WEBVTT c6c6caed-e4fb-487f-b224-3e3054ad6dfd-0 00:00:00.080 --> 00:00:02.880 We actually started to create what we call a micro agent. 15996547-2df9-4a66-9b7c-93cb95713bb5-0 00:00:02.880 --> 00:00:05.941 These are agents that start to generate maybe one, two, 15996547-2df9-4a66-9b7c-93cb95713bb5-1 00:00:05.941 --> 00:00:10.041 three simple activities and then we can string those together to create an 15996547-2df9-4a66-9b7c-93cb95713bb5-2 00:00:10.041 --> 00:00:11.080 end-to-end process. bd6826ad-9d12-4caa-a5f2-5960ea1fcbc0-0 00:00:11.400 --> 00:00:13.600 Charles, Abheek, thank you so much for joining us. 9c42266c-ab97-4331-b377-7c62d4e55d98-0 00:00:13.800 --> 00:00:15.480 Abheek, this first question is for you. c89f3de5-7176-49d9-9eb3-1d3e3ec08987-0 00:00:15.880 --> 00:00:20.074 What is key for organizations to keep in mind when looking for scalable agentic c89f3de5-7176-49d9-9eb3-1d3e3ec08987-1 00:00:20.074 --> 00:00:21.280 go-to-market solutions? 297c11fb-e7f4-4d70-8cb5-580ce2a67648-0 00:00:21.440 --> 00:00:23.120 Yeah, I would say it's probably three things. 2fb3569c-92fe-4a00-8607-0768652185fe-0 00:00:23.440 --> 00:00:27.730 One is the value case in terms of what is the outcome that you're looking to 2fb3569c-92fe-4a00-8607-0768652185fe-1 00:00:27.730 --> 00:00:29.680 achieve from your agentic solution? 1ea49731-ca64-4f21-8f22-216fa725e600-0 00:00:29.680 --> 00:00:32.960 And what is the productivity gain or efficiency gain that you're shooting for? 0d9c3da0-284b-483e-ada7-7d4b9b5367f4-0 00:00:33.280 --> 00:00:37.030 I would say #2 is probably the business process change in terms of what is the 0d9c3da0-284b-483e-ada7-7d4b9b5367f4-1 00:00:37.030 --> 00:00:40.827 future-state business process need to look like that this agentic solution will 0d9c3da0-284b-483e-ada7-7d4b9b5367f4-2 00:00:40.827 --> 00:00:41.160 enable. 034e6647-c103-4a16-9636-c828b6acdf64-0 00:00:41.440 --> 00:00:44.080 And then I would say the third thing is probably architecture. da18f8ae-f4ba-4cf7-8200-3b8b571e3747-0 00:00:44.360 --> 00:00:47.704 There's a lot of different solutions that you can approach from an agentic da18f8ae-f4ba-4cf7-8200-3b8b571e3747-1 00:00:47.704 --> 00:00:48.240 perspective. 873d1159-f8c8-422f-afba-6116c0bef3c8-0 00:00:48.240 --> 00:00:49.680 And so do you want to go out of the box? c60cc59f-fcf8-4a25-9fb6-dd0139fda2e7-0 00:00:49.680 --> 00:00:51.000 Do you want to build something custom? a4cde09b-ca6d-4fc3-892b-24a0fb5d54f2-0 00:00:51.000 --> 00:00:52.360 Do you want to marry the two together? 53209d17-c6bf-4875-84c6-a48d4f19284d-0 00:00:52.360 --> 00:00:54.225 And so, yeah, I would say it's probably those three 53209d17-c6bf-4875-84c6-a48d4f19284d-1 00:00:54.225 --> 00:00:54.800 things together. d1545cdf-7459-4b97-a0d9-bca380b461a3-0 00:00:55.200 --> 00:00:58.635 Charles, this question is for you. How does Workday approach their agentic d1545cdf-7459-4b97-a0d9-bca380b461a3-1 00:00:58.635 --> 00:01:01.200 transformation and what successes have you seen so far? 6371929c-baa3-4574-9c48-f48b3ef172f9-0 00:01:01.320 --> 00:01:05.628 Well, I think first and foremost, one of the things to understand about 6371929c-baa3-4574-9c48-f48b3ef172f9-1 00:01:05.628 --> 00:01:09.338 Workday is we truly believe that humans are still center and, 6371929c-baa3-4574-9c48-f48b3ef172f9-2 00:01:09.338 --> 00:01:14.005 and first and foremost will interact with agentic solutions and will remain a 6371929c-baa3-4574-9c48-f48b3ef172f9-3 00:01:14.005 --> 00:01:16.160 critical part of that going forward. 14128cd2-2fee-4d40-9c67-f938e71b79e7-0 00:01:16.600 --> 00:01:19.441 But for us, it's about improving experience, 14128cd2-2fee-4d40-9c67-f938e71b79e7-1 00:01:19.441 --> 00:01:23.988 so letting humans focus on more strategy, more higher-value activities, 14128cd2-2fee-4d40-9c67-f938e71b79e7-2 00:01:23.988 --> 00:01:27.840 and then augmenting them with these more agentic experiences 6aa2564c-0483-4d2b-ada7-1d506013bfa7-0 00:01:27.840 --> 00:01:31.617 as we go, as we go forward. The number one thing that we're focused 6aa2564c-0483-4d2b-ada7-1d506013bfa7-1 00:01:31.617 --> 00:01:32.840 on is experimentation. 