WEBVTT b2fc1a47-69be-4bd3-8957-4eb978d72f4f-0 00:00:00.200 --> 00:00:01.360 Bryan, Kishore, welcome. 6ef55205-e679-4cea-89f4-94b935bd6482-0 00:00:01.640 --> 00:00:01.960 Kishore, 342ccd54-2c19-406f-bbd6-2b235e1beea5-0 00:00:01.960 --> 00:00:05.920 just explain to me if you would, what is digital labor, and why it matters? ddfa4477-a292-45cf-baf1-88e285364a4d-0 00:00:06.200 --> 00:00:09.840 Well, first of all, thank you, Georgie, for having us here. At Salesforce, a6cee9fa-6b7f-417b-9bb0-47f3bd02870d-0 00:00:09.840 --> 00:00:12.907 for the last 25 years, we've been in the business of helping our a6cee9fa-6b7f-417b-9bb0-47f3bd02870d-1 00:00:12.907 --> 00:00:15.880 customers make sense of their customers data and action on it. 6fb94678-a25b-4ae4-bcf3-01d86096c190-0 00:00:16.400 --> 00:00:20.374 And I think this whole digital labor and this agentic AI is the next iteration of 6fb94678-a25b-4ae4-bcf3-01d86096c190-1 00:00:20.374 --> 00:00:20.520 it. ab1ffeaf-327f-45b4-b5e7-55184205abf5-0 00:00:20.840 --> 00:00:23.630 We're . . . think of it as AI and automation ab1ffeaf-327f-45b4-b5e7-55184205abf5-1 00:00:23.630 --> 00:00:28.405 technologies that mimic human behavior and act in conjunction with humans to ab1ffeaf-327f-45b4-b5e7-55184205abf5-2 00:00:28.405 --> 00:00:30.080 fulfill the business tasks. 84ed0c6a-20a9-49c7-9b1c-4bf0d7b58d82-0 00:00:30.800 --> 00:00:34.546 So for example, you know, on Sundays, most businesses are shut down here in 84ed0c6a-20a9-49c7-9b1c-4bf0d7b58d82-1 00:00:34.546 --> 00:00:35.040 Barcelona. 78431333-dbd1-490f-9e68-93ed06acea8a-0 00:00:35.480 --> 00:00:39.120 So imagine a digital agent acting as a human agent providing help. 90571eff-7f7d-4f3c-96ee-db6e868daaf3-0 00:00:39.480 --> 00:00:42.369 Or, my favorite example, is there's a company called The Adecco 90571eff-7f7d-4f3c-96ee-db6e868daaf3-1 00:00:42.369 --> 00:00:42.640 Group. 6e52d3a2-6ba5-400b-ac69-72a11853819f-0 00:00:42.680 --> 00:00:44.200 You may have heard of them here in Europe. ec416e2b-9965-40a5-85ba-ffc85d6b724c-0 00:00:44.520 --> 00:00:47.240 They're one of the largest talent agencies in the world. ab0799e3-66f9-409c-b6bc-f8709d9beedb-0 00:00:47.520 --> 00:00:50.080 They're in the business of putting people to work faster. f78d26e6-7972-4d32-864c-a4b077dcda67-0 00:00:50.960 --> 00:00:53.240 They get hundreds of millions of resumes a year. 5a610f50-f6d5-4401-ae0d-7eb7a5765bd8-0 00:00:53.240 --> 00:00:56.286 It takes them forever to sort through the resumes and find the right candidate for 5a610f50-f6d5-4401-ae0d-7eb7a5765bd8-1 00:00:56.286 --> 00:00:56.800 the right job. c99f72e8-0b9d-4b3f-83aa-5aab4782f4ae-0 00:00:57.160 --> 00:01:01.045 And they're using Agentforce on our agents to go through it and shorten that c99f72e8-0b9d-4b3f-83aa-5aab4782f4ae-1 00:01:01.045 --> 00:01:01.600 life cycle. 4473e6b9-737b-4463-8d5e-74a7ff62cf6f-0 00:01:01.600 --> 00:01:04.457 So that's kind of what we mean when we say digital labor, 4473e6b9-737b-4463-8d5e-74a7ff62cf6f-1 00:01:04.457 --> 00:01:06.920 working with humans to make humans better. Right. 26bafe37-f693-4dd6-8f7a-ed40e4122e7b-0 00:01:07.600 --> 00:01:08.000 Bryan, 5b524eec-929f-42b7-b2ae-cf140beb2d57-0 00:01:08.320 --> 00:01:12.555 AI is everywhere, but not every company is using it to its 5b524eec-929f-42b7-b2ae-cf140beb2d57-1 00:01:12.555 --> 00:01:13.920 greatest potential, 3478d831-ff65-42f7-9c2a-81b626f9e25d-0 00:01:13.920 --> 00:01:14.680 let's put it that way. 8da58dfa-612d-4036-ae7a-d8e02e6a64c7-0 00:01:14.800 --> 00:01:15.840 What can companies do? b0cf21ae-e5b8-4741-bfb0-06136b692914-0 00:01:15.840 --> 00:01:18.000 What where are they getting stuck, do you think, 51f8687d-da08-4235-9caa-46d692395f4b-0 00:01:18.240 --> 00:01:18.880 primarily? d5fdd0a5-9922-4dcd-82c0-f14378b969dd-0 00:01:18.880 --> 00:01:19.960 Yeah, it's a great question. 