WEBVTT 6427c3c6-eafd-4cf1-ba13-4b511f497fc5-0 00:00:00.120 --> 00:00:02.668 Matt and Julie, thank you so much for joining us here at 6427c3c6-eafd-4cf1-ba13-4b511f497fc5-1 00:00:02.668 --> 00:00:03.160 Dreamforce. 7ed1b842-4e5d-470b-87bf-43ecec48cdfe-0 00:00:03.160 --> 00:00:05.160 Matt, this first question is for you. bce9d063-e3d2-4f38-af40-4b431b0c7b78-0 00:00:05.160 --> 00:00:08.034 What are business leaders overlooking when it comes to AI bce9d063-e3d2-4f38-af40-4b431b0c7b78-1 00:00:08.034 --> 00:00:08.480 adoption? 83640b25-1573-42d9-8950-732bb2856fa9-0 00:00:08.760 --> 00:00:12.196 I think the key is they're overlooking adoption, frankly. 83640b25-1573-42d9-8950-732bb2856fa9-1 00:00:12.196 --> 00:00:15.040 There's so much focus so far on the technology— 10a13d17-dd8a-4333-a5cf-dad8aa38b051-0 00:00:15.040 --> 00:00:17.865 How do I build a tool? And how do I give that tool to my 10a13d17-dd8a-4333-a5cf-dad8aa38b051-1 00:00:17.865 --> 00:00:20.988 employees?—that they're really missing the fact that if I give 10a13d17-dd8a-4333-a5cf-dad8aa38b051-2 00:00:20.988 --> 00:00:24.012 a tool to an employee and they don't use it, I don't get any 10a13d17-dd8a-4333-a5cf-dad8aa38b051-3 00:00:24.012 --> 00:00:24.360 impact. 1d689706-2cad-44b5-99e7-a849e9bc6f05-0 00:00:24.360 --> 00:00:27.583 So you really need to focus on the whole process of changing 1d689706-2cad-44b5-99e7-a849e9bc6f05-1 00:00:27.583 --> 00:00:30.754 the way that people work, so that they actually adopt, they 1d689706-2cad-44b5-99e7-a849e9bc6f05-2 00:00:30.754 --> 00:00:33.872 actually get impact, and you actually get something out of 1d689706-2cad-44b5-99e7-a849e9bc6f05-3 00:00:33.872 --> 00:00:34.560 deploying AI. d79373ba-2c40-4b1d-bb15-1f30203d0696-0 00:00:35.360 --> 00:00:38.390 And Julie, a lot of these conversations end up being about d79373ba-2c40-4b1d-bb15-1f30203d0696-1 00:00:38.390 --> 00:00:39.520 boosting productivity. ecee18a0-d389-4f76-8c65-b343319ac630-0 00:00:39.720 --> 00:00:41.920 Do you think that's more helpful or harmful? 5d1897b5-9839-4ca3-8001-545c2d8a7c3c-0 00:00:42.440 --> 00:00:43.320 It's a great question. 54955ca0-c4d8-4b36-ae18-e6411a5df2ad-0 00:00:43.320 --> 00:00:44.440 It really can be both. 3f2ca750-0b38-45de-b8e8-1676cbe2e2c3-0 00:00:44.520 --> 00:00:45.720 It can be helpful. c27109e5-ca0d-4336-b494-2d449775c01f-0 00:00:45.720 --> 00:00:48.000 We need clear goals when we're talking about AI. b05be7dc-4873-4f48-94dc-5fa73418fcba-0 00:00:48.200 --> 00:00:49.800 We need to know what we're going after. 5d046900-538c-45b1-900b-1a9e7eb401e2-0 00:00:50.080 --> 00:00:52.703 But it can also be hurtful because sometimes we focus on 5d046900-538c-45b1-900b-1a9e7eb401e2-1 00:00:52.703 --> 00:00:53.440 the wrong thing. 1793b234-0b3e-47fb-8133-1981c37cbefa-0 00:00:53.640 --> 00:00:56.640 We pick a metric that's too small, too narrow-minded. fe498c93-a640-4ece-808d-7cc2b31da647-0 00:00:56.800 --> 00:00:59.427 What we really need to do is open up the aperture and think fe498c93-a640-4ece-808d-7cc2b31da647-1 00:00:59.427 --> 00:01:00.960 bigger about how we reinvent work. 93a7f61d-7f34-434c-b546-a1dfc00bdff4-0 00:01:01.280 --> 00:01:05.120 On top of that, employees don't get excited about productivity, 93a7f61d-7f34-434c-b546-a1dfc00bdff4-1 00:01:05.120 --> 00:01:05.480 right? 