1 00:00:00,650 --> 00:00:02,477 [music] 2 00:00:02,560 --> 00:00:05,677 Bloomberg Audio Studios, podcasts, 3 00:00:05,760 --> 00:00:10,157 radio, news. >> Accenture and Anthropic inking a 4 00:00:10,240 --> 00:00:14,077 multi-year partnership deal to drive enterprise AI adoption. The two, they're 5 00:00:14,160 --> 00:00:17,997 going to form the Accenture Anthropic business group with the goal of training 6 00:00:18,080 --> 00:00:20,237 around 30,000 professionals in 7 00:00:20,320 --> 00:00:23,757 leveraging AI as a catalyst for 8 00:00:23,840 --> 00:00:25,837 reinvention. pleased to say joining us 9 00:00:25,920 --> 00:00:28,157 right now is the CEO of Accenture, Julia 10 00:00:28,240 --> 00:00:32,157 Julie, and the CEO of Anthropic, Daario 11 00:00:32,240 --> 00:00:33,917 Amade. Great to have both of you here. 12 00:00:34,000 --> 00:00:38,077 Uh, and Julie, I'll start with you. Uh, mainly because when I saw this, I 13 00:00:38,160 --> 00:00:40,957 thought, "Wow, this is neat." Then I also thought about the fact that just 14 00:00:41,040 --> 00:00:44,237 last week you announced a further expansion of your partnership with 15 00:00:44,320 --> 00:00:48,477 OpenAI. Talk a little bit about how these two coexists under the Accenture 16 00:00:48,560 --> 00:00:52,637 umbrella. >> Sure. So, um, all of our partnerships 17 00:00:52,720 --> 00:00:56,397 start with client demand. So the partnerships are responding to client 18 00:00:56,480 --> 00:01:01,277 demand and we have a long history. Our clients expect us to be the number one 19 00:01:01,360 --> 00:01:06,317 partner for every technology company that matters and uh Enthropic this 20 00:01:06,400 --> 00:01:08,157 partnership that we've announced today 21 00:01:08,240 --> 00:01:10,557 is uh very important to our clients. And 22 00:01:10,640 --> 00:01:13,357 so that's the role we play is to make 23 00:01:13,440 --> 00:01:15,357 sure that we can take the world's best 24 00:01:15,440 --> 00:01:19,117 technology and mix it up with industry 25 00:01:19,200 --> 00:01:23,837 knowledge with process and how to actually use it in the enterprise to get 26 00:01:23,920 --> 00:01:25,997 to an outcome. And as you know today 27 00:01:26,080 --> 00:01:28,717 it's all about when is AI showing up in 28 00:01:28,800 --> 00:01:31,277 the P&L. We call that the P&L pivot and 29 00:01:31,360 --> 00:01:35,757 this partnership uh will help our clients make that pivot. Well, I am 30 00:01:35,840 --> 00:01:39,037 curious then Dario if you can kind of talk about specifically the anthropic 31 00:01:39,120 --> 00:01:41,997 tools, the idea here uh of standardizing 32 00:01:42,080 --> 00:01:46,397 how some of these AI agents actually connect to uh the various uh uh 33 00:01:46,480 --> 00:01:47,837 tentacles if you will, the various 34 00:01:47,920 --> 00:01:50,637 applications out there. 35 00:01:50,720 --> 00:01:53,837 >> Um yes, so we are um uh you know I think 36 00:01:53,920 --> 00:01:56,157 we're going to start on the coding side. 37 00:01:56,240 --> 00:01:59,917 Some of the technologies that have been adopted quickly have been uh uh things 38 00:02:00,000 --> 00:02:04,477 like uh claude code the the the um tool tool we produce to help with development 39 00:02:04,560 --> 00:02:08,157 and we've seen very large productivity gains with that but we want to go beyond 40 00:02:08,240 --> 00:02:12,477 that to more general use of our tool uh claude and we want to drive usage of the 41 00:02:12,560 --> 00:02:15,117 tool across areas like healthcare um 42 00:02:15,200 --> 00:02:19,757 life sciences financial services public sector you know things that anthropic 43 00:02:19,840 --> 00:02:24,237 has worked with for a long time you know more broadly I would say that uh 44 00:02:24,320 --> 00:02:29,197 enterprises have been experimenting with AI for quite a while, but