59ca253a-9472-438e-a4ff-265e30e289b7-0 00:01:33.120 --> 00:01:35.720 And I think that's probably like most companies today. fdac61ca-869c-4869-8f58-414b5da82705-0 00:01:36.200 --> 00:01:40.375 So we have thrown everything at it in an attempt to see what's going to stick, fdac61ca-869c-4869-8f58-414b5da82705-1 00:01:40.375 --> 00:01:42.120 what's going to work really well. 4234ffd1-9bc6-4c84-8ad0-b63a5509d2e8-0 00:01:42.480 --> 00:01:44.840 And I think that's important for companies to understand today. 9e085227-0b24-48aa-9a49-bcb1bb53ae75-0 00:01:44.840 --> 00:01:50.283 It is absolutely okay to experiment in this environment and to try things that 9e085227-0b24-48aa-9a49-bcb1bb53ae75-1 00:01:50.283 --> 00:01:54.280 sometimes you may take down and adjust as you go forward. ef17a059-0349-4ac2-ad4b-ef6fdb154cc0-0 00:01:54.680 --> 00:01:59.408 And that's been a great experience for us. One hundred percent of our field-facing ef17a059-0349-4ac2-ad4b-ef6fdb154cc0-1 00:01:59.408 --> 00:02:03.509 tools today have AI capabilities turned on and we are slowly but surely ef17a059-0349-4ac2-ad4b-ef6fdb154cc0-2 00:02:03.509 --> 00:02:07.440 activating agentic experiences around those AI capabilities as well. 4798e8ba-6bdc-4174-8b3b-10c60e421991-0 00:02:07.440 --> 00:02:08.320 It's been fascinating. bba7354e-291b-413f-8529-c1a62c32e88c-0 00:02:09.040 --> 00:02:13.130 So this questions for both of you then what advice would you give to other bba7354e-291b-413f-8529-c1a62c32e88c-1 00:02:13.130 --> 00:02:15.640 organizations on embedding agentic solutions? 1cacaf65-a45f-4493-9282-584ca85d74d7-0 00:02:16.480 --> 00:02:17.160 I can go first. 15d060e2-716c-48b4-84c4-c17b6eef7d94-0 00:02:17.160 --> 00:02:17.840 Yeah, absolutely. e590fc1a-e0e1-4a4e-a3e6-33a138b5d986-0 00:02:17.840 --> 00:02:21.361 I think I'll, I'll just expand on what I mentioned, e590fc1a-e0e1-4a4e-a3e6-33a138b5d986-1 00:02:21.361 --> 00:02:24.680 which is be ready to be in experimentation mode. a3935d87-eb08-4cdd-b6f8-baaa491c54e1-0 00:02:24.800 --> 00:02:30.760 Think about the activities within your business today that are the easiest to a3935d87-eb08-4cdd-b6f8-baaa491c54e1-1 00:02:30.760 --> 00:02:36.032 start to activate with the agentic experiences that you can start to a3935d87-eb08-4cdd-b6f8-baaa491c54e1-2 00:02:36.032 --> 00:02:36.720 automate. 39956684-8c01-40a9-99d1-f6129d6444d1-0 00:02:37.360 --> 00:02:39.120 Don't be afraid to start really small. 22913337-08b2-4c3e-820b-5a11b836fedc-0 00:02:39.120 --> 00:02:41.880 We actually started to create what we call microagents. 225de63b-2f9e-4182-9ba7-8687fd2b1b89-0 00:02:41.880 --> 00:02:44.809 These are agents that start to generate maybe one, two, 225de63b-2f9e-4182-9ba7-8687fd2b1b89-1 00:02:44.809 --> 00:02:48.734 three simple activities and then we can string those together to create an 225de63b-2f9e-4182-9ba7-8687fd2b1b89-2 00:02:48.734 --> 00:02:52.134 end-to-end process and things that are truly autonomous for our, 225de63b-2f9e-4182-9ba7-8687fd2b1b89-3 00:02:52.134 --> 00:02:54.280 for our users and our, and our employees. ac4e4b39-b2a7-44ed-9269-00a4f4e7a953-0 00:02:55.000 --> 00:02:57.304 Yeah, I'd build on what Charles is saying and I ac4e4b39-b2a7-44ed-9269-00a4f4e7a953-1 00:02:57.304 --> 00:03:00.952 would say talk to your end users, regardless of whether that's a sales rep, ac4e4b39-b2a7-44ed-9269-00a4f4e7a953-2 00:03:00.952 --> 00:03:02.440 whether that's a support agent. 62989c99-6656-4d01-8dd6-ca0685cfe43a-0 00:03:02.440 --> 00:03:05.404 Talk to them about what's going to make their life the most efficient. 62989c99-6656-4d01-8dd6-ca0685cfe43a-1 00:03:05.404 --> 00:03:06.240 And then go test it, 185851cf-f34b-4876-82cd-a63c3d9be60c-0 00:03:06.240 --> 00:03:09.436 like Charles was saying. Have set capacity on your AI teams that 185851cf-f34b-4876-82cd-a63c3d9be60c-1 00:03:09.436 --> 00:03:12.829 are going to go and experiment, that are going to go and pilot these 185851cf-f34b-4876-82cd-a63c3d9be60c-2 00:03:12.829 --> 00:03:15.878 things with a small audience, see how well it works, iterate, 185851cf-f34b-4876-82cd-a63c3d9be60c-3 00:03:15.878 --> 00:03:16.960 and then scale it out. 9103c410-a3a0-43f5-ba5c-7ccfbc431b99-0 00:03:17.800 --> 00:03:18.880 Thank you both so much. 317e2782-cd5b-45ac-9444-5310e2f4a3b0-0 00:03:18.960 --> 00:03:19.360 Thank you. b6a81aae-7e93-47b8-86e7-840131ae7b5a-0 00:03:19.440 --> 00:03:19.840 Thank you. 9d0df730-cb57-46de-89ea-e2de093f6f97-0 00:03:19.840 --> 00:03:20.360 Appreciate it.