it off in a margin 90 00:03:56.666 --> 00:04:00.125 of 1/3 to 40 percentage points. - Yeah. 91 00:04:00.125 --> 00:04:03.833 - So if you can't even measure what your starting point is, 92 00:04:03.833 --> 00:04:05.250 it's very hard to decide 93 00:04:05.250 --> 00:04:06.291 where you want to get to. - Right. 94 00:04:06.291 --> 00:04:08.416 - So this is going to be a critical enabler. 95 00:04:08.416 --> 00:04:09.750 And while we talk 96 00:04:09.750 --> 00:04:11.750 about scope one and scope two, the real problem 97 00:04:11.750 --> 00:04:12.666 is scope three. - Yeah. 98 00:04:12.666 --> 00:04:14.208 - And scope three means you need to work 99 00:04:14.208 --> 00:04:16.333 with people beyond your supply chains 100 00:04:16.333 --> 00:04:19.333 on both your procurement side and your end consumers. 101 00:04:19.333 --> 00:04:21.708 And I think data is going to be integral to solving that. 102 00:04:21.708 --> 00:04:23.916 So this is going to get strongly intertwined, 103 00:04:23.916 --> 00:04:25.583 and I'm excited about that. - Yeah. 104 00:04:25.583 --> 00:04:27.291 - Because the progress you're making on AI will, 105 00:04:27.291 --> 00:04:29.166 I think, I feel good about the possibilities 106 00:04:29.166 --> 00:04:30.208 that it can be unleashed. 107 00:04:30.208 --> 00:04:31.041 - Right, it's great. 108 00:04:31.041 --> 00:04:33.583 It's really good to see those two agendas come together. 109 00:04:33.583 --> 00:04:34.416 - Absolutely.