uh you know I 45 00:02:29,280 --> 00:02:32,717 think really you know as Julie said with the with the P&L the the important thing 46 00:02:32,800 --> 00:02:37,917 is really to deploy these at scale and you know to actually drive ROI for them. 47 00:02:38,000 --> 00:02:39,357 And so that's what we're trying to do. 48 00:02:39,440 --> 00:02:43,277 That's why we have this whole business center. That's why we're training 30,000 49 00:02:43,360 --> 00:02:45,837 people to use Claude. And we're going to 50 00:02:45,920 --> 00:02:48,797 work with CIOS of companies to track how 51 00:02:48,880 --> 00:02:53,277 how how they're using uh Claude through Accenture, how they're uh deploying the 52 00:02:53,360 --> 00:02:56,397 tools, what returns they're getting, what returns they're getting from the 53 00:02:56,480 --> 00:02:59,437 tools. So, we're going to work with Accenture to manage the whole life 54 00:02:59,520 --> 00:03:01,997 cycle. We think these tools are are are 55 00:03:02,080 --> 00:03:06,637 are revolutionary. We've seen that from their use inside Anthropic and many 56 00:03:06,720 --> 00:03:10,717 enterprises already. [snorts] But, uh, the thing we're most excited for is is 57 00:03:10,800 --> 00:03:14,157 to make sure that, you know, we can we can really, uh, uh, spread these across 58 00:03:14,240 --> 00:03:17,597 the enterprise world. And, you know, that's that's where Accenture comes in 59 00:03:17,680 --> 00:03:20,797 and can really drive these things at a scale that hasn't been done before. 60 00:03:20,880 --> 00:03:22,317 >> Yeah, it's interesting, Dario. I mean, 61 00:03:22,400 --> 00:03:24,797 you think about AI, some of these uh, 62 00:03:24,880 --> 00:03:27,997 LLMs, they're very popular with 63 00:03:28,080 --> 00:03:31,997 consumers, individuals. Uh, they've been well socialized. But when it comes to 64 00:03:32,080 --> 00:03:35,757 enterprises, when it comes to that business customer, you touched on maybe 65 00:03:35,840 --> 00:03:38,157 some of the skepticisms that CIOS have 66 00:03:38,240 --> 00:03:41,597 in really adopting AI as you look into 67 00:03:41,680 --> 00:03:46,237 2026, this deal with Accenture. Uh how much further does it put you when it 68 00:03:46,320 --> 00:03:49,837 comes to sort of unlocking the enterprise world and what's the goal 69 00:03:49,920 --> 00:03:52,477 when it comes to 2026? 70 00:03:52,560 --> 00:03:54,957 >> Yes. So, um you know, we we've already 71 00:03:55,040 --> 00:03:57,197 seen substantial enterprise adoption. 72 00:03:57,280 --> 00:04:02,157 This year, Anthropics revenue went from 1 billion uh annualized rate at the 73 00:04:02,240 --> 00:04:05,437 beginning of the year to 10 billion annualized rate at the end of the year. 74 00:04:05,520 --> 00:04:09,837 Um, so we've already seen a lot of adoption and you know 80 80% of that 75 00:04:09,920 --> 00:04:13,437 revenue is enterprise and within that particularly with things like coding but 76 00:04:13,520 --> 00:04:18,157 in other areas we're we're starting to see very strong ROI but the scale of 77 00:04:18,240 --> 00:04:22,317 this is so large right the scale of what needs to be done right there's hundreds 78 00:04:22,400 --> 00:04:24,957 of billions maybe trillions of of you 79 00:04:25,040 --> 00:04:29,437 know kind of enterprise enterprise productivity out there value to be 80 00:04:29,520 --> 00:04:32,957 created and so that while while it has been growing quickly and we've been 81 00:04:33,040 --> 00:04:37,117 overcoming that skepticism I think what needs to be done h has you know is 82 00:04:37,200 --> 00:04:40,717 enormous in scale compared to what to what has been done what has been done 83 00:04:40,800 --> 00:04:44,877 already and that's why partnerships with Accenture are so important that you know 84 00:04:44,960 --> 00:04:49,197 we really need to get