17d87d7e-975f-4ee2-a470-6131ab633c1a-0 00:01:05.480 --> 00:01:08.833 So in the pilot that I did, 80% of people were excited to use, 17d87d7e-975f-4ee2-a470-6131ab633c1a-1 00:01:08.833 --> 00:01:11.920 but to Matt's point, only 20% of people actually adopted. 311c6040-cec0-43e4-81c1-125928356a3a-0 00:01:12.360 --> 00:01:14.895 Part of the driver was the message was all about 311c6040-cec0-43e4-81c1-125928356a3a-1 00:01:14.895 --> 00:01:18.000 productivity, and that wasn't really engaging people in the 311c6040-cec0-43e4-81c1-125928356a3a-2 00:01:18.000 --> 00:01:18.880 learning process. d8cfb69b-cd01-475e-9587-f3731963b7e7-0 00:01:19.040 --> 00:01:22.512 So for AI adoption to happen at scale, what steps to these d8cfb69b-cd01-475e-9587-f3731963b7e7-1 00:01:22.512 --> 00:01:24.160 companies need to be taking? c1474934-9f0d-44e3-b018-72df5b69c15d-0 00:01:24.640 --> 00:01:27.059 Well, first of all, we need to think broader and more ambitious c1474934-9f0d-44e3-b018-72df5b69c15d-1 00:01:27.059 --> 00:01:28.760 about the outcomes that we're driving after. df98fd5c-cbf9-445a-ac67-9a84a6cb2209-0 00:01:28.920 --> 00:01:32.078 We really need to be thinking about, how do we reinvent work? df98fd5c-cbf9-445a-ac67-9a84a6cb2209-1 00:01:32.078 --> 00:01:35.084 And how do we actually build a capability to reinvent work df98fd5c-cbf9-445a-ac67-9a84a6cb2209-2 00:01:35.084 --> 00:01:38.040 again and again and again and allow employees to do that? 98b9f996-3301-4aa6-901d-d77e93a67ff1-0 00:01:38.320 --> 00:01:41.520 That will actually help us get more impact at a broader scale. 2c9b39a0-b4bd-45a7-9b76-dbf40b53b665-0 00:01:41.920 --> 00:01:45.515 Maybe if I can add to that, one of the key things that we do 2c9b39a0-b4bd-45a7-9b76-dbf40b53b665-1 00:01:45.515 --> 00:01:49.110 when we're putting in AI with clients is thinking about, how 2c9b39a0-b4bd-45a7-9b76-dbf40b53b665-2 00:01:49.110 --> 00:01:52.411 do I redesign the entire process? And identifying where 2c9b39a0-b4bd-45a7-9b76-dbf40b53b665-3 00:01:52.411 --> 00:01:55.888 is there toil in the process and where is there joy in the 2c9b39a0-b4bd-45a7-9b76-dbf40b53b665-4 00:01:55.888 --> 00:01:56.360 process. de70c3e2-cdb2-430f-a408-a19b0f478a47-0 00:01:56.680 --> 00:02:00.243 Toil is work that is a lot of manual, not satisfying work that de70c3e2-cdb2-430f-a408-a19b0f478a47-1 00:02:00.243 --> 00:02:03.807 can be automated with AI, and joy is the part of the work that de70c3e2-cdb2-430f-a408-a19b0f478a47-2 00:02:03.807 --> 00:02:06.862 people actually get something out of, that they bring de70c3e2-cdb2-430f-a408-a19b0f478a47-3 00:02:06.862 --> 00:02:10.200 themselves to, that they bring being human and creativity. 88590766-f97c-46af-be71-1c666753f9f4-0 00:02:10.880 --> 00:02:14.977 Really the goal of implementing AI is to minimize toil and 88590766-f97c-46af-be71-1c666753f9f4-1 00:02:14.977 --> 00:02:15.880 maximize joy. 19e92e88-504e-46dc-8e27-1165ca1e1836-0 00:02:15.880 --> 00:02:19.041 And the benefit of that is, I get the efficiency because I've 19e92e88-504e-46dc-8e27-1165ca1e1836-1 00:02:19.041 --> 00:02:22.305 taken work out, but I leave my employees with a better job, and 19e92e88-504e-46dc-8e27-1165ca1e1836-2 00:02:22.305 --> 00:02:24.599 they enjoy more and there's more fulfilling. 113d1b74-a1b8-4cb4-a21a-0a193283061f-0 00:02:25.000 --> 00:02:26.000 Thank you so much.