1245185f-fc27-4b81-8140-8fa4049a420f-0 00:01:19.960 --> 00:01:22.339 I mean, we know from some research that we've 1245185f-fc27-4b81-8140-8fa4049a420f-1 00:01:22.339 --> 00:01:26.011 done recently that nearly everybody believes that there's value in AI, 1245185f-fc27-4b81-8140-8fa4049a420f-2 00:01:26.011 --> 00:01:28.960 but only about 25% are actually seeing tangible results. 0e356a3d-dc0f-4c33-aa4e-eef730c57b81-0 00:01:29.600 --> 00:01:31.480 And so we see them getting stuck in a couple spots. 3cc6a944-4888-46f3-a53d-e2f42c8d1565-0 00:01:31.480 --> 00:01:33.680 One is that they're really taking on too much. 40a3adb8-8c43-4af8-af06-2fe58ef96f07-0 00:01:33.920 --> 00:01:38.254 We actually want clients to be focused on a couple of very specific use cases that 40a3adb8-8c43-4af8-af06-2fe58ef96f07-1 00:01:38.254 --> 00:01:39.560 can drive the most value. 3fc5d833-5f49-4b9b-9899-e17b76315d4d-0 00:01:39.560 --> 00:01:41.080 And that's really what the leaders are doing. 99d7a42c-879b-4301-8b9c-e307ebd81c65-0 00:01:41.320 --> 00:01:43.528 And, secondly, they're struggling on the organizational 99d7a42c-879b-4301-8b9c-e307ebd81c65-1 00:01:43.528 --> 00:01:44.120 impacts, right? 7c626152-24c9-4a1d-87b2-28b25925347d-0 00:01:44.120 --> 00:01:46.281 The technology and the data is one piece of it, 7c626152-24c9-4a1d-87b2-28b25925347d-1 00:01:46.281 --> 00:01:49.028 but there's a lot of impacts to people, process, capability, 7c626152-24c9-4a1d-87b2-28b25925347d-2 00:01:49.028 --> 00:01:52.000 organizational structures that are really holding companies back. 55ae1f7e-33e6-4925-aa2a-d2f9654806d9-0 00:01:52.520 --> 00:01:54.440 We're here at MWC in Barcelona. 00db78dc-4acc-4285-b733-4a12d94aebea-0 00:01:54.440 --> 00:01:55.800 What trends are you seeing? f567516d-dd98-45d0-96ac-08bcc51de249-0 00:01:55.800 --> 00:01:57.080 What's exciting you? 3230e57e-b29e-4224-938c-70b339238503-0 00:01:58.440 --> 00:01:59.000 I think . . . so, cb53236a-764a-4d3a-bfd0-2c4e7a96861e-0 00:01:59.000 --> 00:02:02.055 this time last year, a lot of them were still kind of dabbling cb53236a-764a-4d3a-bfd0-2c4e7a96861e-1 00:02:02.055 --> 00:02:06.031 with the idea of this whole generative AI and what does it mean to their business cb53236a-764a-4d3a-bfd0-2c4e7a96861e-2 00:02:06.031 --> 00:02:08.940 versus this year, even including all the customers we spoke cb53236a-764a-4d3a-bfd0-2c4e7a96861e-3 00:02:08.940 --> 00:02:11.317 with yesterday, they've kind of moved past that, cb53236a-764a-4d3a-bfd0-2c4e7a96861e-4 00:02:11.317 --> 00:02:14.760 and now they're trying to figure out: OK, we understand how to use it. 1122fee7-9176-4dba-8266-427c654e295b-0 00:02:15.360 --> 00:02:18.200 We understand where this technology is going. Now, 0924a5da-fe8b-4090-91d0-6527e51d9626-0 00:02:18.200 --> 00:02:22.435 how can you be make it easier so we can action on what we've learned and actually 0924a5da-fe8b-4090-91d0-6527e51d9626-1 00:02:22.435 --> 00:02:24.708 put it to work in real life, and, you know, 0924a5da-fe8b-4090-91d0-6527e51d9626-2 00:02:24.708 --> 00:02:27.240 help their employees and their customers better. a5da082f-0311-471a-8607-cd09582063a3-0 00:02:27.240 --> 00:02:29.760 So I think that to me is the most exciting thing this year. a8530f12-0631-4c66-bb5b-076271c8e4a4-0 00:02:30.640 --> 00:02:31.920 Yeah, I agree with that. 5c0a8f49-ce73-4b77-a9e3-46d21bab76c8-0 00:02:31.920 --> 00:02:35.769 But I'd also say that I'm starting to see things turn from being very cost focused, 5c0a8f49-ce73-4b77-a9e3-46d21bab76c8-1 00:02:35.769 --> 00:02:38.840 which we know continues to be on the minds of most of our clients, 275ce435-a0bc-4f3a-8c9d-e2eb10dfb27c-0 00:02:39.120 --> 00:02:42.318 but they're actually now talking about ways to grow revenue and get back to that 275ce435-a0bc-4f3a-8c9d-e2eb10dfb27c-1 00:02:42.318 --> 00:02:45.280 front office transformation that, you know, we do so well with Salesforce. fb6a90a3-4de8-4688-93d5-fdce27293c7f-0 00:02:45.760 --> 00:02:48.960 And that's enlightening and exciting for us to just see that turn a little bit. 250365af-ca2d-4853-9f83-5cb7c42dbc16-0 00:02:49.680 --> 00:02:51.840 Bryan, Kishore, thank you so much for your time.