to a scale that it's it's you know a young company needs 85 00:04:49,280 --> 00:04:52,957 to needs to partner with someone who really understands the enterprise world 86 00:04:53,040 --> 00:04:55,837 >> and Julie you made the point that you know one of the big questions here is 87 00:04:55,920 --> 00:04:59,997 when we're going to see AI show up uh in the P&L there's also the big question of 88 00:05:00,080 --> 00:05:04,237 when you see AI really show up in the job market and I'm curious you know 89 00:05:04,320 --> 00:05:08,317 whether you're seeing AI slow down hiring for entry- levelvel jobs. Over at 90 00:05:08,400 --> 00:05:11,677 Accenture, for example, [snorts] 91 00:05:11,760 --> 00:05:14,317 >> we are really focused on creating 92 00:05:14,400 --> 00:05:18,077 sustainable entry-level jobs with AI. 93 00:05:18,160 --> 00:05:21,597 And so our pivot in the talent side is 94 00:05:21,680 --> 00:05:24,717 how do we now take our entrylevel 95 00:05:24,800 --> 00:05:29,437 uh uh uh people, we call them reinventors coming in and give them the 96 00:05:29,520 --> 00:05:33,517 skills so they can operate at a higher level. So for example, if you can now do 97 00:05:33,600 --> 00:05:37,277 things that maybe would have taken you two years, uh then you're losing two 98 00:05:37,360 --> 00:05:41,997 years of experience at client. So how do we now intro we're now introducing 99 00:05:42,080 --> 00:05:44,077 communication training to help close 100 00:05:44,160 --> 00:05:47,117 that gap? And so we believe it's way too 101 00:05:47,200 --> 00:05:50,477 early to call the end of entrylevel jobs 102 00:05:50,560 --> 00:05:52,877 versus the responsibility that we have 103 00:05:52,960 --> 00:05:55,917 as a company and as companies across the 104 00:05:56,000 --> 00:05:59,197 globe to change the way that we train 105 00:05:59,280 --> 00:06:03,837 and that we think about jobs. so that we have sustainable entry level jobs. 106 00:06:03,920 --> 00:06:05,757 >> I am curious though uh Julie as you sort 107 00:06:05,840 --> 00:06:08,877 of prep your own uh folks in internally 108 00:06:08,960 --> 00:06:13,037 for uh not only using the tools yourselves but obviously the idea that 109 00:06:13,120 --> 00:06:15,437 these tools ultimately will then somehow 110 00:06:15,520 --> 00:06:18,157 be used by clients. What has been the 111 00:06:18,240 --> 00:06:21,037 uptake internally? How much retraining 112 00:06:21,120 --> 00:06:24,637 have you had to do for your own folks? 113 00:06:24,720 --> 00:06:27,277 >> Our training has been massive. Uh it 114 00:06:27,360 --> 00:06:30,237 starts with back in 2019 we introduced 115 00:06:30,320 --> 00:06:33,037 uh classical AI training. So when chatpt 116 00:06:33,120 --> 00:06:35,917 came on that we were already had 500,000 117 00:06:36,000 --> 00:06:37,997 people trained in the fundamentals of AI 118 00:06:38,080 --> 00:06:40,237 in addition to the specialists. We just 119 00:06:40,320 --> 00:06:43,277 kicked off our entire 750,000 120 00:06:43,360 --> 00:06:46,877 or so reinventors agentic AI training 121 00:06:46,960 --> 00:06:48,797 and in fact this partnership is all 122 00:06:48,880 --> 00:06:51,357 about tapping into the talent advantage 123 00:06:51,440 --> 00:06:54,157 that Accenture brings because you need 124 00:06:54,240 --> 00:06:59,117 great technology expertise you also need the industry expertise like in banking 125 00:06:59,200 --> 00:07:03,277 you need to know your be able to know your customer and so we're training all 126 00:07:03,360 --> 00:07:07,997 of our people those who understand industry and function and that's a big 127 00:07:08,080 --> 00:07:11,757 talent advantage. So Dario, but this goes back to the idea kind of of what 128 00:07:11,840 --> 00:07:15,117 you're contributing and the idea that a lot of people have been looking for more 129 00:07:15,200 --> 00:07:19,357 of a standardized model to be able to work off of and obviously anthropic at 130 00:07:19,440 --> 00:07:23,197 least the way as you guys promote yourselves. The idea of open source, the 131 00:07:23,280 --> 00:07:26,637 idea of maybe a little bit more transparency. There was a report today 132 00:07:26,720 --> 00:07:31,037 in your information that you along with a couple of others uh including uh 133 00:07:31,120 --> 00:07:35,917 Google and open AAI for that matter are going to be part of this AI foundation 134 00:07:36,000 --> 00:07:39,837 that will seek to create a more standardized system for some of this. 135 00:07:39,920 --> 00:07:42,317 Can you talk a little bit about that? 136 00:07:42,400 --> 00:07:44,477 >> Yes. So, so this is um we're we're 137 00:07:44,560 --> 00:07:47,517 donating uh MCP which is the called the 138 00:07:47,600 --> 00:07:49,837 model context protocol along with some 139 00:07:49,920 --> 00:07:52,237 other things to a to a to a foundation. 140 00:07:52,320 --> 00:07:54,957 And and what MCP is, it's it's a tool 141 00:07:55,040 --> 00:07:57,437 that uh uh uh kind of governs and 142 00:07:57,520 --> 00:08:00,797 standardizes how AI models intersect 143 00:08:00,880 --> 00:08:04,477 with data. If you think about it, you can think of an AI model as kind of as 144 00:08:04,560 --> 00:08:07,997 kind of the brain, right? You have a smart brain that knows a lot of things. 145 00:08:08,080 --> 00:08:12,077 And when I bring that brain into an enterprise context, there's all this 146 00:08:12,160 --> 00:08:15,917 kind of knowledge and and tacit knowledge that is present in the 147 00:08:16,000 --> 00:08:19,037 enterprise. And so bringing them together is always a challenge. You 148 00:08:19,120 --> 00:08:23,037 know, the way I often like to say it is, you know, suppose I took some very smart 149 00:08:23,120 --> 00:08:25,517 person off the street and, you know, plopped them into the middle of my 150 00:08:25,600 --> 00:08:29,117 enterprise, gave them an executive role and say, "Okay, congratulations. You 151 00:08:29,200 --> 00:08:32,797 know, you're in you're in charge. You get to make all the decisions." Now, as 152 00:08:32,880 --> 00:08:36,237 smart as that person is, they wouldn't at least not right away make make make 153 00:08:36,320 --> 00:08:39,917 make make immediately good immediately good decisions. So, there's this 154 00:08:40,000 --> 00:08:43,277 integration layer that's missing and and you know, I think these tools are 155 00:08:43,360 --> 00:08:46,957 designed to facilitate that integration layer and and make it easier. And so 156 00:08:47,040 --> 00:08:50,237 we're we're excited to provide these things to the world and and you know 157 00:08:50,320 --> 00:08:53,517 kind of kind of make it easier to facilitate access to the technology 158 00:08:53,600 --> 00:08:56,157 across the world. >> And Dario, before we let the two of you 159 00:08:56,240 --> 00:08:59,997 go, I have to ask while we have you, there has been reporting that you've 160 00:09:00,080 --> 00:09:02,557 been in early talks for an IPO as early 161 00:09:02,640 --> 00:09:06,397 as 2026. Can you confirm that? Uh 162 00:09:06,480 --> 00:09:11,117 currently we are we are just focused on growing our business um uh and you know 163 00:09:11,200 --> 00:09:15,197 growing growing growing enterprise uh growing enterprise revenue as as you can 164 00:09:15,280 --> 00:09:17,677 see in this partnership. >> All right. Well, obviously it's a great 165 00:09:17,760 --> 00:09:21,357 partnership. I've known Julie for some time and certainly uh you picked a good 166 00:09:21,440 --> 00:09:25,917 partner there. Dario Amade is the CEO of Anthropic. Julia, great to see you once 167 00:09:26,000 --> 00:09:30,720 again. and Julie, the CEO over at Accenture.